Some of the favoured traits may promote pest control or pollination services in adjacent fields , but other traits may not . Even if these particular bee and fly species are not contributing substantially to pollination or pest control services now, they could become important in the future if environmental conditions change – for example, as a result of changes in farm management, climate or altered biotic relationships . Further work is needed to elucidate how small-scale restoration influences pollination services via their effects on species’ response and effect traits . Meanwhile, this study shows that these habitat enhancements provide clear conservation benefits for sensitive species in flower visitor communities, even in highly intensively managed agricultural landscapes.Land use change is a main driver of biodiversity declines. Most land conversions are associated with the expansion of croplands, habitat loss and fragmentation, and biodiversity loss . Currently, agricultural land conversion is concentrated in the tropics, where most new agricultural lands – especially between 1980 and 2000 – came at the expense of undisturbed and disturbed forests raising important global concerns about biodiversity conservation . Furthermore, agricultural intensification, or changes in the actual management within farms may exacerbate the impacts of land use conversion for biodiversity . Thus, factors acting at multiple spatial scales may have strong impacts on diversity and alter processes structuring biotic communities . Yet, the effects of environmental change on community composition are not random . Hence, changes in biotic communities depend on the abundance of different taxonomic groups and on particular traits that mediate species’ responses to the magnitude, frequency and spatial patterns of disturbance . In light of environmental change,draining pots communities can undergo biological homogenization whereby sensitive species are lost from a regional pool of species or experience range contraction and tolerant species increase their ranges and abundance .

These non-random changes can cascade to affect the functional traits within a community, and thereby affect ecosystem functioning, with important implications for ecosystem services . Bees provide ecosystem services, but bee communities and populations are affected by environmental change. Most tropical crop plant species require or benefit from visits by native and non-managed bees for successful reproduction . Thus, conservation of diverse bee communities is important for both food production and tropical plant communities . Bee communities and populations are affected by land use modifications at both local and landscape scales . Bee diversity increases with flowering plant diversity and the availability of nesting sites . Agricultural practices such as tillage and sowing that reduce available resources, along with pesticide use, negatively affect bees and drive population declines . At the landscape scale, land use diversity, connectivity and proximity to undisturbed forest fragments benefits bees . In simplified landscapes, local factors are more important predictors for bee community composition, whereas these same factors are less important in highly diverse landscapes . Furthermore, local and landscape factors differentially influence bee species with specialist and low-dispersal ability species being more strongly affected by intensification and fragmentation compared with generalist, social, and high-dispersal ability species such as Apis mellifera . Most research evaluating how local and landscape factors influence patterns of bee diversity in agricultural landscapes focuses on temperate latitudes, where farms tend to be large and homogeneous but see . However, the effects of local and landscape factors on tropical mountainous bee communities is still under explored . Understanding how local and landscape disturbance affects bees in heterogeneous agricultural landscapes is important for designing conservation strategies in areas with high dependence on non-managed bees. In this study, we ask how differences in local habitat structure and landscape configuration affect bee communities across a heterogeneous, mountainous agricultural landscape in Anolaima, Colombia. We asked Which local and landscape factors influence bee abundance and diversity ?Which local and landscape factors drive changes in generic and tribe abundance and composition across farms in Anolaima?

We predicted that farms with a higher percent of undisturbed habitat, more complex vegetational structure, lower agricultural disturbance, and surrounded by a higher percent of complex habitat at the landscape scale will host higher abundance and richness of bees; local factors will have greater influence on bee community composition compared with landscape factors; and bee generic richness and abundance of specific tribes will vary depending on availability of land use types. We conducted this study in Anolaima, in the eastern slope of Andes mountains in Colombia . This municipality extends between 900 and 2800 m.a.s.l., with an average elevation of 1650 m.a.s.l.. Most lands in the municipality have steep slopes . The traditional precipitation regime is bimodal, with marked dry seasons between Dec – Mar and Jul – Sept, mean annual precipitation of 1232 mm, and average relative humidity between 70% and 80% . Life zones in the municipality transition between cloud-submontane forest and tropical dry forest, but most land cover in the area is comprised of cattle ranching and cropland . Coffee is the most extensive crop covering 10% of the total area. Small farms represent 92.6% of private landholdings in the area, and cover 53% of the total land area in the municipality . We worked in seventeen farms chosen to represent a gradient of management intensification. Farms were separated by a minimum of 2 km and represented the full range of agricultural management types present in Anolaima. Land uses included secondary forests; permanent crops arranged as agroforests ; shaded crops with simplified shade ; unshaded staple crops ; unshaded commercial short-cycle crops ; fallow lands or unmanaged areas undergoing natural regeneration; and pastures. Permanent shaded crops and traditionally managed staple crops are managed in diversified systems seldom treated with synthetic biocides. In contrast, conventional short-cycle crops are monocultures or polycultures intensively managed with synthetic biocides and with short fallow periods. Because of the average farm size , monocropping seldom extends over large areas in this region . We measured local and landscape habitat features for each study farm. To survey vegetation, we established a 1-ha plot centered on a random point within each farm and divided it into sixteen 25 m x 25 m quadrants . We classified land use types and measured canopy cover in each 25 m x 25 m quadrant. Within each quadrant, we established 4 random 2 m x 2 m sub-plots, 64 in total per farm, in which we measured ground cover and flower abundance.

In addition, we established a 200 m-radius circle around the center of the 1-ha plot and divided it into six pie pieces. In each pie piece we randomly established a 15 m x15 m plot in which we measured arboreal vegetation. We conducted landscape analyses within circles of 200 m, 500 m and 1 km radii around the 1-ha plot. We measured local vegetation features within each farm. Within each 2 m x 2 m subplot we estimated ground cover , measured height of the tallest herbaceous vegetation, and counted the number of flowers on herbs and shrubs. Within 25 m x 25 m quadrants we counted the number of flowering trees, and measured canopy cover with a concave spherical densitometer by averaging measurements at the center, and 10 m to the east, west,large plastic garden pots north and south of the quadrant center. We also observed and registered the land use of each 25 m x 25 m quadrant and then grouped them in one of seven categories: forest/agroforest; crops with simplified shade; unshaded crops with traditional management; fallowed lands; pastures; unshaded crops with conventional management; constructions ; and border of roads. We collected this data on the same days that bees were collected in each site. Within each 15 m x 15 m plot, we estimated the vertical structure of the vegetation , counted the number of trees , and registered tree morpho-species, tree height, and tree diameter at breast height . We measured tree diversity, tree size, and the vertical structure of the canopy between Jun – Aug 2015. We analyzed the configuration and composition of the landscape surrounding each farm with SPOT satellite images and digitalized aerial photographs from Instituto Geográfico Agustín Codazzi. To estimate landscape composition and determine the landscape context of each site, we classified images and created four land cover categories: complex habitat ; unshaded crops; pastures; and eroded soils. We estimated the percent area of each land cover category within 200 m, 500 m and 1000 m of the center of each farm. We also calculated the nearest distance from the center of the bee survey plot to complex habitat, unshaded crops, and to water. We conducted these analyses in ArcGis 10.3. We used aerial nets and observations to survey bees. We netted and observed bees between 0-3 m above ground in each 25 m x 25 m quadrant during 10 min. and walked all quadrants four times during the same day, for a total of 40 mins. per quadrant. Overthe four visits to each 1-ha plot, we varied the time of day each quadrant was visited to capture bees under different temperature, humidity, and sunlight conditions. We netted all bees except for Apis, Trigona , Tetragonisca and Eulaema bees that we identified and counted in the field. We killed bees with ethyl acetate, placed them in dry containers, and pinned them. We determined bees to the genus level using identification keys for bees in Colombia, Panama and Brazil at Laboratorio de Abejas in Universidad Nacional de Colombia. We sampled bees in the dry and wet seasons of 2016. We registered the type of land use in which we captured each bee.

All bee netting and observations took place between 7 AM and 2 PM on sunny days with low wind speed and with no rain. We took data on relative humidity, temperature, and wind speed at 8:00am and 12:00m as covariates. We selected five bee abundance variables, two community similarity variables, and three bee diversity variables for inclusion in model analysis. We sampled bees in the 25 m x 25 m quadrants but aggregated bee data at the farm scale for all analysis. For abundance, we used total bee abundance, partial abundance after excluding the two most common genera, and abundance of the three most common tribes. For community similarity, we used axis 1 of a non-metric multidimensional scaling analysis based on Bray-Curtis similarity for bee genera and for bee tribes. For bee diversity we used estimators of bee richness, evenness and dominance using rarefied Hill numbers. Hill numbers convert basic diversity measures to “effective number of species” numbers that obey a duplication principle. We calculated Hill numbers at three different orders of diversity. Order q=0 is equal to species richness, giving more weight to rare species; when q=1 the weight of each species is based on its relative abundance; and when q=2 abundant species have a higher weight in the community . We used 0D numbers as estimators of richness, the Hill estimator of evenness , and the Hill inequality factor as estimator of dominance across study sites . Because sample size differed across farms, we rarefied Hill numbers at q=0, q=1 and q=2 to assemblages of 72 individuals with all genera, and to 31 individuals for analysis without the two most common genera. We calculated rarefied Hill numbers with the iNEXT package and plotted diversity profiles with the Entropart package . To select explanatory variables for analyses, we grouped local and landscape features as separate groups and then ran Pearson’s correlations to identify non-correlated variables within each group. Some variables did not fit within any group and were included. Other variables had high numbers of zeros and were excluded. We used 12 explanatory variables in our models . To test whether local and landscape factors influence bee variables, we ran generalized linear models in R using the glmulti package . We tested all combinations of explanatory factors and compared Akaike Information Criterion values to select for the best models. We report conditional averages for significant model factors, AICc values, p-values and multiple linear R2 values for the best predicting models. When more models were within 2 AIC points of the next best model, we averaged models using the R MuMIn package and used conditional averages to account for significant model factors . To test whether factors influenced community similarity, we ran a permutational multivariate analysis of variance on bee genera and tribe similarity matrices using the R vegan package .

The measurement sites represented eight crop types, namely, alfalfa, almond, citrus, corn, pasture, rice, tomato, and beardless winter wheat .The eddy covariance system uses a sonic anemometer and infrared gas analyzer to measure three-dimensional wind velocities and high-frequency fluctuations of water vapor concentrations . It measures evapotranspiration by monitoring the vertical flux of water vapor. High-frequency eddy covariance measurements in two alfalfa, two corn, and one rice AmeriFlux sites were collected and preprocessed into half-hourly evapotranspiration data as outlined in Eichelmann et al. and Hemes et al. . Of the five AmeriFlux sites, net radiation for alfalfa and corn was measured with four-channel net radiometers. Most sites were located in the Sacramento-San Joaquin Delta region was also employed in the Delta Consumptive Use Comparative Study supported by the California State Water Resources Control Board Office of the Delta Watermaster and other agencies. Less expensive surface renewal systems were deployed over 14 sites for corn, alfalfa, and pasture. They use thermocouples to measure sensible heat flux, an NRLITE2 Net Radiometer for net radiation, and either measure ground heat flux with a combination of ground heat flux plates and soil thermocouples or assume it is zero for daily estimates. Evapotranspiration is then estimated as the residual of the energy balance. For each crop type, an eddy covariance tower was deployed to calibrate the sensible heat flux relationship between eddy covariance and surface renewal measurements . Evapotranspiration measurements were compiled from two specialty crop research projects in Tulare and Kern county of the southern Central Valley, including surface renewal measurements in citrus orchards from 2001 to 2004 and eddy covariance measurements in an almond orchard from 2009 to 2012 . We used only data collected after February 2003 in this study, considering the data availability of California Irrigation Management Information System Spatial product data. The most recently available eddy covariance tower measurements by NASA JPL were also added. The JPL sites were located at the Russell Ranch research field, near Davis,plant raspberry in container including one over tomato from February to October 2017, and the other over winter wheat from December 2016 to October 2017.

