Human subjects in this research are protected under the Committee for Protection of Human Subjects, protocol number 2018-04-1136 , of the Office for Protection of Human Subjects at UC Berkeley.Siskiyou is a large rural county located in the mid-Klamath River basin in Northern California . Since the mid-19th century, immigrants have historically engaged in agriculture, predominantly livestock grazing and hay production, and natural resource extraction, primarily timber and mining . Public records demonstrate that although the value of the county’s agricultural output and natural resource extraction is declining, these cultural livelihoods still shape the area’s dominant rural values of self-reliance, hard work and property rights . For instance, one county document stated that Siskiyou’s cultural-economic stability depends on nonintervention from “outside groups and governments” and residents should be “subject only to the rule of nature and free markets” . Another document, a “Primer for living in Siskiyou County” from the county administrator, outlined “the Code of the West” for “newcomers,” asserting that locals are “rugged individuals” who live “outside city limits,” and that the “right to be rural” protects and prioritizes working agricultural land for “economic purpose[s]” . We heard a common refrain that localities will eventually succumb to the allure of a taxable, profitable cannabis industry. Indeed, interviewees in Siskiyou universally reported economic contributions from cannabis cultivation, especially apparent in rising property values and tax rolls and booming business at horticultural, farm supply, soil, generator, food and hardware stores . However, a belief in an inevitable free market economic rationality may underestimate the deep cultural logics that have historically superseded economic gains in regional resource conflicts . As one local store owner told us, “I’d give up this new profit in a heartbeat for the benefit of our society.” Many long-time farming and ranching families remain committed to agricultural livelihoods for cultural reasons , even as the economic viability of family farms is threatened by increasing farmland financialization ,hydroponic gutter corporate consolidation and biophysical decline . Many interviewees felt that the recent rapid expansion of county cannabis cultivation and corresponding demographic changes were a visible marker of broader tensions of cultural continuity and endangerment.

As the sheriff expressed, cannabis cultivation would “jeopardize our way of life … [and] the future of our children” . This sense of cultural jeopardy , echoed by numerous interviewees, materialized in a range of negative quality-of-life comments about cannabis cultivation: noisy generators, increased traffic, litter and blighted properties, and unsafe conditions for residents. Non-cannabis farmers also reported farm equipment and water theft, livestock killed by abandoned dogs, wildfire danger, illicit chemical use and poisoned wildlife. Some non-cannabis farmers expressed a sense of regulatory unfairness — that their farms were subject to onerous water and chemical use regulations while cannabis growers “don’t need to follow the government’s regulations.” Enabling cannabis cultivators to pursue state licensure would facilitate just such civil regulation, but some feared that regulating this crop as agriculture would threaten “the loss of prime agriculturally productive lands for traditional pursuits” . If nothing less than the county’s culture and agricultural order were considered at stake, it is no wonder that absolute, even prohibitionist, solutions emerged in Siskiyou, with the Sheriff’s Office having a central role in defending local culture.Siskiyou’s sparsely populated landscape has been home to illegalized cannabis cultivators at least since the late 1960s, largely in remote, forested, and public lands in the western part of the county. Medical cannabis’s decriminalization in 1996 inaugurated a modest expansion of cannabis gardens throughout the county . However, for the next 19 years, Siskiyou did not establish regulations for medical cannabis, in line with locally dominant ideologies of personal freedoms and property rights. Instead, the county relied on de facto management of cultivation by law enforcement and the court system’s strict interpretation of state law . In 2015, informed by public workshops held by the Siskiyou County Planning Division, supervisors passed the county’s first medical cannabis ordinance, which seemingly balanced concerns of medical cultivators and other county residents. Regulation would be overseen by the Planning Division, which placed conditions on cultivation , limited plant numbers to parcel size and would establish an administrative abatement and hearing process for complaints.