These towers have advanced thermal infrared radiometers to measure land surface temperature, and two sets of four channels net radiometers to reduce measurement uncertainty. High-frequency evapotranspiration data were automatically processed using Campbell Scientific Inc.’s standard Eddy-Covariance Datalogger Program software and various quality control procedures. All half-hourly measurements were preprocessed and aggregated into daily evapotranspiration if <20% of the half-hourly measurements were missing within a day.We obtained the daily gridded meteorological data, including minimum and maximum-air temperature at 1.5 m, and daily dew point, from Spatial-CIMIS at a 2-km resolution . The DWR manages a network of over 145 automated weather stations over well-maintained and well-watered grass sites across California providing reference evapotranspiration for pasture. The station data were spatially interpolated to produce the 2-km gridded data set since 2003. We also used the Spatial-CIMIS cloud cover and incoming solar radiation for both clear-sky and all-sky conditions, derived from Geostationary Operational Environmental Satellite visible channel imager data, for our radiation component calculation.All available surface reflectance and surface temperature products, and the corresponding quality assessment layers at 30 m were downloaded from USGS Landsat Analysis Ready Data set . A total of eight tiles covered the whole study area. The land surface temperature retrieval from the Landsat thermal data is based on a radiative transfer model with an improved surface emissivity estimate . Each active Landsat satellite takes snapshots between 9:53 and 10:55 a.m. Pacific Standard Time every 16 days. Invalid or high uncertainty pixel values were filtered based on the quality assessment rasters, including SLC gaps , snow, cloud/cloud shadow, for example, a high value for cloud or cirrus confidence, or with a surface temperature uncertainty greater or equal to 6 K. For model calibration and validation purposes, a single pixel near each measurement site was extracted.During cloud-free days with Landsat overpasses, Landsat-derived LAI and NDMI were fed into Equation to estimate the actual Priestley-Taylor coefficient for each pixel, which was then combined with available energy to estimate daily evapotranspiration . For days between Landsat overpasses without valid or high-quality values such as cloudy days or over scan-line corrector data gaps , a temporal interpolation approach was adopted . First, daily evapotranspiration estimates, during the adjacent clear-sky Landsat days and within ±2 months search window, were divided by the concurrent Spatial-CIMIS daily reference evapotranspiration to derive the fraction of reference evapotranspiration .

A shape-preserving piece wise cubic interpolation was applied to this discrete time series of EToF to obtain a continuous time series of daily EToF. We set a requirement of a minimum of 2 valid observations within the search window for a robust interpolation. This temporal interpolation was needed mostly during rainy season in winter and early spring in California, an off-season for the majority of the crops. Finally, daily evapotranspiration for missing days was estimated as a product of the interpolated EToF and Spatial-CIMIS reference evapotranspiration.The Priestley-Taylor method optimized here was applied over the whole California Central Valley to estimate crop evapotranspiration during the 2014 and 2016 water years. The crop-specific actual Priestley-Taylor coefficient parameterization results were used for daily averaged evapotranspiration estimation over alfalfa, almond, corn, citrus, pasture, and rice areas during Landsat over passing days. For remaining crop types, including but not limited to grapes, walnut, pistachio, tomatoes, wheat, and cotton, where no field evapotranspiration data were available for crop-specific optimization, the generalized actual Priestley-Taylor coefficient parameterizations was applied. Temporal interpolation was applied to derive a complete time series of daily evapotranspiration for each Landsat pixel. For each month, an EToF pixel is interpolated only if there are at least two estimates on clear-sky over passing days with a ±2 months moving time window; the uninterpolated pixels were gap-filled by multiplying daily reference evapotranspiration by EToF averaged by corresponding month and crop within each Landsat Analysis Ready Data tile. Daily evapotranspiration estimates were further averaged to annual time scales to analyze the regional patterns. Evapotranspiration was summarized for each crop type and compared the differences among crops by evaluating the annual evapotranspiration, reference evapotranspiration, and EToF. Specifically, the per-area water consumptive use average was computed by dividing the sum of annual evapotranspiration by crop area over nongap-filled pixels, while total consumptive use was computed over all crop area pixels. We further summarized annual evapotranspiration by GSA boundaries to provide agricultural water use information for water planning. This was achieved by quantifying annual water use and variability for each planning area and compared across areas.

We also analyzed the association of water use with corresponding land use, Rn, actual Priestley-Taylor coefficient, EToF,plastic seedling pots and reference evapotranspiration, to understand what contributed to water use differences among GSAs. While GSAs manage local groundwater resources, DWR oversees water resources regionally by water planning area. We summarized our annual crop evap-otranspiration estimates by water planning areas in the Central Valley and compared them with DWR’s estimates for the water year 2014.The seasonal dynamics of the actual Priestley-Taylor coefficient typically followed the plant growth curve, as shown by the values derived from both the field measurements and satellite observations . For example, the actual Priestley-Taylor coefficients of alfalfa frequently fluctuated from 0.5 to 1.5, likely due to the multiple cuttings throughout the growing season, as shown by the similar variations in LAI . Field measurements showed a substantial seasonal variation in the actual Priestley-Taylor coefficient for the corn and rice sites, e.g., with towering peaks in summer growing season, a relatively small peak in spring, and much lower values in between fall and winter . In general, the remote sensing-derived actual Priestley-Taylor coefficients, from the crop-specific optimization, could explain 56% of the variance observed across sites and time periods, with an RMSE and RMAD of 0.23% and 17.7%, when compared with the field-based estimates over the testing data set . For the generalized optimization, the uncertainties of actual Priestley-Taylor coefficient estimates increased slightly . Among crop types, both crop-specific and generalized actual Priestley-Taylor coefficient estimation performs best for almond . The performance of the crop-specific actual Priestley-Taylor coefficient is significantly better than the generalized actual Priestley-Taylor coefficients for corn and citrus. The actual Priestley-Taylor coefficient estimates showed significant improvement when compared to those derived from PT-0, which only captured small seasonal variation and had a higher bias of 0.24 and a larger RMAD of 34.7% over the irrigated cropland in the valley . In contrast, PT-JPL estimates showed a reasonable seasonal pattern for alfalfa and corn , although it was not calibrated for any land cover type . Across all sites, the crop-specific PT-UCD showed an overall improvement over PT-JPL, as shown by the empirical cumulative distribution function of the absolute errors when compared to both testing and independent testing data . For example, 88% of testing samples had an absolute error were below 0.30 from crop-specific PT-UCD estimates, compared to 62% and 59% from PT-JPL and PT-0 estimates, respectively. The generalized PT-UCD performed only slightly better than PT-JPL .Two types of cross-validation testing further showed the optimization of the parameters in Equation 3 for estimating the actual Priestley-Taylor coefficient was reasonably robust. The distribution of the estimated parameters showed a very small variance, for the majority of the crops and the generalized optimization . One exception was parameter D, which represented the moisture regulation over the coefficient, for citrus and pasture . The estimated actual Priestley-Taylor coefficients were shown to be stable among the repeat and leave-two-out cross-validations , with an Inter Quantile Range of RMAD of <5% .We found a good agreement between field measurements of evapotranspiration and satellite-based estimates during the clear-sky days with Landsat acquisitions. When evaluated with the testing data set, both the crop-specific and generalized evapotranspiration models captured the seasonal variability well . Across all sites, the crop-specific evapotranspiration had an R2 of 0.79, RMSE of 0.90 mm day−1, and RMAD of 20.5% . Only a small bias of 0.14 mm day−1 was found. When using the generalized actual Priestley-Taylor coefficients, slightly higher uncertainties were found, with an R2 of 0.76, RMSE of 0.98 mm day−1, and RMAD of 23.1% . The performance of evapotranspiration estimates varied by crop types. When using the crop-specific Priestley-Taylor optimization, the RMSE and RMAD ranged from 0.68 to 1.34 mm day−1 and 13.3% to 28.4%, based on the comparison with the testing data set . The best performance was found for alfalfa, citrus, and pasture sites, while the weakest performance in rice. The generalized approach also performed the best for alfalfa and citrus and performed the poorest for rice and corn . The leave-two-out cross-validation showed relatively small differences in RMSEs of daily ET estimates from site to site , e.g., 0.7 mm day−1 in alfalfa site #6 vs. 0.9 mm day−1 in site 5 based on the results from alfalfa-specific optimization, and 0.7–1.2 mm day−1 among the corn sites. Crop-specific PT-UCD showed an improvement over PT-0, PT-JPL, and generalized PT-UCD. About 80% of crop-specific evapotranspiration estimates in the testing and independent data set had an error of <1 mm day−1, as shown by the empirical cumulative distribution functions of the absolute errors between the daily crop-specific evapotranspiration estimates and field measurements . In contrast, both generalized PT-UCD and PT-JPL appeared to perform similarly, that is, about 70%–76% of samples had an evapotranspiration error <1 mm day−1, and about 85%–90% <1.5 mm day−1. However, for the PT-0 evapotranspiration estimates, only 55% and 70% of samples had an error <1 and 1.5 mm day−1, respectively.The interpolation of EToF from adjacent overpassing days introduced a small overall uncertainty in daily evapotranspiration estimates, for example, RMSE increased by 0.10–0.17 mm day−1 and decreased R2 by 0–0.08 when estimating evapotranspiration for alfalfa, citrus, corn, and pasture . When further aggregated to weekly and monthly time scales, the satellite-derived evapotranspiration estimates agreed better with those from the field measurements . For example, across all sites, R2 was increased to 0.83 and 0.88, and RMSE reduced to 0.79 and 0.65 mm day−1, respectively, for weekly and monthly evapotranspiration values based on the crop-specific Priestley-Taylor optimization.

The initial installation of the pump intake is usually above zt to minimize costs of screen installation and to maximize the capacity for useable water production . It was assumed that the cost of rehabilitating wells to alleviate well production losses caused by falling groundwater levels would be prohibitive for rural communities. Thus, the analysis assumed that pump intakes would remain at some depth above zt and wells would become inactive if the groundwater level dropped below the pump intake . Since the OSWCR contains no information on the pump intake depth, a submergence value, hs, was calibrated using the reported well failures as a validation data set. Submergence in this study is defined as the depth of the top of the well screen below the groundwater table. Depth to the groundwater table and estimated zt values were used to quantify changes in well status between Spring of 2011 and Fall of 2015 . Groundwater depths at each well location were extracted from interpolated seasonal groundwater levels spanning the entire shallow to semi-confined CV aquifer system . To calibrate the required submergence value, hs, zt values were compared to predrought and post drought groundwater levels to identify wells that became inactive as a result of groundwater level declines. Using the reported well failures in rural communities as validation data, the required pump submergence value was calibrated to be hs = 10 m for which the model estimated 923 well failures during the drought period, most of which were concentrated in the northeastern region .Many rural communities in the southern CV are not connected to municipal water supply systems and generally rely on a single water source, typically a groundwater well,planting blueberries in a pot which puts them at risk of water supply failures . Water supply connection density is a metric that describes the pressure exerted on community drinking water supply sources, given as the ratio of active public water supply sources to water supply connections in each community .

Lower values indicate a higher per capita reliance on active public water supply sources, indicating the community has lower water supply security. Communities that rely on a single public water supply source are especially vulnerable to shortages and contamination, as the failure of a single source compromises the community’s entire water supply. In the study area, 91 rural communities only have a single public water supply source of which more than 75% rely on groundwater. Communities solely reliant on unregulated domestic wells do not have any access to public water supply sources and as such, are the most vulnerable to shortages and contamination. The communities reliant on single or unregulated sources are concentrated in the northeastern and eastern regions of the southern CV.The California Department of Pesticide Regulation recognizes the following seven active ingredients contained in pesticides as a public health risk having the potential to pollute groundwater: atrazine, simazine, bromacil, diuron , prometon, bentazon, and norflurazon . Records of total annual application amounts of these active ingredients were obtained from the California Pesticide Information Portal for the year 2015. Values range between 0 and 1,024 kg within the study area , with higher loads concentrated in northeastern and eastern regions .Prolonged and unsustainable groundwater pumping causes severe settling or sinking of the land surface due to subsurface compaction of earth materials, known as land subsidence . Land subsidence rates estimated with InSAR technology between May 2015 and September 2016 was used in this analysis. The data reveal two major subsidence bowls in the northwestern and eastern regions of the southern CV and the development of a new hot spot between them . Land subsidence is of particular concern because it directly affects major surface water conveyance systems and threatens the integrity of shallow, domestic wells.Socio-economic parameters of poverty status, linguistic isolation, and educational attainment were selected as unique and complementary factors contributing to community vulnerability to change in groundwater supply . Socio-economic data were obtained by block group from the U.S. Census Bureau’s American Community Survey’s 5-year estimates for 2011–2015 and processed using the R library tidycensus . For each of the three parameters described below, demographic percentages were calculated for all block groups in the region. If multiple block groups intersected a community, an area-weighted average was calculated and the value was applied to each respective community. Poverty status is defined as the percentage of the population for whom the ratio of income to national poverty level in the previous 12 months was below one .

Poverty status is believed to contribute to community vulnerability as poorer households have less financial capacity to preemptively address or remediate water supply shortages .Households that have limited English-speaking capacity are to a lesser extent able to engage with administrative authorities to voice concerns or resolve problems, and thus have increased community vulnerability . Educational attainment is defined as the percentage of population over 25 years of age, who have completed some education above the high school level . Educational attainment can influence risk perception, skills and knowledge, and access to information and resources, hence less educated populations may be less empowered to prepare and recover from resource shortages .A GIS-based MCDA was used to combine the biophysical, hydrological, and social-ecological data listed in Table 1 to delineate and prioritize locations for multi-benefit Ag-MAR. An equal weighting scheme for thematic layers and proposed rankings of categorical features was adopted in this study following recommendations of Visser and based on the variability present in existing recharge mapping studies .Thematic layers “soil suitability for groundwater recharge,” “land use and land cover,” and “surface water conveyance infrastructure” were combined to assess the suitability of land parcels for Ag-MAR . Boolean criteria were used to restrict focus to soil types that allow percolation of surface water into groundwater aquifers, land use, and land cover types that show tolerance to prolonged flooding conditions, and land parcels that are near existing surface water conveyance infrastructure. The capture and source area of a groundwater well is dependent on the depth of the well, length of the screened section, and the groundwater flow field. A particle tracking algorithm using the Runge-Kutta-Fehlberg numerical method was implemented to identify the capture areas for all domestic wells within rural communities. The Runge-Kutta-Fehlberg uses a self-adaptive step procedure, where the step size is reduced as the curvature of the particle trajectory is increased . Using the quasi steady-state groundwater flow field extracted from CDWR’s C2VSim model, the particle tracking algorithm calculates the velocity and trajectory of a particle by interpolating the velocities between the nearest points of simulated groundwater heads in the model, then transporting the particles backward in time to determine their exit points using discrete steps of a predefined time length.

Well construction information from the OSWCR database and well status modeling , including the well location, depth of well screen, and screen length, are used for each domestic well within a rural community. More information on the parameters used in the particle tracking can be found in the Supplemental Materials.Although a wide variety of decision support tools are available for general surface and groundwater management and drinking water quality in California ; none of these tools provide information on mitigation or remediation options for chronic groundwater overdraft or contamination. This study is the first effort to systematically explore the potential for targeted Ag-MAR to directly improve the drinking water supply from groundwater in rural communities. In past decades, MAR has been used to achieve varying objectives , however, implementation of MAR is often limited by challenges of recharge water availability , locating suitable groundwater recharge zones, regulatory constraints, and funding obstacles . Ag-MAR overcomes many of these challenges due to low capital cost and permitting requirements ,raspberries in pots and with appropriate planning can be used to provide multiple benefits to a region including stabilized domestic and agricultural water supply, flood control, and climate change mitigation . However, Ag-MAR implementation in the southern CV might be constrained by the existing surface water conveyance capacity, which Hanak et al. deemed inadequate for capturing and moving high flows to suitable recharge locations. Conveyance capacity data were not available for this analysis, but according to Hanak et al. represents one of the major limitations for MAR implementation. In this study, almost 3,000 land parcels suitable for Ag-MAR ranging in size from 0.2 to 260 ha have been located within the well capture zones of rural communities. Of the 288 rural communities included in this analysis, 253 communities rely on groundwater as their main source of drinking water. However, suitable Ag-MAR parcels could only be identified within the capture zones of 149 of the 288 communities, 144 of which are reliant on groundwater for their drinking water supply. Most of the communities for which no nearby AgMAR parcels could be identified are located near large urban areas or near the CV rim, where topography and a lack of conveyance infrastructure prohibit Ag-MAR. A complex political and socio-economic environment around water governance in the region has historically prevented more inclusive water management but for these communities, other types of MAR , well head treatment, or incorporation into nearby public water supply systems might be the only options to improve the quantity and quality of drinking water supplies. For reference, 118 of the 288 communities studied have no access to public water supply sources but 56% of these communities are within the boundaries of existing public water supply systems.MAR site selection studies using GIS-based MCDA approaches have been developed in many regions across the world . The majority of these studies use slope, land use, geology and soil type as the main criteria for identifying MAR sites . Similarly, our study uses soil characteristics and land use as the main criteria to determine Ag-MAR site suitability, but differs from earlier studies in that we refine suitable sites by linking the GIS analysis with deterministic groundwater modeling and particle tracking to only select sites with potential to benefit the drinking water supply in rural communities.

The integration of groundwater modeling and particle tracking also ensured the inclusion of climate and hydrogeological data in the analysis. However, the groundwater modeling also introduced uncertainty in the estimated well capture zones, due to the spatio-temporal resolution of the model and because a quasi steady-state groundwater flow field was used for the particle tracking. The generalized groundwater flow field likely does not capture local spatio-temporal dynamics in the flow field caused by seasonal pumping, which can change or reverse some of the flow directions depicted in Figure 5. These seasonal dynamics should be considered in the final selection of Ag-MAR locations using field-level studies. In addition, in groundwater-dependent regions where an integrated surface water-groundwater model is not available, well capture zones may need to be derived from field observations. The Ag-MAR locations identified in this study relied on the integration of regionally specific data for the southern CV, but the methodology can be applied to other groundwater-dependent regions. To implement the Ag-MAR site suitability analysis, regional soil or geomorphology data can be used instead of SAGBI, and land use and surface water hydrology can be inferred from air photographs and satellite images. Similarly, data descriptive of the socio-economic status of rural communities in groundwater-dependent regions or adverse environmental effects of human activities and groundwater overdraft on rural populations can be substituted with locally available demographic data or remote sensing data , respectively. In regions where little geologic or physiographic data exists, growing availability of high-resolution remote sensing data of land surface and subsurface characteristics may be useful . Many previous MAR site suitability studies were conducted to inform sustainable groundwater management , to serve as guidelines and decision support for farmers and policy makers , or to raise general interest for MAR development . However, as showcased in this study, GIS-based MCDA can also be used to identify priority areas for intervention or disaster management if site suitability analysis is combined with vulnerability analysis . This combination can be particularly useful in water resources management because the outputs can provide easily interpretable visual information, help refine the spatial focus of the problem, support priority development, and allow for assessment of different management scenarios before field-level investigations begin.

It is not yet clear how this legacy nitrogen may respond to changing hydrologic regimes and variations in AgMAR practices, and more importantly, if flooding agricultural sites is enhancing nitrate transport to the groundwater or attenuating it by supporting in situ denitrification. Denitrification rates in the subsurface have been reported to vary as a function of carbon and oxygen concentrations, as well as other environmental factors . While total soil organic carbon typically declines with depth , dissolved organic carbon can be readily transported by water lost from the root zone to deeper layers and can therefore be available to act as an electron donor for denitrification . Oxygen concentration in the vadose zone is maintained by advective and diffusive transport, while oxygen consumption occurs via microbial metabolic demand and/or abiotic chemical reactions . The effects of drying and wetting cycles on oxygen concentrations in the deep subsurface are not well documented. However, in 1 meter column experiments, there is some evidence that O2 consumption proceeds rapidly as saturation increases and rebounds quickly during dry periods . These variations in oxygen concentration can influence N cycling and thus, transport to groundwater. Variability in nitrate concentration has also been linked to heterogeneous subsurface properties, rainfall events, seasonality of flow and other local geochemical conditions across a diversity of settings However, a gap currently exists in quantifying N attenuation and transport from agriculturally intensive regions with a “deep” vadose zone while accounting for the many competing N cycle reactions and transformations, as impacted by different hydrological regimes imposed under AgMAR. The application of AgMAR itself can vary in terms of the hydraulic loading and rates used, as well as the duration between flood water applications. These can in turn affect water retention times, O2 availability, consumption of electron donors and consequently, denitrification rates . For example, denitrification rates were found to increase with increased hydraulic loading and with shorter times between flood applications within the vadose zone of a rapid infiltration basin system used for disposing of treated wastewater . In shallow, sandy soils, high flow rates – above an infiltration threshold – were negatively correlated with denitrification rates,plant pots with drainage suggesting that an optimum infiltration rate exists for a given sediment stratigraphy to maximize NO3 – reduction .

Given the immense stratigraphic heterogeneity in alluvial basins, such as in California’s Central Valley, a range of optimum infiltration rates may exist with implications for managing AgMAR differently based on the geologic setting of the intended site. Therefore, the objectives of this study are to: a) understand the effects of varying stratigraphy and hydrologic regimes on denitrification rates, and b) identify AgMAR management scenarios that increase denitrification rates, such that the potential for N leaching to groundwater is decreased. Herein, we focus on an agricultural field site in Modesto, California located within the Central Valley of California, which is responsible for California’s $46 billion-dollar agricultural economy . The field site typifies the deep vadose zones prevalent in this region, which are characterized by heterogenous layered alluvial sediments intercalated with discontinuous buried clay and carbon rich paleosols . These discontinuous, layered features, especially the paleosols and areas of preferential flow, are typically associated with enhanced biogeochemical activity, higher carbon content and availability of metabolic substrates such as nitrogen . These regions respond to and change depending on environmental conditions such as water content and oxygen concentration in situ that are influenced by the hydrologic regime at the surface and may be important for NO3 – attenuation and reduction prior to reaching the water table. Therefore, this study considers varying hydrologic regimes and stratigraphic variations that are prevalent in the region. More specifically, at the Modesto field site , large amounts of legacy N already reside in the vadose zone, while N fertilizer application and irrigation occurs throughout the spring and summer months. AgMAR, if implemented, occurs during the winter months as water becomes available. Therefore, we focus here on quantifying the effects of AgMAR on N cycling in the deep vadose zone and implications for NO3 – contamination of groundwater at this characteristic agricultural field site. We also investigate the specific AgMAR application rates that would increase the effectiveness of in situ denitrification under different stratigraphic configurations through the development and testing of a reactive transport model. We believe such an analysis provides important insights for the successful application of AgMAR strategies aimed at improving groundwater storage in a changing climate.

Reactive transport models can be beneficial tools to elucidating N fate and transport in deep vadose zone environments. Herein, we develop a comprehensive reaction network incorporating the major processes impacting N transport and attenuation, such as aqueous complexation, mineral precipitation and dissolution, and microbially mediated redox reactions. While using the same reaction network, we implement several numerical scenarios to quantify the range of denitrification rates possible under different AgMAR implementation strategies and stratigraphic configurations . For the latter, we used four different stratigraphic configurations with a low permeability layer on top including i) two homogeneous textural profiles, ii) a sand stratigraphy with a discontinuous silt band, iii) a silt stratigraphy with a discontinuous sand band, and iv) a complex stratigraphy more representative of the field conditions investigated by electrical resistance tomography . The top layer served two purposes, one, it allowed the net infiltration rate to be calibrated to match measured average field infiltration rates of 0.17 cm/hr and two, it represented the expected increase in sediment uniformity expected in ploughed or tilled layers in agricultural settings. While, the impact of the top layer resulted in water being delivered more slowly to the heterogenous sediments below, varying rates of percolation occurred after reaching below the more homogenous layer allowing us to examine the effects of heterogeneity on nitrate transport and fate in the vadose zone. For each stratigraphy, we further varied the frequency and duration of water per application to investigate the impact of different AgMAR implementations that are similar to recent field trials conducted throughout the state . In addition, we tested the effect of antecedent moisture conditions on N biogeochemistry within the more complex stratigraphy by setting the model with a wetter initial moisture profile. Overall, a set of 18 simulation experiments were used to isolate and understand the contribution of different AgMAR strategies to enhance or decrease denitrification rates in deep vadose zone environments with homogeneous and banded configurations. A detailed model setup and numerical implementation is provided in Section 2.3. Although our reactive transport analysis was guided by a particular field site that is classified as a “Medium to Good” site for MAR , our aim was not to replicate site conditions in its entirety, but rather to enhance our understanding of how hereogeneity might impact nitrogen transport and fate under MAR.