The Planning Division, however, had been without code enforcement officers since 2008 budget cuts. Though the county authorized the hiring of one civil code officer in 2015, the Sheriff’s Office felt that the Planning Division “needed outside help” and moved to assist. Soon, the county’s limited abatement capacities were overwhelmed by vigorous enforcement and a wave of complainants. County supervisors, responding to the sheriff’s 2015 reports on the “proliferation” of cannabis gardens on private property, moved to heighten penalties for code violations, place numerous new restrictions on indoor growing and ban all outdoor growing . These strict county measures, which discarded and replaced publicly developed regulations, stoked reaction. When the Siskiyou County Board of Supervisors met in December 2015 to vote on these measures, advocates and cultivators presented 1,500 signatures to forestall its passage, a super majority of attending residents indicated opposition, and supervisors had to curtail 3 hours of public comment to vote. Despite this showing, supervisors passed the restrictive measures, prompting cannabis advocates to collect 4,000 signatures in 17 days to place the approved ordinances on the June 2016 ballot. Meanwhile, the Sheriff’s Office enforced the new stricter regulations . The Sheriff’s Office assumption of code enforcement blurred the line between noncompliance with civil codes and criminal acts. Stricter ordinances, still in effect in Siskiyou, created a broad, nearly universal category of “noncompliance.” No one we interviewed, including officials at the Planning Division and Sheriff’s Office, knew of a single cultivator officially in compliance. One interviewee estimated that growing 12 indoor plants would cost $40,000 in physical infrastructure, in addition to numerous licensing and inspections requirements, effectively prohibiting self-provisioning. The Sheriff’s Office notified the public that it would initiate criminal charges against “non-compliant” cultivators, specifically those suspected of cultivation for sale , child endangerment or suspected drug trafficking . Since the county regulations produced a situation where no one could comply, law enforcement could effectively criminally pursue any cultivator. The slippage from civil noncompliance to criminality was mirrored in enforcement practices. Investigations were “complaint driven,” meaning not only that warrants could be issued in response to disgruntled neighbors upset about a barking dog on a cultivation site, as one person reported, but that police officers could serve as a kind of permanent, general complainant and take “proactive action” when they spotted code violations .

Administrative warrants allowed deputies to enter properties with a lower evidentiary bar than they would have needed for criminal warrants, leading one patients rights group — Siskiyou Alternative Medicine — to file a lawsuit alleging county violations of Fourth Amendment protections against unreasonable search and seizure . In effect, cannabis’s criminal valences in the county endured through California’s shift of cannabis from criminal to civil provenance. Formerly illegal activities continued to be formally or informally treated as criminal matters,u planting gutter as researchers have noted with other stigmatized activities and groups, for example, after the decriminalization of sex workers in Mexico . Also, enforcement of civil matters can lead to substantive criminalization when those matters are stigmatized, as in the regulation of homelessness . While it is not unique for police officers to enforce civil codes, what is unique in Siskiyou County is the assumption of the entire civil process under the sheriff’s authority. To understand how this civil process became criminally inflected, in a county that voted for statewide cannabis legalization in 2016, one must first understand significant contextual shifts in who was growing cannabis where — and the challenge this posed to dominant ideas of land use, agriculture and culture. Since 2014, cannabis gardens have emerged on many of the county’s undeveloped rural subdivisions in unincorporated areas of Siskiyou. Subdivided into over 1,000 lots each in the 1960s, these subdivisions contain many parcels that are just a few acres in size and relatively inexpensive. Previously populated mostly by white retirees, squatters and a few methamphetamine users and makers, the parcels were often bought sight-unseen as investments or potential retirement properties, with most remaining unsold and undeveloped until the mid-2010s. In 2014, these subdivisions became destinations for Hmong Americans from several places, including Minneapolis, Milwaukee and Fresno; many of them cultivated cannabis. The inexpensive, sparsely populated, rural subdivisions enabled Hmong-Americans to live in close proximity to ethnic and kin networks, which multiple interviewees expressed was especially important for elders who had migrated to the United States as refugees after the Vietnam War. The county sheriff estimated that since the mid-2010s around 6,000 Hmong-Americans had moved to Siskiyou, purchasing approximately 1,500 parcels . In an 86.5% white county with just 745 non-cannabis farms and fewer than 44,000 people , this constituted a major demographic shift. Cannabis growers in Siskiyou’s subdivisions are especially vulnerable to detection. The subdivisions are often sparsely vegetated, dry and hilly, making them not only unproductive as agricultural lands but also highly visible from public roads, horseback, neighboring plots, helicopter and Google Earth. Green screen fencing, wooden stakes, portable toilets, generators, campers, plywood houses, or water tanks and trucks often signal cannabis cultivation but would be necessary for many land uses, especially since many lots are sold without infrastructure like water, sewer or electrical access. If detection of code violations depends upon visibility, Hmong Americans on subdivisions have been made especially visible and vulnerable to detection.