The study site is an almond orchard located in California’s Central Valley, southwest of Modesto, and north of the Tuolumne River . The surface soil is classified as a Dinuba fine sandy loam . The site is characterized by a Mediterranean climate, with wet winters and hot, dry summers. Average annual temperature and total annual precipitation are 17.5° C and 335 mm, respectively. As suggested above, the vadose zone typifies the valley with contrasting layered sequences of granitic alluvial sedimentary deposits consisting of predominantly silt loams and sandy loams. We therefore use these textures to design our modeled stratigraphic configurations with and without banded layers. The groundwater table in the study area typically occurs around 15 m below ground surface. Soil properties including percent sand, silt, clay, total N, total C, and pH are shown in Table 1.To specifically characterize the textural layers and subsurface heterogeneity at our site, we used electrical resistivity tomography . ERT profiles were generated along a 150 m transect to 20 m depth prior to flooding to quantify subsurface heterogeneity while the subsurface was relatively dry . Further,plastic plants pots to validate the texture profiles generated by the ERT data, a set of six cores were taken along the transect of the ERT line down to nine meters with a Geoprobe push-drill system . The first meter of the core was sampled every 25 cm. Thereafter, cores were sampled based on stratigraphy as determined by changes in color or texture. The ERT profiles were used to develop the stratigraphic modeling scenarios and the coring guided the specification of the hydraulic parameters. Redoximorphic features were noted throughout the cores. centrifuge tubes with 40 mL of 0.5% sodium phosphate and shaken overnight . Samples were hand shaken immediately before a 2.5 mL aliquot was taken 11 seconds and 1 hour and 51 minutes , respectively after shaking and placed in a pre-weighed tin. Tins were oven dried at 105°C overnight and Statistical analysis was used to help guide the development of the geochemical reaction network. First, correlation analysis was used to inform the choices of primary geochemical species on the basis of the strength of their relationship with N2O. Second, on the basis of cluster analysis, stratigraphic configurations with different textural classes were developed. In particular, a Spearman’s rank correlation was conducted on the dataset including several physical and geochemical measurements collected on the soil cores. Specific variables included pH, N2O, NO3 – , NH4 + , DOC, Fe, Mn, S, total C, percent sand, silt, and clay, and depth. Variables were standardized using the median and mean absolute distance because most variables were found to be non-normally distributed based on the Kolmogorov-Smirnov test. To further understand how the data grouped, a cluster analysis was conducted using the partitioning around medoids method for the same set of variables. Interestingly, data were found to group according to textural classes and depth, which provides a mechanism to develop the modeling strategy around these textural profiles.Several scenarios were developed based on the soil textures identified in cores and the ERT profiles to provide insights into the effect of stratigraphic heterogeneity and AgMAR management strategies on NO3 – cycling in the deep subsurface, as described in section 2 above.

The five stratigraphies modeled in this study are shown in Figure 1. The limiting layer in the ERT scenario spans 187 to 234 cm-bgs based on field core observations. For each lithologic profile, three AgMAR management strategies were imposed at the top boundary between 20 m and 150 m of each modeled profile . For each AgMAR management strategy, the same overall amount of water was applied, but the frequency, duration between flooding events, and amount of water applied in each flooding event varied : a total of 68 cm of water was applied either all at once , in increments of 17 cm once a week for four weeks , in increments of 17 cm twice a week for two weeks , and all three scenarios with an initially wetter moisture profile . Note, that for all scenarios, the same reactions were considered, the water table was maintained at 15 m, and temperature was fixed across depths at 18°C, the mean air temperature for January to February in Modesto. For all scenarios, the modeling domain consists of a two-dimensional 20-meter deep vertical cross-section extending laterally 2,190 m and including a 190 m wide zone of interest located at its center, thus distant from lateral boundaries on each side by 1,000 m to avoid boundary effects. The zone of interest was discretized using a total of 532 grid blocks with a uniform grid spacing of 1 m along the horizontal axis, and a vertical grid spacing of 0.02 m in the unsaturated zone increasing with depth to 1 m in the saturated zone. A maximum time step of 1 day was specified for all simulated scenarios, although the actual time step was limited by specifying a Courant Number of 0.5, typically resulting in much smaller time steps during early stages of flooding. Before each flooding simulation, the model was run first to hydrologic steady state conditions including the effect of average rainfall . The water table was set at a depth of 15 m by specifying a constant pressure at the bottom model boundary , and the model side boundaries were set to no-flow conditions. Under these hydrologic conditions, the model was then run for a 100-yr time period including biogeochemical reactions and fixed atmospheric conditions of O2 and CO2 partial pressures at the top boundary, a period after which essentially steady biogeochemical conditions were achieved, including the development of progressively reducing conditions with depth representative of field conditions.

Instead, it subsidizes the production of cheap fats, sugars, and oils that fuel obesity; creates profit for food processors and corporate farmers; and supports agricultural practices that damage the environment, with long-term consequences for our health. The upcoming Farm Bill reauthorization requires that those concerned about health and well-being become involved in this issue and demand not only good economic policy but also sound health policy. In this article, we outline 3 major public health issues influenced by American farm policy. These are rising obesity; food safety; and environmental health impacts, especially exposure to toxics and pesticides.Two thirds of American adults are overweight and one third are obese.Though the prevalence of obesity remained stable through the 1960s and 1970s, America experienced an increase of more than 50% per decade in the 1980s and 1990s. These trends have significant long-term implications for our health and quality of life. The three leading causes of death in the United States are all associated with poor diet and overweight. Diabetes—America’s 6th leading cause of death—is also dramatically rising. The term adult-onset diabetes has become Type II diabetes as more young people develop the disease.If obesity trends continue, the lifetime risk of developing diabetes will be 1 in 3 for children born in 2000.There is increasing likelihood that for the first time in American history this generationof children will live shorter lives than their parents.The young and poor are most affected by rising obesity, but these trends hold for both sexes, all major racial and ethnic categories, geographic regions,growing raspberries in pots and socioeconomic strata.As Americans loosen their belts, they must also open their pocketbooks, because poor diets create additional costs to society.

Not only is poor diet linked to the major causes of death and increased medical spending, but it also carries other costs: overweight persons retire earlier, go into nursing homes at younger ages, have higher absenteeism rates, and are more likely to be disabled.The costs of obesity are borne not just by obese individuals but also by the public who supports their care: half of obesity-related medical costs are borne by public systems funded by taxpayers—Medicare and Medicaid.Public health professionals have achieved limited success in reversing obesity trends. Their main efforts focus on educating the public about the importance of individual behaviors and by supporting legislation to alter food and physical activity environments, especially in schools. But an unavoidable obstacle to success is the American food supply, which continues to provide an overabundance of cheap fats, oils, and sugars.Typical supermarkets and convenience stores contain an abundance of cheap, unhealthy food items. If tomorrow every American woke up and refused to consume anything but the foods recommended by the US Department of Agriculture Dietary Guidelines for Americans, there would be a catastrophic food shortage. Although the USDA guidelines recommend the consumption of fruits and vegetables as part of a balanced diet, the food system falls drastically short of providing enough fresh fruits and vegetables to meet their recommendations.The public health community has been slow to examine the link between food policy and public health. Until now, most attempts to reverse the American obesity epidemic have focused on changing consumer behaviors, but the results are depressingly inadequate. Little attention has been focused on examining the “upstream determinants”; namely, the food supply. Just as Americans have failed to ask why there is not enough healthy and affordable food, the public health community has failed to adequately consider what policies are driving the obesity epidemic. By following the pathway of public funds to what and how Americans choose to eat, one finds that American farm and food policies are major vectors of diet-related disease.

Fruits and vegetables are good for us. They lower the incidence and mortality of the most common chronic diseases in America.Yet less than 4% of totalUS cropland in 2004 was planted with fruits and vegetables.What is happening on the rest of our farmland? These acres are dominated by the 8 main “commodity” crops . Why is this the case? Government agricultural policies extend from the 1930s when federal policy-makers passed laws to create price stability and ensure the long-term economic viability of farming, particularly for family farmers. But in the 1970s, farm policy shifted away from maintaining stable prices to maintaining low prices and maximizing production of certain commodity crops that could be bought and sold on the international market. Direct payments were established to encourage competition and increase production, thereby lowering the price of these commodities. Farmers rely on government payments for economic stability, so they plant the crops that farm policy encourages them to grow. Seventy to 80% of all farm subsidies are directed toward the 8 commodity crops, which together cover 74% of US cropland. Farmers growing “specialty crops” such as fruits and vegetables are not eligible for direct subsidies and are penalized if they have received federal farm payments for other crops. In addition, large farms, which make up only 7% of the total, receive 45% of all federal payments. Meanwhile, small farms, which are 76% of the total, receive just 14% of the payments.The end result is a government-structured food supply that heavily favors just a few crops, grown by large-scale farming operations that fail to satisfy the healthy dietary needs of Americans .Certain subsidies provide a critical safety net to family farmers, but food processors are among those who gain the most from government payments. Processors have profited from the conversion of these subsidized commodities into processed foods sold at ever higher prices despite lower nutritional content. Between 1980 and 2000, consumer food expenditures in the United States increased two and a half times to $661 million, while the farm value of these foods increased only one and a half times. During this period, the proportion of each food dollar that went to farmers dropped from 31% to 19%, meaning that 81 cents of each dollar spent on food in 2000 went to non-farm-related activities, including labor, packaging, transportation, and marketing .

Our food system provides greater rewards to those who process, market, and distribute food than to those who actually grow it. Food processors, with proportionally more of their funds available for marketing, have been successful at creating new foods with desirable characteristics: low cost, convenience, high energy density, and appealing taste .13 With the additional support of government-sponsored product and processing research at land grant universities, these innovations use cheap agricultural inputs to make tastier and longer lasting foods with higher profit margins. Processed grocery foods dominate supermarket sales , and simultaneously the consumption of added fats and sugars has increased . Americans are eating more food, most of which is unhealthy. Between 1970 and 2000 the average consumption per person of added fats increased38% and average consumption of added sugars increased 20% . Researchers estimate that if we acted rationally and in our best interest, the average person over age 4 would consume about 2350 calories each day.Yet our food supply makes available 3800 calories per person each day. The price of fresh fruits and vegetables increased 118% from 1985 to 2000, and the price of fats and oils increased only 35%. Consumers are price sensitive, such that even small changes in the price of healthy foods affect their consumption.Not surprisingly,plant pot with drainage when ingredients are cheap, producers also compete by increasing portion sizes .The cost of the food itself is small relative to the price of preparing, packaging, shipping, and advertising, so the cost of increasing portion size is small relative to the perceived value of larger sizes. Cheap food inputs make it possible for food retailers to double the calories in an item while selling it for only cents more. This profitable strategy offers consumers short-term bargains but staggering long-term costs.While $21 billion dollars were spent under the Farm Bill to support commodity crop production in 2005,Americans are spending $147 billion a year on obesity-related illnesses, not to mention the costs of time, productivity, and quality of life lost.Agricultural policy subsidies come at a cost to public health. The system provides all consumers with excess fats and sugars, but especially vulnerable are children and the poor. Lifetime dietary patterns—healthful or not—are generally set early in life. Unhealthful patterns are important; obese children are likely to remain obese into adulthood. Poor families who live in low-income communities often find themselves living in food deserts, where healthy food options are unavailable but fast food abounds. Many older citizens who live on fixed incomes must choose between medicine and vegetables. Freedom of choice for consumers is desirable, yet we have a food system that increasingly limits healthy choices for large segments of the population, making unhealthy eating the default option.Foodborne pathogens cause approximately 76 million illnesses, 325,000 hospitalizations, and 5000 deaths in the United States each year.This too is related to the Farm Bill. Current US farm policies encourage a system that is both highly centralized and relies on large amounts of imported foods. American food travels through several stages and many miles as it journeys from farm to table—each link presents an opportunity for food contamination. Poorly monitored food imports, the threat of agro-terrorism, and our system of highly centralized food production put the safety of our food system at risk.Though foodborne pathogens most often affect raw foods of animal origin, the 2006 Escherischia coli spinach outbreak demonstrates the vulnerability of our entire food system to contamination.

Despite comprehensive food safety regulations and consistent food sanitation surveillance nationwide, a batch of contaminated fresh spinach from a single farm in Monterey County, California, infected 205 persons across at least 26 states in a 2-month period.This outbreak resulted in 102 hospitalizations and 3 deaths. How does contaminated spinach from one farm infect people all over the country? Spinach from California travels the country as a result of the large-scale centralized production and distribution of our food. When American farm policy changed in the 1970s to encourage low prices and competition between farmers, many went out of business. The farmers who survived were the ones who successfully increased their overall size and their investment in technology. Since 1900, the number of farms has fallen 63% and the size of farms has increased 67% .To reduce costs, large-scale farmers typically use highly centralized and mechanized production practices, including confined animal feedlot operations and monocultures. Though these methods are efficient, they create conditions that put plants and animals at risk of disease and microbial contamination and harm the environment. Monoculture techniques increase the risk of crop disease and deplete nutrients in soil, requiring the use of artificial fertilizers which evaporate, descend as acid rain, contaminate the water supply, and contribute to global warming.To promote rapid growth, cattle are frequently fattened with large quantities of grains that change the acidity of their digestive systems making them more vulnerableto pathogenic strains of E. coli. Increased shedding of such pathogens in animal waste occurs with the decline in the state of an animal’s health and an increase in its stress levels,both of which are exacerbated in CAFOs.Inadequate manure treatment, contamination of nearby fields and water, and contamination of slaughtered livestock are a frequently suspected sources of contaminated foods.To maintain the animals’ health, many producers dose the animals with antibiotics,a practice that poses its own set of problems . Centralization also creates large distribution channels through which contaminated foods may easily spread without aggressive vigilance. Though centralization may make detection of contaminated foods easier, potentially more individuals are at risk if contamination goes undetected. The consequences of a breach in food safety are much greater in this type of system. This is illustrated by the recent salmonella-tainted peanut butter scare, which sickened hundreds of people, caused several deaths, and put the Peanut Corporation of America out of business. Smaller, more isolated food systems are inherently less vulnerable to large-scale contamination.A highly centralized structure also increases the risks of harm from deliberate attacks. Biological agents introduced undetected into the system could result in a major disruption of our food supply. Additionally, high-speed, automated methods of slaughtering and food processing may make contamination both more likely and more difficult to detect.New threats to food safety have also arisen from global food trade.

Then as a young adult, she worked with other farms and Veritable Vegetable, a women’s cooperative and organic vegetable distribution company, all of which resulted in “a lot of influences around cooperative economics, cooperatively owning land, collectively owning land and managing land in that way” . Robinson continues to turn to these influences in conducting the work of the organization. In 2011, Urban Tilth staff visited Boston and the Dudley Street Initiative, a successful example of using a community land trust to provide affordable housing and gardening opportunities under a governance structure of community management. For Robinson, community land trusts can be an important means for residents to have actual control of neighborhood resources and to maintain the possibility for these community members to stay in their homes. “If we do all this work around food and whatever and then the population that we are trying to serve gets pushed somewhere else, what’s the point?” . While land trusts inspire many Bay Area urban agriculturalists, there are still relatively few land trusts working with urban gardens, in part due to the high costs of regional real estate. While trusts have shown interest in supporting urban gardeners, they are also interested in maximizing their impact with limited funds. The exception are small housing trusts and community development corporations, which have placed gardens on their land such as the 55th Street Garden in Oakland formerly run as a market garden by the People’s Grocery and now functioning as a community plot garden owned by the North Oakland Land Trust, a member owned intentional community owned by the Northern California Land Trust called the Mariposa Grove in Oakland, and the Tenderloin People’s Garden run by the Tenderloin Neighborhood Development Corporation. The Oakland Community Land Trust is currently developing a plan to better support urban agriculture, “Our primary role will be to acquire and provide secure access to land for residents and organizations looking to grow their own produce. Recognizing that fresh food options can be scarce in East and West Oakland,round plastic plant pot active urban agriculture and community gardens can serve as a healthy and locally accessible source of vegetables and fruits for neighborhood residents. OakCLT will support the gardening efforts of land trust homeowners, as well as residents and organizations already engaged in agricultural activities”.

McClintock suggests that urban gardens can resist capitalism by using the state and the state’s property. Gardeners can facilitate not only the reclamation of land as commons, but also the promotion of new commons such as genetic material in seeds and cultural culinary traditions . Cultivating the Commons, an action research and education project included the use of land inventory and emphasized public land explicitly. Through advocacy with the HOPE Collaborative and Oakland Food Policy Council, the Cultivating the Commons authors put the responsibility of providing land for production on the City of Oakland. As one gardener stated, “I think the use of public land is meaningful in a kind of normative way. It’s important to have this idea of creating these sort of common spaces” . The Edible Parks Taskforce is an example of attempt to reclaim public commons for community self-determination. This approach has particular traction in contemporary society and also has its constraints and detractors. In addition to gardeners discussing collective management and collective ownership, many gardeners speak to the material, perceived, and lived experiences of engaging non-capitalist value production. Projects create opportunities to reconceive ‘work’ as being outside a wage labor relationship, elevating the importance of social reproduction and promoting non-consumer based, collective experiences that sustain gardeners in various ways. In describing the goal to create housing and gardens on collective land, Tree explained, “And I think everybody should kind of like reclaim that space, that frame and that thought of sustaining ourselves, sustaining each other to building community.” . Another gardener described the difference between public parks as commons and their project, “Just that notion of saying like, this isn’t a store, it isn’t a business, it’s not a house, it’s not a park. I mean it’s interesting because the only form of commons that we have in the city are parks right? But the way you can relate with a park is in very limited ways. Like the park is maintained by the city for you to like walk through and enjoy, but after it closes you have to leave. La Mesa Verde, for example, instituted a system of “community guilds”, a concept borrowed from permaculture, which refers to a horticultural association of biotic and abiotic elements designed to work together to help ensure mutual survival and growth.

For LMV organizers, a guild can provide the space and structure for increased community support and sharing, a fundamental element of commoning. While coordinated sharing events are still in the future goals of the program, participants already use these networks for informal sharing. Program staffer, Patty Guzman, noted, “One family started seeds and brought seedlings to share with all the families. Others have brought cherry seedlings, nopales. Definitely with the fruit harvests we see a lot of sharing – avocado, chayote, peaches.” . Guzman also noted that some guild leaders have gone above and beyond the expectations she originally had. She described one leader of a Spanish-speaking guild on the East Side of San Jose: “She really pitched in for her members. She already knows them outside the class and so she works to help them even if they don’t come to meetings. Like if a participant’s husband doesn’t want her to go to class, would get her the information or plants outside of class time” . Many LMV gardeners are initially attracted to the program by the desire to increase self-provisioning of health food at home, but similar to the WinklerPrins and Souza study of Brazilian home gardens, LMV families demonstrate the links between household self-provisioning and informal economies of exchange. The labor of unpaid self-provisioning is conducted when gardeners’ time is not occupied with wage labor or other household tasks. Gardening, like other household labor and reproductive labor can be viewed as simply an essential support to capitalist economies . But as feminist economic geographers JK Gibson-Graham claim, this view excessively limits our ability to understand the non-capitalist elements of these practices . In other words, LMV gardeners are creating economic networks based on sharing, co-operation and mutual aide. These non-commodified practices promote alternative forms of valuing work and, as such, are alternatives to capitalist class processes. As I have described, gardeners have multiple claims to their practices and experiences of commoning.

Commons, or commoning, is comprised of three animating ideas. First, the commons provides a space or framework in which people are encouraged to reimagine how a community or resource is managed – promoting deeper and wider participation in decision making of those impacted. Second, the commons offers a definition of land access that moves away from private or state ownership. And finally, the commons affirms the production of non-capitalist forms of value. By using both concepts of commons that put pressure on the state to support urban gardens and those who see the power of urban agriculture as going beyond the limitations of a liberal state, the questions of how we reimagine urban governance and economic networks are emphasized. By encouraging forms of social relations based on increased participation and mutual aid, by challenging how land is used and distributed based on development priorities, and by refocusing their attention on producing non-capitalist forms of value and non-waged forms of labor, urban gardeners see their projects as part of the global movement for growing urban commons. Similar to those concerned with communal management for particular parcels of land, urban gardeners have connected their work to the greater struggle for gaining power in urban governance at large. Many gardeners work to try to gain community land management and in so doing gardeners connect their work to other justice oriented urban social movements including housing justice, economic justice, and the like. For these gardeners,25 liter round pot the central question becomes whether gardening is a movement with food production as an ends or as a means towards a larger scale of community organizing. Many urban scholars have documented the growing popularity of urban social movements since the late 1990s. Mayer argues that organizing has continued along three lines. First, urban movements have contested the patterns of neoliberal urban governance and growth politics. Contemporary urban space in the US exists in a constant state of contestation between capital, whose desire is to promote the greatest exchange-value, and urban movements that want to enjoy the use-value of the land . Mayer describes urban movements that contest the corporate control of urban development, accumulation by dispossession, gentrification and displacement. Movements have resisted new entrepreneurial policies, privatization of public goods, and gentrification through different strategies such as placed-based coalitions and symbolic disruptive actions . Second, urban movements continue to fight the dismantling of the welfare state, uniting along lines of social, environmental and economic justice. Third, the anti-globalization movements across the world manifest in the global north in cities where globalization’s impacts can be seen ‘touching down’ through outsourcing, privatization, and other impacts. Purcell concurs and adds that these movements are coalescing around a broad spectrum of issues to work to democratize cities and global processes in resistance to neoliberalism.

I would argue that in this same vein, today’s Occupy Movements express many of the same sentiments of outrage with the impacts of the dismal state of the economy and the highly unequal power dynamics that have lead to this situation. In fact, in Seeking Spatial Justice Soja speaks to primacy of the right to the city as a right to occupy and inhabit space.Haleh Zandi, of Planting Justice in Oakland, advocated that gardening could connect land and housing justice. She is inspired by the idea of “being able to partner with folks whose homes are getting foreclosed on, not only saving those homes from being foreclosed upon, but protecting those people’s rights and figuring out different financial solutions for them, but also building gardens in their homes, so that way, it’s like the banks aren’t taking people’s land and people’s homes and we’re committed to sustainable home environments where we’re not always eating food from 1,500 miles around the world. So, it’s connecting to the international movement for food sovereignty and land sovereignty, but really relevant to what’s happening systematically within the U.S.” . Similarly, in San Francisco, Markos Major of the former Growing Home Garden, saw their primary role, as volunteers in a garden focused on homeless, as “more about social justice… holding the space. We hold the space and people come in like these individuals and gentlemen and other people come and hang out and have a safe space.” . In reflecting on Growing Home’s social justice mission and prospects for continuing their work as they were being evicted, Major considered if only focusing on gardening alliance was strategic, “you know we’re not all the same, that’s the other thing we’ve realized I think. We’ve taken the relationship with Urban Ag alliance as far as we can. It [social justice] is really important and it’s unfortunate that it’s not a priority” . For Jeffrey Betcher of Quesada Gardens, gardening should be part of a movement for community organizing. He identified himself as a community builder, not an urban agriculturalist, although he has gardened and helped many others start gardens for over a decade. Betcher worries the current San Francisco urban agriculture movement shares similar obstacles to the Environmental Movement, namely its whiteness and focus on particular outcomes. Betcher argued, “… if were connected to urban community development and social justice movements, it wouldn’t look that way… People come to me as though of course I agree that if we plop a garden down, we’ll build community. And I have to say gardens don’t build community, people build community” . He went on to describe a garden project that he led that was conceived of and funded by people outside of the community, “if people can be involved at the beginning and really have the agency, can go in and say ‘ok this a shared resource, what do we want to do, it can be anything’. But now I have to go in and say, ‘You should know that if you choose a garden there are gonna be incentives for that’, and then the conversation goes in that direction” .

These trends have allowed US activists to increasingly identify with international calls for food sovereignty.Food sovereignty, which has largely been an international movement, is just now, post-global financial crisis, gaining popularity in the US. Food sovereignty contains an explicit critique of neoliberalism, not just for the wealth inequality it creates but also for the lack of control that communities have over the production of their food system. La Via Campesina has been an international leader in promoting the movement particularly in the context of peasant farmers in the southern hemisphere. Scholars and activists argue that a large part of the power of this movement stems from the southern origins of the ideas coming from groups like La Via Campesina, the MST , and others . Despite its basis in peasant struggles, the framework of this movement is being adopted in the US. Through a study of urban food movements in New York City, Schiavoni found the discourse of food sovereignty to be prominent as activists demanded that control of the food system be put in the hands of the people. In June 2010, the second US Social Forum brought together over 20,000 individuals in the wake of the largest economic depression the nation had faced in generations. At the forum the US Peoples Movement Assembly on Food Justice and Sovereignty drew around 150 individuals representing between 70-90 organizations to discuss the impacts of the global financial crisis and continued development of capitalist-industrial agriculture on farming and other communities in the US and world. At the Assembly, the US Food Sovereignty Alliance was born out of the former US Food Crisis Working Group, and a declaration was made claiming “It is our time to make salt”. La Via Campesina characterizes food sovereignty as a right to define agricultural and food policy from below and as a movement that goes beyond questions of policy to promote democratic control over the resources and processes involved in the food system . Advocacy does not stop with conscious consumerism but instead entails demanding control over productive and political resources to control the right to food.

The movement has been highly critical of international financial institutions, historical inequities in land distribution,25 liter plant pot and the commodification of food . Food sovereignty advocates argue neoliberal policy and institutions have largely perpetuated and frequently caused contemporary food crises, persistent food insecurity and lack of stability in rural areas of the Global South . Starting in the 1980s the liberalization of agricultural trade and development of structural adjustment programs sought to remove perceived barriers to economic progress through the dismantling of farmer subsidy programs, halting of agrarian reform processes, and opening of Global South markets to cheap agricultural imports from the North . Pressure from economic institutions such as the World Bank have promoted industrialized forms of agriculture designed to maximize production and in which peasantry is seen as an obstacle in need of modernization . Food sovereignty advocates dispute the need for the growth of capitalist industrial agriculture, claiming small farmers still feed the majority of the world’s population . Trade liberalization and state adoption and enforcement of these socio-economic policies are seen as primary catalysts in farmer displacement as well as an absolute barrier to local economic development and the promotion of food sovereignty . As such, food sovereignty activists are not just concerned with encouraging state institutions to make better decisions, but also with the redistribution of power in agrarian societies.Land access is a key issue for the international food sovereignty movement, which has impacted urban garden activism in the US. Land grabs, or “large scale land acquisitions” as financial institutions have termed them, have become a normal occurrence worldwide . Agricultural land is an important commodity for financial investors and state entities that see the need for continued enclosure and privatization in order to capture more of this $8.4 trillion land market . But land grabs and neoliberal dismantling of decades of agrarian reform in the Global South has been met with fierce resistance in many places. The MST, Zapatistas, and others have fought to reclaim, occupy, and put lands to community uses. Food sovereignty advocates have highlighted the absolute need for access to and control over landed resources .