One lawyer, for instance, reported that 90% of the defendants present at administrative county hearings for code violations in fall 2015, when the first complaint-driven ordinance was put in place, were Hmong-American. One Hmong-American resident reported being stopped by police six times in 3 months and subjected to unfriendly white neighbors patrolling on horseback for cannabis — one of whom made a complaint for a crowing rooster, a questionable nuisance in this “right to farm” county. Numerous Hmong-Americans and sympathetic whites echoed these experiences. County residents confirmed their antagonism toward Hmong-Americans by characterizing them in interviews and public records as dishonest, thieves, polluters, negligent parents and unable to assimilate, and making other racializing and racist characterizations. While written regulations and enforcement profess race neutrality, in a nuisance enforcement regime based on visibility, Hmong Americans were more visible than others, leading many to argue that they were being racially profiled. Rhetoric emerging from the county government amplified racial tensions and visibilities. Numerous Sheriff’s Office press releases located the “problem” in subdivisions and attributed it to “an influx of people temporarily moving to Siskiyou” who were “lawbreakers” from “crime families” with “big money” and who threatened “our way of life, quality of life, and the health and safety of our children and grandchildren” . Just 2 days before the June 2016 ballot on the strict cannabis ordinances, state investigators responded to county reports that newly registered Hmong-American voters might be fraudulent or coerced by criminal actors and visited Hmong-American residences to investigate, accompanied by sheriff’s deputies . The voter fraud charges were later countered by a lawsuit alleging racially motivated voter intimidation; the suit was eventually dismissed for failing to meet the notoriously difficult criteria of racist intent. The raids may have discouraged some Hmong-Americans from voting, charges of fraud may have boosted anti-cannabis sentiment, and, one government official explained, “creative balloting” measures enabled some municipal voters in conservative localities to vote while others in more liberal places could not. The voter fraud charges, raids and legal contestation drew widespread media attention that further linked Hmong-Americans and cannabis. Amidst these now-overt racial tensions, the restrictive June 2016 ballot measure passed, allowing the Sheriff’s Office to gain full enforcement power over the “#1 public enemy to Siskiyou citizens … criminal marijuana cultivation” . Shortly after the June 2016 ballot measure affirmed stricter regulations, the Sheriff’s Office formed the Siskiyou Interagency Marijuana Investigation Team with the district attorney to “attack illegal marijuana grows” “mostly” around rural subdivisions . Within a month, SIMIT had issued 25 abatement notices and filed 20 criminal charges, in addition to confiscating numerous plants. Meanwhile, the Planning Division’s role had diminished — code enforcement officers were relegated to addressing violations not directly related to cannabis .

Historic water prices over the last 50 years for water deliveries from the Central Valley Project are listed in the 2000 Irrigation Water Rates Manual available at the library of the Bureau of Reclamation in Sacramento. Finally, the acreage of each district is derived with the help of geographic information systems of the irrigation district boundaries. Researchers also obtained observations on more than 15,000 groundwater wells in the Central Valley. Groundwater is a virtually unregulated resource and in many areas it provides a substitute for surface water in the event of a shortage. The depth of groundwater varies significantly, both spatially and temporally, between years and between months within a year. Researchers calculated the average well depth in the month of March, the beginning of the growing season, for each of the years 1990 to 1998 and then averaged the depths over these years. The groundwater depth at each farm location is derived as a weighted average of all well locations, where the weight is the inverse of the distance of each well to the farm to the power of 2.14—the exponent that minimizes the sum of prediction errors from cross‐validation. In the cross‐validation step each well is excluded from the data at a time and the depth is calculated using all remaining wells. There are several soil databases of potential interest to this analysis. In order of increasing detail, they are the: National Soil Geographic Database that relies on the National Resource Inventory , State Soil Geographic Database and Soil Survey Geographic Database . While SURGO is the most detailed soil database designed to allow erosion management of individual plots,there is no uniform reporting requirement for the United States. Furthermore, the observations in the June Agricultural Survey include all farms in the vicinity of a longitude/latitude pair, and hence, choosing field characteristics of one individual plot appears inappropriate. Instead,bato bucket the study uses the more aggregated soil database STATSGO, which groups similar soils into polygons for the entire United States.

Average soil qualities are given for each polygon. Although this soil database gives a first approximation of the actual average soil qualities, there might be significant heterogeneity, which is addressed in the empirical section. Finally, farmland close to urban areas has an inflated value compared to farmland elsewhere because of the option value of the land for urban development . Plantinga et al. examine the effects of potential land development on farmland prices and find that a large share of farmland value, more than 80% in major metropolitan areas, is attributable to the option to develop the land for urban uses. This study therefore constructed a variable to approximate population pressure by summing the population in each of the 7049 Census Tracts from the 2000 Census divided by the inverted square of the distance of the tract to the farm. Table 3‐1 displays the data’s summary statistics. This section presents the estimates for the hedonic regression with farmland value per acre as the dependent variable. The results are listed in Table 3‐2. The table uses feasible generalized least squares weights that account for the spatial correlation of the error terms.10Researchers conducted three spatial tests to test whether spatial correlation is indeed a problem. One test is the Moran‐I statistic . However, since this test does not have a clear alternative hypothesis, researchers supplemented it with two Lagrange‐Multiplier tests involving an alternative of spatial dependence: the LM‐ERR test of Burridge and LM‐EL test of Anselin et al. .The normal test statistic for the Moran‐I is 16.8, and the Lagrangian multiplier test are χ 2 ‐distributed with test statistics of 299 and 289, respectively. Therefore, all tests indicate that spatial correlation is indeed present. Hence the standard ordinary least squares estimate underestimates the true variance‐covariance matrix—OLS assumes all errors to be independent, even though they are in fact correlated. This suggests that standard OLS estimates of standard errors for hedonic regression equations generally might be misleading if the error terms among observations in close proximity are correlated. In fact, it is not uncommon in hedonic studies for variables to be statistically significant, yet to switch signs between alternative formulations of the model. Table 3‐2 therefore uses feasible GLS to construct the most efficient estimator by premultiplying the data by .