Recently the US Food Sovereignty Alliance has launched a campaign to build awareness of the problems of land sovereignty for US food movements and promote resistance. I will explore this work in a later chapter. Food sovereignty advocates have critiqued the contemporary dominant global food system for its emphasis on the commodification of food . Hunger is seen as a direct result of this commodification. Commodity trading markets and the speculation by investors in food commodities have had significant roles in the dramatic rise in food prices in 2007 and 2008 . Commodification is seen as undermining communities’ abilities to value food for nutritional and cultural purposes as well as undermining the autonomy of these communities . Food sovereignty activism has challenged the place of food in commodity markets and sought to “defetishize” the commodity by increasing global understanding of the production processes behind global food. Alkon and Mares argued that food sovereignty is translocal and multiscalar. Food sovereignty as an international movement of peasants and advocates mirrors what Wekerle understood to be the translocal politics of food justice . While food sovereignty activists advocate for community-based economies and local bottom-up food and agricultural policy, local efforts are not seen in isolation from broader collective development. Postcolonial or decolonial work has highlighted the importance of valuing subaltern identities that may be place-based. In Wekerle’s analysis of food justice activism in Toronto, she cites Escobar’s research, which suggested that local and transnational social movements may be deeply connected. Acting through transnational networks, movements may choose, strategically, to utilize place-based identities . Escobar did not see the defense of local as simplistic communitarian politics. Instead he observed “subaltern strategies of localization” working through both place-based practices of connection to territory and culture and more globally oriented strategies that promote a politics from below . As such, food sovereignty holds a place in international social movements oriented against global capitalism. Movement gatherings, such as the World Social Forum and US Social Forum, align activists from diverse local commitments to discuss, debate, and articulate strategies and politics “from below”. Many activists advocate for a focus on deconsolidating power and decision-making paired with the development of democratically governed networks that may work at multiple scales . For food sovereignty advocates, these networks are envisioned similarly as places where self-reliance, autonomy and mutual aid are expressed between individuals and communities .

Food sovereignty has been a key component in many descriptions of solidarity economics, community economics, and other socio-economic models of respect and care. Commitment to the local as embedded in a better global raises the question of egalitarian universals. Patel described a core value of food sovereignty: “There is, at the heart of food sovereignty, a radical egalitarianism in the call for a multifaceted series of “democratic attachments” . Patel observed commitments to feminist, anticolonial, and other food sovereignty-based efforts challenging deep inequities in power. He argued a radical “moral universalism”, that of egalitarianism, may be necessary as a precursor to the kind of formal “cosmopolitan federalism” supported by food sovereignty advocates. While Patel viewed this position as potentially dangerous within the movements because it is promotion of universals as opposed to a completely bottom-up approach to values and practice, he argued this egalitarian commitment is already there. From this standpoint food sovereignty activists argue not just for culturally appropriate foods, but food produced, exchanged, and consumed in networks that value the cultural identities of peoples engaged . In the US and Canada decolonizing food projects have been gaining popularity in many cities. In Oakland, two History of Consciousness PhD program graduates and local professors run the Decolonize Your Diet Project which links spiritual healing and political resistance through reclaiming cultural ways of eating and knowing . Other Oakland organizations and groups like Planting Justice, Phat Beets, and Occupy the Farm host events and conversations with title like “Decolonizing Permaculture” or “Decolonize your Diet” where participants connect questions of cultural identity, racialized histories of place, the consumption and production of food,black plastic plant pots and the transnational movement for food sovereignty. The alternative food movement in the US has been concerned with environmental protection as a core value since its inception . Community food security and food justice activists in the US frequently have added ‘produced by ecologically sustainable means’ to definitions of alternative food systems. And many debates have occurred as to the meaning and practices of sustainability. Within agroecology as a field, an increasing emphasis has been placed on agroecology as engaged with questions of food systems, not just plot based questions of ecology and questions of social movements, not just individual behaviors of farmers or plants. Steve Gliessman, Miguel Altiere, John Vandermeer, and Ivette Perfecto, along with many other agroecological scholars, have led this charge since the 1970s. Food sovereignty, as a peasant-based movement, has had close connections to the field of agroecology as it has developed. Smallholder, traditional agriculture has provided both the socio-cultural and ecological basis of study for the field . Agroecological knowledge production and sharing has frequently, though by no means exclusively, focused on farmer-based approaches and farmer-to-farmer network development . Gliessman traced the roots of agroecology in Mexico to resistance to practices of the Green Revolution, which were seen as harmful to rural agriculture and communities. Gliessman cited the first example of the use of the term agroecology by Bensin in 1930 as one already framing a field of resistance to the overuse and over marketing of agrichemicals . In Mexico, agroecología developed with an emphasis on traditional knowledges of farming system practices, adaptation, and change.

For Gliessman, the example of agroecology’s history in Mexico pointed to this as a field concerned with a goal greater than just developing more environmentally sustainable agricultural production. Agroecology is “a social movement with a strong ecological grounding that fosters justice, relationship, access, resilience, resistance, and sustainability.” . Agroecology has developed with farmer movements that emphasize the importance of traditional and local agriculture . Altieri and Toledo have taken this a step further to argue that an “agroecological revolution” is unfolding in Latin America where epistemological, technical and social changes are occurring which prompt the development of selfreliant, low-input, agro-biodiverse agroecosystems that produce healthy food and empower peasant organizing efforts. This agroecological revolution has been framed as resistant to agribusiness and to neoliberal modernization and trade liberalization. This rapid spread of the agroecological revolution is in part thanks to the diálogos de saberes of La Vía Campasina where connective space is created for dialog between different knowledges, experiences, and ways of both knowing and practicing . Where agroecology, as a field and as practices engaged in by networks of farmers, comes together with agrarian struggles for food sovereignty, it may build significant power for socio-ecological change, as in the case of Ecuador’s food sovereignty law . Similarly, Rosset and Martinez-Torres found an increased adoption of agroecology by agrarian movements in recent years as both adopting agroecology-as-practice and agroecology-as-framing. Agroecology-as-practice has allowed some small farmers to ‘re-peasantize’ by returning to traditional farming practices or rejecting agribusiness. Agroecology-as-framing has given farmer organizations a critical tool in defending existing peasant territories and the repeasantizing of lands in public opinion. For many agroecologists the two struggles are inextricably entangled just as it is for urban political ecologists. Agriculture is a result of complex and constant interactions between social/economic and ecological factors . As documented above, agroecology and rural social movements have changed together, co-constituting each other in the socio-natural processes for better food systems. Justice for food sovereignty activists has multiple and complex meanings. Advocates are not solely concerned with access to adequate food resources or freedom from discriminatory social processes. Food sovereignty engages critiques of colonial/imperialist, capitalist, liberal statist, and anti-ecological socio-economic processes that dominate the contemporary world system. It is a movement that best engages the call for a reflexive approach to food politics . Theorists like DuPuis et al. have conceptualized justices that retain aspects of community autonomy and difference while foregrounding concerns of equity through reflexivity or dialectics .

Schlenker et al. indicate that dryland and irrigated counties require two separate estimation equations, unlike the single estimation equation in Mendelsohn and Dinar . A single equation erroneously implies that dryland farms requiring irrigation in the future will have access to analogous large-scale water projects peculiar to the western US at a given point in history. In testing the null hypothesis that each of the 16 climate variables in the original analysis are individually the same in the dryland and irrigated farm sub-groups, they find that between 4 and 6 coefficients are significantly different from 0, depending on the weighting method. Even though Schlenker et al. did not have access to water data on irrigated counties at the time, their F-test was still able to provide sufficient proof of the bias in pooling dryland and irrigated counties into one model. After studying climate impacts to dryland agriculture in the US , Schlenker et al. study the impact of water availability and degree days on California farmland values. Their cross-sectionaldataset represents individual farms, rather than county aggregates.Including groundwater and surface water supply corrects for the omitted irrigation variable bias in Mendelsohn et al. . Importantly, Schlenker et al. include a nonlinear measure of temperature effects on crop growth known as degree days.Their results suggest a positive relationship between the long-run annual availability of surface water and farmland value . They find that the coefficient on surface water is sensitive to water price: as water price per acre-foot increases, this coefficient decreases. They also find that the coefficient on degree days is positive and statistically significant, while degree days squared is negative and statistically significant. They do not use these relationships to estimate impact to farmland value under future climate scenarios. A criticism of degree days, as used in Schlenker et al. , is that it is a measure of weather not climate . In contrast to cross-sectional analysis, Deschenes and Greenstone estimate the impact of yearly fluctuations in weather on annual farm profits using US countylevel panel data , under 3 climate scenarios: uniform, Hadley II , and Hadley II .

When they account for county and year effects,hydroponic vertical farm the results from all three models show a negative impact on profits. With the addition of state-by year fixed effects, all three models show a positive impact on annual profits. Fisher et al. find data and coding errors in the Deschenes and Greenstone model, biasing the original results in the positive direction. Specifically, the climate variable on the average number of degree days has a zero value for several counties, and climate projections varied by state while their historic climate data varied by county. Both of these errors tend to result in a regression toward the mean effect, with warm counties projected to get cooler, and vice versa. In response, Deschenes and Greenstone acknowledge the data and coding errors, and find that the $1.3 billion benefit in annual profits under Hadley II is actually a $4.5 billion loss.However, Deschenes and Greenstone disagree that state-by year fixed effects are misspecified. Like Fisher et al. , they find that state-by-year fixed effects tend to absorb most of the weather variability, resulting in a positive impact on profits. Their purpose in including state-by-year fixed effects is to control for state-level shocks in prices and productivity. To test for this, Deschenes and Greenstone include two additional specifications of year fixed effects: varying according to 9 USDA Farm Resource Regions, and varying according to 9 US Census Divisions. The results from an F-test reject the null hypothesis of zero local shocks. Just as excluding all year effects, as in Fisher et al. , may bias the results downward, including fixed effects at the state level may be too strict, biasing the results upwards. The two intermediate cases of region-by-year fixed effects may present a “happy medium” to this problem. Schlenker and Roberts use panel data to study yield impacts to cotton in the western US.Constructing a dataset that incorporates the entire distribution of temperatures within a day, and across all days of the growing season, they find that the level of yield decline is greater under nonlinear temperature effects than linear ones. Even under a moderate emissions scenario , cotton yields decline across the western US by approximately 30%. Their approach is analogous to statistical crop studies discussed in the Impacts of Climate Change to California section .

Massetti and Mendelsohn test the use of panel data on a Ricardian model using the same Agricultural Census data as Deschenes and Greenstone . They test the stability of climate variables using two panel data approaches against a repeated cross-section Ricardian model. Both panel models have relatively stable climate variables across the six Census years tested. There are $15 billion in welfare gains for a uniform 2.7 C warming and 8% precipitation increase for both panel models, although this ignores distributional welfare impacts. In contrast, the climate variables of a repeated cross-section Ricardian model vary through time. As a result, the welfare calculations also vary through time. Deschenes and Kolstad use panel data on aggregate county-level farm profits to study the differential effects of climate and yearly fluctuations in weather . Their climate variables include a 5-year moving-average of the annual degree days and precipitation, while weather variables are represented by annual degree days and precipitation. While none of the coefficients on annual degrees days are statistically significant with either the historical or CCSM models, their study is instructive in finding that the climate variable has a greater magnitude than the weather variable both in the baseline and climate change scenarios. This corroborates the theory that long-term changes in weather are more costly for some farmers than short-run fluctuations. They tease out which farmers may be most affected by changes in climate by analyzing 15 of the largest crops . They find that certain crop revenues respond positively to degree days , while others respond negatively . A few econometric approaches study specific adaptations.Using a hedonic property value approach, they find that the marginal value of average water supplies from the Central Valley Project or State Water Project decreases as access to other sources increases. Lobell and Field study the use of federal crop insurance and emergency payments/loans in California from 1993–2007. They find that the most common cause of insurance and disaster payments during this period is excess moisture. Cold spells and heat waves are also important causes.