In the second stage, researchers estimated the model and use White’s heteroscedasticity consistent estimator to account for the heteroscedasticity of the error terms . The estimates in Table 3‐2 are based on observations with a farmland value below $20,000 per acre and water prices below $20. Including higher value observations in the analysis increases the R‐square of the regression, but the variable with the greatest explanatory power becomes population density. At the same time, the confidence levels for soil quality and water availability are reduced. Farmland with values above $20,000 per acre is generally close to urban areas, and the value of this land reflects what is happening in the urban land market, and the value of the future potential to develop this land for urban use—not what is going on in the local agricultural economy. Including these observations creates large outliers and results in estimates that are mainly driven by these outliers.Second, the research team excluded irrigation districts with expensive water prices from the analysis to get a better estimate of the net value of water. Only the net value of water, the difference between gross value and delivery cost capitalizes into farmland values. As an example, if the gross discounted value of an acre‐foot of water were $1000 and the annual delivery cost $50, the net value of the water would be zero . The researchers therefore test the sensitivity of the results to variations in water price by excluding irrigation districts with high prices from the analysis to get a better estimate of the net value of water. The coefficients on the climatic variables appear reasonable. The result for degree‐days implies that the quadratic form peaks at 1630 degree‐days. This is consistent with the agronomic literature, which indicates degree‐day requirements of this order of magnitude for several important crops grown in the Central Valley.While the coefficients are borderline significant under the feasible GLS model, the p‐value on the hypothesis that the linear and squared term on degree‐days is jointly equal to zero is 0.008, and degree‐days as a group are hence highly significant. One potential problem in the estimation using both the linear and squared variable is the high degree of colinearity between the two variables,dutch bucket hydroponic which will reduce the significance level of each individual variable. The correlation coefficient between degree‐days and degree‐ days squared is 0.98.

Another problem is that the variation in climatic variables with the Central Valley, the main growing region, is limited. In a related paper that examines the effect of degree‐ days on farmland values in the Eastern United States, the degree‐days variables are comparable in size and highly significant. Because many tree crops need cool nights, increasing temperatures substantially above the required degree‐days to grow a crop can only be harmful. The sign of the regression coefficient on water availability in Table 3‐2 makes intuitive sense: rights to subsidized surface water are beneficial. However, water rights have a price, as well as a quantity dimension. As mentioned before, only the net value of water capitalizes into farmland values. Therefore, the study tested the sensitivity of its results to variations in water price by excluding irrigation districts with high prices from the analysis, to get a better estimate of the net value of water. Restricting the sample to observations that have water rights with water prices less than $30, $40, and $50, and using no price restriction at all decreases the value of an acre‐foot from $809 in Table 3‐2 to $625, $583, $524, and $395, respectively, as the hedonic regression only picks up the net benefit of the water right. The linearity of the coefficient on water rights is confirmed when dummies for different ranges of water rights are included.14 The sample includes districts with zero private or federal water rights. These are districts that depend primarily on groundwater and state water. Since state water is very expensive, it is excluded from the estimation.15 Finally, a greater depth to groundwater is harmful, as it would result in larger pumping costs, but the coefficient of this variable is not significant. Soil variables have intuitive signs as well, and four of the five soil variables are significant at the 5% level. Higher values of the variable K‐factor indicate increasing erodibility of the top soil. Similarly, a higher clay content is also less desirable, as is low permeability, which indicates a soil that does not hold water. Finally, population density has a big influence on land prices: this variable is highly significant and of a large magnitude compared to the sample mean. The potential to sell agricultural land for urban development is often the most profitable option for farmers. The research team conducted several sensitivity checks, which are listed in Appendix 1. The results on water availability are remarkably robust, while the results for the variable degree‐ days are more sensitive to the particular implementation. However, the latter might be explained by the limited climatic variation in this project’s sample study. The team conducted a similar analysis for the Eastern United States with much larger variation in climatic variables, and find results that are again very robust and similar to the ones presented above. The coefficients on the climatic variables can now be used to calculate the impact of climate change on farmland values in California.