We return to the question posed in the title. What have we been assessing with respect to the human and institutional responsiveness known as adaptation to climatic change in more recent studies on the topic? Several sub-questions are subsequently discussed. To what extent have study results identified economically efficient adaptations? To what extent have economically efficient adaptations reduced vulnerability to climatic changes and/or welfare losses? Have these studies identified limits to adaptive capacity in the agricultural sector, tempering the optimism of earlier studies? We have examined both normative and positive approaches to studying adaptation. Normative approaches have provided insight into which adaptations may be economically efficient equating this with the optimal solution to the farmer’s objective function. There are two such adaptations explicitly represented in the CALVIN/SWAP models: changes to crop mix and water transfers/markets.9 As water resources decline, the resulting crop mix will reflect a decline in field crop acreage, with relatively less change for specialty crops . CALVIN includes water markets as an institutional adaptation. Under climate-induced water reduction scenarios, water is transferred from low-value to high-value use. Implicit in this is the transfer of land from agricultural to urban uses, though this is not directly modeled in these studies. In the WEAP-CVPM framework, Joyce et al. implicitly model the potential for converting to drip irrigation, particularly for thirsty field crops. By contrast, economically efficient adaptation is assumed, rather than modeled, in positive approaches, such as Ricardian models. Ricardian approaches have thus studied how climatic change will impact agriculture in the presence of long-run economically efficient adaptations. Without knowing the actual adaptations undertaken,nft vertical farming this approach provides limited analysis on economic efficiency. Hanemann argues that Ricardian models may not even capture long-run efficiency because economic agents do not behave optimally even in the long run. Studies of both short-run and mid-to-long run suggest that farmers with access to groundwater will tend to increase pumping, increasing the likelihood of aquifer subsidence, to compensate for losses in surface water or increases in crop water demands . Based on definitions in the latest IPCC report, this is maladaptation more than it is efficient adaptation. Schlenker and Roberts suggest that there is minimal adaptation even in the long run when they find that the results of their isolated time series are similar to those of the isolated cross section. Suffice it to say, that Ricardian approaches are capturing some level of adaptation, but it is likely not economically efficient. Panel data studies on farm profits are not able to capture adaptation even implicitly. In both programming and econometric approaches, vulnerability is measured as loss in economic welfare , which is perhaps the greatest limitation of comparative static approaches. Unlike economic welfare, vulnerability is a dynamic concept. For example, the move from field to high-value crops dampens the economic welfare decline caused by a warm-dry climate mid-to-late century. That is, the percentage loss in farm revenue is less than the decline in farm acreage. Water markets are also likely to dampen the welfare loss associated with climate change . However, these high-value crops tend to have lower heat tolerance as temperature increases . Further, field crops are generally regarded as more secure assets with lower associated production costs, than vegetable or tree crops.

The concept of vulnerability is able to capture this insecurity. Vulnerability to overall profit loss may be reduced by the crop mix change, but the increased variability in farm income will also increase vulnerability to temperature increases. Medellin-Azuara et al. illustrate this with high-value orchard crops, where the gross revenue declines even as prices increase. Econometric approaches illustrate that California agricultural land value may be particularly vulnerable to changes in surface water supply and nonlinear temperature effects . Deschenes and Kolstad also illustrate that farm profits may be more responsive to climate than annual fluctuations in temperature and precipitation. Several analyses illuminate our understanding of adaptive capacity. The overarching focus for many CALVIN-SWAP studies is to start with a worst-case scenario approach and see how well we fare even with some of the best-case farmer and institutional responses . Joyce et al. also illustrate an example of adaptive capacity through time. Assuming drip irrigation is more widely adopted in the Central Valley by mid-century, they find that groundwater pumping declines. However, as the climate continues to warm towards the end of the century, the positive effects of drip irrigation are eliminated. Beyond this, a discussion of adaptive capacity is lacking. We have moved ahead in the past 15–20 years from the early agro-economic assessments of the early/mid-1990s, but it appears that we are also standing still. This review has illustrated the various ways comparative static approaches have incorporated adaptive actions to illuminate our understanding of climatic impacts to California agriculture. But, as critics suggest , questions of when and how much farmers and institutions will adapt are left unanswered. Responsiveness — the key characteristic of decision-making — is only vaguely addressed, and, important distributional consequences of climate impacts to agriculture while alluded to, are not identified. Lack of responsiveness and distributional consequences is mostly due to a dearth of individual farm-level data, rather than the incapacity of programming and econometric approaches to accommodate a more specific analysis. Using the same county-level data with more innovations in a comparative static framework could only take programming and econometric approaches so far. There is also a degree of comfort with identifying the primary barrier to moving forward as uncertainty in climate projections. While vulnerability arises out of biophysical processes, it is critical to understand that it is imposed on a pre-existing, dynamic socioeconomic structure . It is important that our economic models do more to capture this structure. 

As Indian epidemiological evidence grows and concentration response function models are being developed and improved, future work may benefit from the adoption of a new method. Third, we assume a single diurnal cycle for burning emissions based on satellite information due to limited data of hourly burning activities from local sources . Lastly, since this study focuses on the broader air quality impacts over a large dispersion population, we do not specifically look at individual pollution hot-spots such as Delhi. We do however provide additional assessments for densely populated areas, where Delhi is a main recipient of pollution from agricultural fires .Our approach allows any proposed emissions change to be related to the eventual air quality impacts for the Indian population and sets the stage for future research into crop residue burning. Since we have focused most of our analysis on a single intervention, it would be a natural next step to examine the effects of such interventions in downwind locations using conventional forward modeling techniques. Online modeling considering aerosol-meteorology interactions is also needed to better understand whether these feed backs would suppress or enhance reductions in exposure. Furthermore, since our assumed diurnal pattern of burning may not reflect true fire activities, focused observational work on burning practices is needed to verify that these benefits are realizable. In addition, a deep assessment of any single alternative is needed to determine how plausible such an intervention would be in practice. Our study estimates the total annual premature deaths and the value of mortality risk reduction attributable to PM2.5 exposure from crop residue burning in India over 2003–2019. We also estimate the efficacy of marginal changes to reduce these impacts at the district level,stacking flower pot tower finding that a small number of administrative regions could be prioritized to provide the maximum air quality improvement. We find that six districts in Punjab are responsible for 40% of the nationwide air quality impacts as a result of meteorological factors, the size of the downwind population, and the use of residue-intensive crops.

Our work provides additional insights into potentially low-cost interventions that may significantly reduce the air quality impacts, such as shifting to burning in the morning rather than afternoon and promoting less residue-intensive crops . These findings provide a quantitative basis for the design and optimization of mitigation strategies for crop residue burning on a broad scale, as well as providing new opportunities for future regional and local studies on agricultural fires in India.Consistent with GBD 2018 India Special Report, we calculate emissions from agricultural residue burning using the Global Fire Emissions Database v4.1s from 2003 to 2019 and from 1997 to 2019. GFEDv4.1s is a hybrid emissions inventory that incorporates satellite and ground-based measurements to estimate fire emissions of various types . In particular, it includes a small fire boost based on active fire detections outside the burned area extent, which improves estimation of emissions from frequent and/or short-lived burning events. A comparison using alternative fire emissions inventories is provided in the Supplemental Information. Similar to Koplitz et al. 2016, we define burning-attributable PM2.5 as the sum of black carbon and organic carbon , the primary components of fire smoke-related PM2.5. The diurnal pattern of fire activity in the standard GFEDv4.1s product is estimated using an emissions redistribution approach. The diurnal cycles of burning are estimated based on observational data from geostationary satellites over the Americas, which are then applied to other parts of the world by matching three broad fuel types. While appropriate for many applications over North and South America, this method is not likely to accurately reflect agricultural residue burning in India because the crops grown, crop cycles, field size, and crop practices are different. We therefore apply an alternative diurnal cycle for agricultural residue burning in India based on satellite information from prior literature. The fire activity in sub-tropical areas is typically more intense in the early- to late-afternoon. Over India, the fire counts from MODIS Aqua are three to four times greater than those from Terra during periods of crop residue burning.

Based on this information, and in the absence of more reliable and/or accurate observational data specific to agricultural burning, we assume that agricultural burning emissions have a triangular profile , where 95% of emissions occur between 06:30 LT and 19:30 LT, with a peak at 14:30 LT. Sensitivity to this assumption is explored in Supplementary Discussion.We use the adjoint of the GEOS-Chem atmospheric chemistry and transport model to quantify the sensitivity of annual mean population exposure to PM2.5 in India with respect to emissions sources in the extended Asia domain. Adjoint simulations are performed at a resolution of 0.5° × 0.667° , with 47 uneven vertical layers from the surface up to 80 km altitude. Boundary conditions are saved from global runs at a resolution of 2° × 2.5°. The adjoint model quantifies the effect of changes in any emissions species at any time and any grid cell in India on a scalar quantity J. In our case, the cost function is the India-wide population weighted exposure to PM2.5. The adjoint approach has been widely applied in inverse problems such as air quality impact attribution, which suits the need of this study. We use GEOS-5 meteorological fields from the Goddard Earth Observing System of the NASA Global Modeling Assimilation Office and non- fire anthropogenic emissions from the Emissions Database for Global Atmospheric Research v4.3.2. Each adjoint simulation first requires a conventional, forward simulation to be performed; the data from these forward simulations is compared against observational data in our model validation . Two sets of simulations are run with the GEOS-Chem adjoint model. First, we perform three sets of simulations for three full years which respectively represent a typical rainfall condition for a “flood”, “drought” and “normal” year, based on 20-year monsoon rainfall data in India . Each set includes an adjoint run and a forward run . For each year we calculate the sensitivity of annual population-weighted exposure to PM2.5 across all of India with respect to emissions from December 1st the previous year to January 31st the year after. The first and the last month are discarded due to model spin-up and down, such that data for the whole year are used in the analysis. We then classify 2003–2019, where daily fire emissions are available, into three categories by meteorology type . By applying adjoint sensitivities with gridded agricultural fire emissions corresponding to their rainfall condition, we estimate the total change in population-weighted exposure for the entire Indian population due to emissions from crop residue burning for each year. Second, we perform two other full-year adjoint simulations, where the cost function is modified to annual population-weighted PM2.5 exposure for population in urban areas and highly populated areas, for a typical “normal” year . We define urban and densely populated areas as locations in which the population density exceeds 400 and 1,000 people per km2 , respectively. Besides estimating impacts on India as a whole, this allows us to separately quantify the impact of residue burning on different population groups, as people living in densely populated areas may be exposed to different exposure levels than those living in rural areas .

To inform estimates of long-term trends in exposure and the spatial distribution of impacts, we use the “forward” model GEOS-Chem Classic and perform 23 sets of conventional, forward-running simulations for September 1st to December 31st for each year between 1997 and 2019, where monthly fire emissions are available. September is discarded due to model spin-up, and only October to November are considered for the “post-monsoon season”. Each simulation is performed over the extended Asia domain at a resolution of 0.5° × 0.625° , with 73 uneven vertical layers from the surface up to 80 km altitude. Similar to adjoint simulations, boundary conditions are saved from global runs at a resolution of 2° × 2.5°. Each set includes two simulations with and without Indian agricultural residue burning emissions,ebb and flow which provides information on the long term impact of Indian post-monsoon crop residue burning on population living in neighboring countries including Bangladesh, Nepal and Pakistan. We use meteorological data from the ModernEra Retrospective analysis for Research and Applications, Version 2 and monthly agricultural residue burning emissions data from GFEDv4.1s. This data is also used in our model validation.Here the cost function for the adjoint simulation, J, is the annual mean population-weighted exposure to PM2.5 within India, including 29 states and seven union territories which are further divided into 666administrative districts. The adjoint method quantifies a linearized relationship between emissions and PM2.5 exposure. This makes it well suited for computing the impact of marginal emissions changes of a particular type at a particular location or time. Although there may be non-linearities that are not captured by this approach, the atmospheric processes relevant to PM2.5 ––wet deposition and advection––are accurately represented as linear operations in the GEOS-Chem model. As such, the error due to atmospheric non-linearities is expected to be small. Atmospheric chemistry-transport models depend on emissions inventories to compute air quality impacts, and our estimate and attribution of population exposure is subject to the specific choice of emissions inventory. While various global fire emissions inventories have been developed, differences across inventories such as satellite image interpretation and adjustment for small fires can result in large regional differences in emissions estimates. We select six global emissions inventories, including five commonly used and one newly-developed for Indian agricultural residue burning, and make an inter-comparison by calculating PM2.5 exposure due to post-monsoon crop residue burning using each of the emissions inventories . We find that exposure estimates vary by up to a factor of seven due to uncertainty in emissions inventories . However, we find that this does not significantly affect our conclusions, which are focused on the relative reduction in harm which could be achieved through targeted interventions. Detailed comparison and discussion can be found in Supplementary Discussion.Do political leaders benefit from anti-poverty programs? There is a large and growing literature on the targeting of government expenditures, but less is known about the political effects of distributive programs, particularly large-scale poverty-reduction efforts that target substantial portions of the population. Across Africa, governments have increasingly adopted agriculture subsidy programs in recent years to combat rural poverty and food insecurity, embracing a strategy common in the 1960s and 1970s before structural adjustments programs reduced such market interventions in the 1990s . While the political appeal of agricultural subsidies in countries where the majority of the population is engaged in smallholder agriculture are obvious, there has been little quantitative research on their effects.In part this lacuna stems from the difficulty of quantifying the political effects of subsidy programs. Because subsidy programs may be targeted, often for political reasons , researchers must confront the thorny challenge of teasing apart selection effects from potential treatment effects. This paper contributes to studies of distributive politics by examining Malawi’s Agricultural Input Subsidy Programme , one of the largest and most expensive programs implemented to date. To examine whether the incumbent party, the Democratic Progressive Party headed by president Bingu wa Mutharika, benefited from Malawi’s subsidy program, we draw on panel data from a survey of 1,846 respondents interviewed in 2008 and again in 2010. We proceed in two steps. We first investigate whether the program was targeted at the local level. We propose that because of informational constraints and the weakness of party institutions at the grassroots level, the subsidy is likely to be untargeted with respect to party support and the main determinant of party allegiances – ethnicity – at the village level. Consistent with these expectations, we find no evidence of partisan or ethnic targeting in our sample area. This finding is interesting in its own right, especially given dominant theories of distributive politics that argue whether politicians benefit by targeting material transfers to core supporters or swing voters . The second step in the analysis is to test for potential effects on preferences. While we find no evidence of political targeting at the individual level, we do not claim that distribution was random. Accordingly, testing for political effects requires accounting for potential confounding factors. We employ two alternative methods for addressing possible omitted variables.

IoT4Ag launched its collaborative programs across the four NSF ERC pillars of convergent research, engineering workforce development, diversity and culture of inclusion, and innovation ecosystem. IoT4Ag research is creating novel, integrated systems that capture the microclimate and spatially, temporally, and compositionally map heterogeneous stresses for early detection and intervention to better outcomes in agricultural crop production. The Center is working to realize IoT technologies to optimize practices for every plant; from sensors, robotics, and energy and communication devices to data-driven models informed by plant physiology, soil, weather, management practices, and socio-economics. Diverse participant groups have been and continue to be recruited and are being educated through IoT4Ag workforce development and diversity & culture of inclusion programs to have strong science and engineering knowledge to create transformative, socially-just, engineered products and systems. The Center is working to build a workforce able to discover, innovate, translate, and practice precision agriculture solutions. IoT4Ag has established and continues to expand an innovation ecosystem and network with academic, industry, investment, and government partners and the end-user farming community to collaboratively build the future of precision agriculture. IoT4Ag’s research program aims to transform agriculture today and invent integrated systems to realize the farm of the future . IoT4Ag is working to create next-generation IoT sense-communication response technologies and establish engineered integrated systems for precision farming of tree crops and row crops, mainstays of the food supply chain.

The Center’s research is driven by the agricultural-specific use case of IoT, e.g., its scale, the environment, and the socioeconomics. It is pushing the fundamental scientific understanding and bringing together the tools of our disciplines, i.e., the fields of agronomy, agricultural engineering, agricultural economics, environmental science, and of chemistry and chemical engineering, computer science, and electrical, materials, mechanical, and systems engineering. It is propelling us, in partnership with our innovation ecosystem, to create “IoT4Ag breakthrough technologies” in sensors, robotics, and energy and communication devices to inform data-driven models constrained by plant physiology, soil, weather, management practices, and socioeconomics that enable the optimization of farming practices for every plant. Integrated systems engineered from these technologies are being designed to capture the microclimate and spatially, temporally, and compositionally map heterogeneous stresses for early detection and intervention to ensure better outcomes in agricultural crop production. The Center is structured into three thrusts that vertically integrate fundamental knowledge and technology from different disciplines and that are horizontally integrated to achieve next generation engineered systems for agriculture. The “flow” or “wiring” diagram in Fig. 3 portrays the scientist’s or engineer’s depiction of the structure and connectivity of the three thrusts to realize the sense-communication response integrated systems of the farm of the future to realize better outcomes in agricultural crop production. The diagram also shows the structure and connectivity of IoT4Ag thrusts and the requisite convergence of disciplinary expertise. Plant and environmental scientists are exploring the biotic and abiotic variables that affect crop health and are working together with engineers to design and specify sensors that are embedded in the field, to measure these variables from above and below the soil surface. Multi-mode sensors are being co-designed and co-created with energy and communications technologies for the agricultural use case that calls for sensor systems that require zero- or near-zero power, are low cost, can be deployed at large scale, are bio-compatible/biodegradable,hydroponic nft gully and can operate below the soil surface and in/below the canopy. Signals are transmitted at the “edge” to existing farm machinery or to ground and aerial robots, that are being adopted by the farming community.

Robots are being codesigned and equipped with energy and communications technologies to allow autonomous, coordinated multi-robot excursions at the large scale of agricultural fields and to receive and process signals at the edge, directly imaging the field and indirectly imaging sensors from above and below the canopy. A suite of Ag-specific backhaul technologies are investigated to transmit signals to the cloud in the characteristically remote and “unconnected” environments of agricultural fields. Multiple instance, multiple resolution sensor fusion techniques are being developed to unite the spatially, temporally, and compositionally heterogeneous sensor data. Models that are data-driven, and yet constrained by the biophysics of plant physiology, the soil, weather, and management practices are being created to “make the invisible visible” and provide “better data”. These models are being used to build a decision Ag interface, which coming full circle, allows farmers to intelligently manage their fields to ensure crop yield and resiliency in a cost-effective manner. Thrust 1 research is in the design and manufacture of resilient, networked, intelligent sensor-robotic systems that monitor the state of plant and soil health over extended areas. Thrust 1 is addressing fundamental scientific questions to uncover how the complex system of abiotic and biotic variables affect crop yield and resilience, and with this knowledge is designing technologies and systems that will be deployed with the spatial, temporal, and compositional resolution needed to capture the state of the field. Thrust 1 unites faculty research groups from eight departments across all four partner universities with expertise in plant and environmental science and in sensors, robotics, and mapping of agricultural fields. hrust 2 research is in enabling advanced approaches for powering IoT devices and robots in the field and for data communication from heterogeneous platforms of sensors, robots, and farming equipment. Thrust 2 is working to establish the knowledge and technologies specifically needed in agriculture, from powering devices and communicating from below the soil surface to deploying technologies at field scales.

Thrust 2 is composed of faculty groups from four departments and three of our universities with expertise in IoT sensor and robotic power and in edge and backhaul communication. Thrust 3 research is in building and deploying smart response systems that are driven by machine learning and decision-based models for precision agriculture. Thrust 3 is creating techniques to manage uncertainty and fuse the spatially, temporally, and compositionally heterogeneous data from the field to collect not just more, but better data. The thrust is building models, constrained by the biophysics of plants in agricultural fields, to establish a decision-Ag interface for growers to intelligently manage their fields in a cost-effective manner. Thrust 3 is brings together faculty groups from seven departments and our four universities with expertise in machine learning and sensor fusion and in controls and decision agriculture architectures. Fig. 4 is a Milestone Chart describing the work of the thrusts to deliver IoT4Ag technologies and to increase their complexity and scale over the lifetime of the Center to realize the two IoT4Ag testbeds, i.e., 1) Integrated Systems for Precision Farming of Row Crops and 2) Integrated Systems for Precision Farming of Tree Crops. In Year 1, 28 multi-institutional, multi-disciplinary, multi-thrust research projects vertically integrating the ERC 3-planes of fundamental knowledge, enabling technologies, and integrated systems across three horizontally integrated research thrusts were launched. A number of projects are operating within the IoT4Ag test beds. The fundamental knowledge and enabling technologies are intimately connected. For example, IoT4Ag is working to probe the theoretical limits of electromagnetics, important to understanding signals in the soil, canopy occlusion, and signal interference; to create of a suite of Ag-specific communication technologies that connect sensors located in remote and obstructed agricultural environments to the cloud. The Center is advancing materials properties and processes, e.g., from host guest chemistry to low-cost processable, biodegradable, and biocompatible materials, to realize sensors, that measure variables of interest, and energy devices, that operate in the soil, and allow agricultural field scale measurements. IoT4Ag is developing machine learning approaches to deliver robust predictive models that effectively capture site-to-site variability due to environmental changes and decision science to synthesize Decision Ag interventions that are interpretable, risk-based, and economically feasible. Finally, and coming full circle, IoT4Ag ‘sense communication-response’ technologies are impacting agronomy,aeroponic tower garden system addressing fundamental scientific questions such as understanding how abiotic and biotic variables affect crop yield and resilience. The Center is educating diverse groups of students and professionals to build and practice precision agricultural science, IoT technologies, and systems. IoT4Ag is engaging K-12 and community college students; through exhibits, kits, and lessons/labs with our partner schools, museums, and organizations; high-school students and teachers and community college and undergraduate students in research experiences; PhD and postdoctoral fellows in interdisciplinary research and intraCenter and international exchange; and agricultural professionals and growers through IoT4Ag Ag-extension programs.

IoT4Ag is committed to creating, sustaining, and promoting a diverse community by developing and delivering programs, based on good practices, to create transformative changes in engagement, equity, and inclusion of diverse groups in science and engineering and in the practice of agriculture that creates a lasting sense of belonging for Center members and a positive, productive, collaborative climate. The IoT4Ag ERC provides a platform of disciplinary, institutional, and demographic diversity amongst the core institutions and its partners in research, workforce development, and innovation ecosystem to unite and include diverse groups as they educate each other and work collaboratively to achieve the common goal of realizing food, energy, and water security to benefit society through the development of transformative, socially just engineered products. Diversity & Culture of Inclusion educational programs foster critical reflection about issues that intersect innovation and equity, such as facilitating technological access in underserved communities, ethics in agriculture, data governance, and algorithmic and implicit bias. IoT4Ag research efforts will lead to systems that combine state-of-the-art sensors, robotics, communications, and data science approaches for monitoring the state of a field of crops with high spatial and temporal resolution and making decisions on this data using bio-physically informed models. Even with successful achievement of the IoT4Ag mission to create and translate these precision agriculture technologies and systems, a mission which is necessary to realize the overarching vision of improved crop yields with less water, energy, and fertilizer use as outlined in Section I, it may not be sufficient. Technical and non-technical challenges that are outside the scope of the Center could limit the impact of IoT4Ag technologies and systems and prevent the vision from being achieved. Three primary risks are briefly discussed here. First, the Center will have highest impact if local interventions can be made quickly and cost effectively based on the data and insight provided by IoT4Ag integrated systems. The development of intervention approaches is not within the scope of IoT4Ag’s work, so these technologies must be developed by other researchers and companies. If approaches for local interventions do not advance quickly enough, IoT4Ag systems may have less impact than anticipated. To mitigate this risk, we are creating systems designed to work with both existing and more nascent local interventions. Furthermore, we are continually keeping track of the state of intervention technologies, in part through connections with industry members and end-users in the Center, and will adjust our technology road map based on internal and external advances. Second, IoT4Ag systems are being developed to take advantage of data from multiple sources, this includes our own sensors, commercial sensors and agricultural equipment, and public and private sources. Standards and policies for accessing, sharing, and using data from a number of these sources is quite variable and also evolving as precision agriculture technologies develop. We are working to mitigate this risk by engaging stakeholders, including end-users and agricultural companies, through the Center to understand perceptions and expectations regarding data privacy and accessibility. As we develop decision systems in Thrust 3, issues related to data standards and access are actively being addressed in our projects. Finally, IoT4Ag systems will only have impact if the technologies are adopted by end users. Adoption is not guaranteed even if the systems are engineered to meet performance targets and economic constraints. Adoption will also require education of end users on the benefits and implementation of IoT systems which are different from existing management practices. To mitigate this risk, we are and will engage with members of IoT4Ag’s ASAB and agricultural professionals, including crop consultants, through our research on adoption and our professional education activities as part of our workforce development, to identify routes and broadly disseminate information about IoT4Ag systems. The IoT4Ag logic model for the Center convergent research pillar is shown in Fig. 5. The model highlights the convergence of institutionally, disciplinarily, and demographically diverse IoT4Ag faculty and students from academia with partners in education, government, industry, and the end-user farming community.