The impact of climate change on farmland values can be derived by evaluating the hedonic function both at the current climate and at a new predicted climate.First, note that a decrease in availability of federal and surface water would have a large and significant impact on the value of farmland. The coefficient on water availability is between $400–$850 per AF, depending on the price a district pays for water.Because researchers modeled surface water availability as additively separable from other exogenous variables, the impact is easily derived as the product of the value per AF and the decrease in water availability.As mentioned before, recent hydrological studies for moderate‐temperate climates utilizing a smaller geographic scale discovered that despite the increase in annual precipitation, the runoff during the main growing season , might actually decrease as a seasonality effect dominates the annual effect.The decrease in runoff translates into decreasing surface water availability, where the magnitude depends on the seniority of water rights. More senior water rights holders always get served first and are hence less prone to a decrease in water availability. For the same reason, junior rights holders will face potentially large reductions in availability. Given that the estimated value for cheap water is $809 per AF, a modest reduction of just 0.5 AF per acre will lower the value of the affected farmland by approximately $400 per acre. In this study’s degree‐day model, changes in temperatures have nonlinear effects on the resulting number of degree‐days. In fact, the study’s approach is conservative in the sense that temperatures above the upper threshold b2 = 32°C are assumed to have no impact on plant growth and 35°C are the same. The approach therefore assumes the marginal effect of further temperature increases to be zero, while some agronomic studies argue it should be negative.Table 3‐3 lists the average area‐weighted impact of a change in climatic conditions for three uniform temperature increases.The research team used the coefficient estimates from Table 3‐2 that corrects for the spatial correlation of the error terms.For comparison, the area‐weighted value of all observations in this study’s sample is $4,265. On average, the value of farmland in California would decrease by $482 per acre, or around 11%, under the hottest 3°C increase scenario. However, the distribution of impacts is quite different, ranging from large damages to modest benefits. Existing areas with a very hot climate—especially farms in the Imperial Valley—would face much larger relative decreases in value, while farmland around the Delta with its natural cooling mechanism would benefit slightly from an increase in temperatures, and hence degree‐days. Given the linear structure of the hedonic equation, the aggregate impact is simply a linear combination of the regression coefficients, and hence is itself normally distributed.

A mid-range projection forecasts up to 59 million residents statewide by 2050, with massive conversion of agricultural to urban land in the Central Valley, and cities such as Fresno doubling in population . Urbanization in California tends to consume lands with high quality soils and relatively abundant water supply due to their proximity to existing towns and cities in the valleys . Given such prospects of population growth, the purpose of this task was to develop future urbanization scenarios for Yolo County, and assess implications for agriculture, greenhouse gas emissions, and other issues related to land use change. Urbanization presents both opportunities and challenges for agriculture. In some regions, it does generate markets for agricultural products, such that farm production increases locally . But urbanization is more typically accompanied by challenges: the loss of agricultural land due to subdivision and development; vandalism at the urban edge ; and conflicts with new suburban residents about noise, odor, and potential spray drift associated with farming operations. If development takes place in a dispersed pattern that fragments agricultural land, farming may become difficult on some remaining agricultural parcels due to difficulties in moving farm machinery from field to field. Also, fragmentation and loss of farmland causes farmers to lose benefits associated with being part of a large farming community, such as sourcing inputs, accessing information, sharing equipment, and supporting processing and shipping operations . Impacts on agriculture from urbanization will then be disproportionate to the land area covered. Suburban or exurban development increases GHG emissions per land area substantially when compared with agricultural land uses .

It is useful to know the extent of these increases,blueberry packing boxes especially since California counties will need to demonstrate ongoing commitment towards reducing GHG emissions in response to state mandates, such as the Climate Action Plan that was adopted in 2011 for the unincorporated areas in Yolo County . In addition, land use planning for climate change can potentially set the stage for greater provision of other ecosystem services at the rural‐urban interface, such as regulation of environmental resources, biodiversity conservation, livelihood options, and business opportunities that build social capital . The A2 and B1 scenarios of the International Panel on Climate Change are based on story lines for higher and lower GHG emissions, respectively, which can be conceptually down scaled at local scales to explore how future local land use patterns will respond to climate change . A2 has higher economic and population growth, and less emphasis on environmental, social, and sustainability priorities than B1. The downs caled story lines can form the basis for spatial modeling of land use change and the challenges that would occur at the rural‐urban interface. In California, UPlan is a simple rule‐ based urban growth model used for regional or county level modeling . The spatial configuration of each land use type is based on demographics, land use designations of the General Plan , and on a set of attractors and detractors for land use change that can be informed by the story lines of climate change scenarios.The majority of California’s new residents will settle in urban areas in coastal counties and in the Central Valley. The Sacramento metropolitan region, where Yolo County is located, will house a significant portion of this growth. Projections prepared for the Sacramento Area Council of Governments Blueprint project in 2005 estimated a population increase from 1,948,700 persons in 2000 to 3,952,098 persons in 2050, i.e., >100 percent increase . The conversion of the region’s undeveloped land into urban, suburban, and exurban development often occurs at the expense of agriculturally productive land. 

Yolo County includes 653,452 acres according to the 2008 California Department of Conservation Farmland Mapping and Monitoring Program . Agricultural land occupied 538,043 acres in 2008. About 87 percent of the acreage was in agricultural use . Land use was classified as 4.6 percent urban in the incorporated cities of Davis, West Sacramento, Woodland, and Winters. Important farmland was 57 percent, and livestock grazing land was 24 percent of the county’s acreage. In 1998, Yolo County alone contained about 43 percent of the prime farmland that existed within the Sacramento region , and it yielded the highest farm market values out of all the counties . Thus Yolo County is an important reservoir of productive farmland within the Sacramento metropolitan region. A net loss of about 30,000 acres of agricultural land occurred between 1992 and 2008, and this includes a net gain of about 16,000 acres of grazing land . New grazing lands were formed by draining parts of the Yolo Bypass along the Sacramento River and by transitioning dry‐farmed grain fields to grassland, such as near the Dunnigan Hills. Overall, only 1 percent of Yolo County’s total prime farmland was lost up until 2000 . Between 1998 and 2008, the rate of agricultural conversion to wetlands, especially along the Sacramento River for wildlife conservation, has increased to approximately 2,000 acres yr‐1 . Urbanization accounted for the loss of about 6,500 acres of agricultural land between 1992 and 2008 , i.e., approximately 406 acres yr‐1 . Most of this was prime farmland and farmland of local importance. Yolo County has been relatively successful at protecting agricultural land from urban conversion through land preservation programs, incentives for farmers, and land use policies that make it difficult to develop land zoned for agriculture. Yolo County’s population grew an average of 2.2 percent per year from 1985 to 2007, from 120,300 to 197,530 residents . But by 2050 the county’s population may reach 320,000 to 394,000 , depending on assumptions used in scenarios for either regional or statewide planning. This would result in an increased urban population and pressures to expand the current urban footprint.

Given the county’s geography, urban expansion will almost certainly occur at the expense of farmland and open space if growth is not restricted to infill development within existing boundaries. With respect to California’s climate change policies aimed at reducing GHG emissions 16 and Senate Bill 375 17 which connects land use planning with implementation of AB 32, urbanization onto agricultural land raises two important issues for the 2050 time frame: magnitude of the loss of agriculturally productive land that provides ecosystem services such as meeting the food needs of an expanding state and global population, wildlife habitat, and open space for residents; and an increase in GHG emissions from decentralized urbanization when compared with more compact, centralized forms of urban development that leave agricultural lands undeveloped. There is a need to better understand the relationships between urbanization, agriculture, and climate change, and their interrelated effects on ecosystem services. In order to understand the type, extent, and likely locations of urbanization in the county, we used UPlan GIS‐based software, a rule‐based, land use allocation model developed by the Information Center for the Environment at the University of California, Davis . UPlan is an open‐source, relatively simple model that can be run on a sub‐county area, a county, or a group of counties. It is a suitable model for broad‐brush urbanization modeling of large land areas using multiple development scenarios, and has been used by more than 20 counties in California,package of blueberries including a group of rural Blueprint counties in the San Joaquin Valley . In the past it has been employed to assess the impacts of urbanization policies and growth on natural resources , to understand the risk of wildfires in rural woodlands from urban growth , and to evaluate the effect of land use policies on natural land conversion . Households are divided into four residential land‐use types based on density parameters, while employees are assigned to nonresidential land use types , also by density. New development is divided by land use type and allocated across the landscape based on the geographic cells with the highest combined attraction weights and the user‐defined land use order. The model uses a cell size of 50 meters, roughly about half an acre. The final output is a map displaying the location, by land use type, of future urbanization.For the purposes of this project, we modified UPlan in several ways when compared to previous usages. Since our time frame is longer than in many previous applications, we no longer required that the model place growth in areas conforming to the current county General Plan. Land use politics and regulation can change greatly over 40 years, which is equal to at least two General Plan cycles in most California counties. Furthermore, the County Board of Supervisors by majority vote can approve zoning variances four times a year, allowing development that does not conform to a current General Plan and zoning code. Thus, the planning documents and zoning codes that are a short‐term deterrent to development may no longer be relevant in the longer term. The purpose of this project was also to model three significantly different scenarios, and restricting development to the current policy framework would make this difficult.

For these reasons we did not include the countywide General Plan land use designations. We also modified UPlan to allow development within existing urban areas, on the assumption that a significant amount of urban redevelopment is likely within the 2010–2050 time frame. Lack of an infill development option was a significant drawback with previous versions of UPlan. Sharply increased levels of infill are likely within more environmentally oriented future scenarios. Indeed, our AB32+ scenario assumes that 100 percent of development takes place within existing urban areas. This approach is likely to rapidly decrease GHG emissions because lifestyles of urban area dwellers tend to have smaller carbon footprints, such as less energy expenditure for transportation , as long as their economic actions do not increase to the point of significantly outweighing that benefit . Urban development is already increasingly taking the form of infill within the state’s largest urban areas, including Los Angeles and the San Francisco Bay Area, and infill development is a leading goal of the Sacramento region’s 2004 Blueprint vision for the future . Lastly, we established density categories that are relatively high by historical California standards, but fairly close to the density levels of recent development in the more urban portions of the state. Our categories were “Very Low Density Residential,” with an average lot size of one acre; “Low Density Residential,” with an average density of 8 units per acre ; “Medium Density Residential”, with an average density of 20 units per acre; and “High Density Residential,” with an average of 50 units per acre. The latter two categories are similar to densities currently being achieved within many of California’s more urban communities . Within each scenario, we also apportioned development differently between these types. The A2 scenario focuses primarily on Low Density Residential development, while B1 is relatively evenly split between High, Medium, and Low Density types, and AB32+ favors High and Medium densities. In terms of building types, the Medium Density category might consist of two‐ to three‐story apartment or condominium buildings with significant green space around them, while the High Density category might include three‐ to five‐story buildings in a more urban format. It is important to emphasize that none of these categories require high‐rise apartment living, although this development type is not forbidden, and might in fact be desirable for limited locations within the county during the study period.Several types of urbanization attractors are typically used in UPlan, including blocks with growth in the previous census period , freeway ramps, arterial streets, collector streets , and urban spheres of influence. To predict infill development more accurately, we added additional attractors such as existing commercial strips, shopping centers, freeway retail zones , existing neighborhood centers, and rail transit stations. In most cases adding these factors meant creating new GIS data layers with information from publicly available sources or visual analysis of Google aerial imagery. We took into account existing land uses within cities by creating a data layer of current zoning districts, and consolidating these districts into high and low infill potential layers. The first of these includes existing commercial and industrial land, which typically consists of relatively large parcels of land being used for relatively short‐lived purposes , owned by landowners who are likely to be open to profitable redevelopment over a 40‐year time frame.

Our econometric models related acreages of each major crop to relative crop prices and key climate variables that are expressed as 10‐year moving averages to represent the recent memory of growers’ decision making. In general, the data indicate significant influences of prices and only moderate influences of expected growing degree days, chill hours and precipitation on acreage of individual crops. Overall the data indicate that Yolo County climate change has played a moderate role in the evolution of crop acreage in Yolo County in recent decades. The models did not investigate many other factors that affect Yolo County crop acreages, such as irrigation water effects of climate change outside Yolo County, extreme events, or the potential influence of statewide or global climate change on relative prices,. We applied the estimated parameter values to down scaled GFDL climate projections to assess how future climate change in Yolo County may affect crop acreage patterns from 2010 to 2050. The results should not be interpreted as acreage forecasts. For example, we took no account of recent trends or expected changes in prices, technology, or other factors in projecting acreage change. Instead, we invesitgated the acreage impacts of two paths for climate change , holding constant the relative prices and other relevant drivers of crop acreage. An underlying assumption in our approach was that the basic relationships between climate and acreage that were estimated using the data from 1950 to 2008 apply to projected climate effects on acreage from 2010 to 2050. Average temperature is projected to rise in Yolo County under both scenarios, associated with winter temperature increases and the reductions in winter chill hours. The two climate scenarios diverge for the period after 2035 with the A2 scenario cooler during this period,garden grow bags despite a long‐term increase in temperature compared to B1. Among field crops, warmer winter temperatures are projected to cause wheat acreage to decline and alfalfa acreage to rise. This led to a small projected decline in total field crop acreage and projected increase in tomato acreage.

The largest impact of warmer winter temperatures is for projected wheat acreage. Using the historical relationships, climate change induces a decline in projected wheat acreage share from about 17.5 percent of crop acreage in 2008 to as low as 4 percent of acreage in 2050. Even though the projected change was significant for the acreage of certain crops, the overall impact on total crop acreage has been moderate. Some care must be exercised in interpreting our results. Our projections focused exclusively on the using historical patterns to project relationships between acreage change and climate change. They are not year‐to‐year forecasts. Further, our projections were based on the statistical estimates derived solely from historic data, meaning that factors other than climate do not change from their historic values. In terms of adaptation to climate change, however, these results indicate that farmer decisions may now need to be based more on uncertainty of climate than in the past, which is not incorporated in our projection .In California, demand for water from agriculture, industry, urban areas and the environment has meant that most watersheds in the state are consistently over‐allocated . In the near term, projections suggest that by 2020 demand for water will exceed the available supply by >2.4 million acre‐feet in average rainfall years and up to 6.2 million acre‐feet in dry years . In the long term, climate change and population growth will place additional demands on the state’s water resources .While there is uncertainty regarding the extent to which climate will change in any given location, there is a growing consensus that the impacts on California’s water resources will be outside the range of past experience . Consequently, state agencies such as the Department of Water Resources and the California Energy Commission have urged water managers at the regional, district, and local levels to examine the potential impacts of and responses to climate change as a part of their planning efforts . Past climate and hydrologic records provide ample evidence that climate change is already having a measurable effect on California’s water supply . For instance, statewide weather records show that mean annual temperatures have increased by roughly 0.6– 1.0°C during the past century, with the largest increases seen in higher elevations .

This warming trend has contributed to a 10 percent decline in average spring snow pack in the Sierra Nevada over the same period, which equates to a loss of approximately 1.5 million acre‐feet of snow water storage . Global climate models suggest that this warming trend will accelerate, with temperatures expected to increase by 2 to 6°C by the end of this century . While there tends to be less agreement among the climate models as to whether mean annual precipitation in California will increase or decrease, inter‐annual variability is already on the rise and projected to increase further during the latter half of this century . Since the relationship between precipitation and surface runoff is non‐linear, a minor decrease or increase in precipitation could have disproportionate effects on the state’s water supply . Some of the water supply vulnerabilities for agriculture and other sectors can be mediated through traditional infrastructure improvements or alternative water policies; for instance by expanding water storage, updating levies and aqueducts, interstate transfers, modifying the existing operating rules, expanding conjunctive use or groundwater banking . Many of these supply side adaptations also have important trade offs, namely high capital costs and/or significant environmental impacts . Shifts in temperature and precipitation are also projected to have significant implications for the demand side of California’s water balance. Higher temperatures will increase the demand for water from agriculture, as well as the losses associated with water storage, delivery and irrigation. Since agriculture accounts for approximately 80 percent of California’s water use, methods to manage and minimize agricultural water demand are seen as an important way to adapt to climate change . Local conservation strategies implemented by water managers and agricultural users tend to also be more economical than developing new supplies . Demand management options may include water pricing and markets, allocation limits, improved water use efficiency, public and private incentives for irrigation technology adoption, reuse of tail‐water, shifting to less water‐intensive crops, and fallowing . The degree to which climate change will impact both water resources and agriculture is likely to vary considerably throughout California . Thus, for climate impact assessments to be useful they must be conducted at a scale which is fine enough for regional and local water managers to integrate research findings into their planning and adaption efforts. One tool that has helped water resource managers integrate climate change projections into their decision making process is the Water Evaluation And Planning system .

WEAP is a modeling platform that enables integrated assessment of a watershed’s climate, hydrology, land use, infrastructure, and water management priorities. In California, WEAP has been used to model the impact of various climate change, land‐use and adaptation scenarios on the Sacramento and San Joaquin River Basins . Likewise, Mehta et al. used WEAP to evaluate potential climate warming impacts on hydropower generation in the Sierra Nevada. Joyce et al. combined these regional models into a statewide WEAP application that is being used for integrated scenario analysis by the California Department of Water Resource. While these large‐scale hydro‐climatic models have proven useful for state and regional water managers,tomato grow bags their spatial resolution is often too coarse to be of immediate value to local irrigation districts. The WEAP framework has the potential to address this limitation by developing local applications that use more refined input data and greater spatial disaggregation. Models developed at the district scale would also provide an opportunity to improve communication between water managers and climate scientists, cultivate a better understanding of the risks and uncertainties, and ultimately enhance the community’s capacity to adapt . In this study we use WEAP to build a hydrologic model of the Cache Creek watershed and to assess the potential effects of climate change and adaptive management on the water resources dispensed by Yolo County Flood Control and Water Conservation District. This district was chosen for several reasons. First, most studies examining climate impacts on the state’s water resources have focused on watersheds fed by the Sierra Nevada, while those originating in the Coast Range have received little attention. Examining the Cache Creek watershed therefore provides an opportunity to investigate how watersheds that are not reliant on Sierra Nevada snowmelt may be affected by climate change. A second reason is that Yolo County is the site of an ongoing interdisciplinary case study on agricultural adaptation to climate change carried out by the University of California at Davis and the California Energy Commission. As such, the hydro‐climatic analysis is further informed by locally relevant agronomic and socioeconomic data. While several integrated water management plans have been formulated for the District over the past decade , our work adds value in several ways. Unlike past studies, we simulate the hydrology of the catchments in Lake County which form the headwaters of Cache Creek. Since this analysis is conducted at the district scale, we are also able to capture the explicit operating rules and legal decrees which govern local water management decisions. We then use down scaled climate projections from two IPCC emissions scenarios to simulate the District’s future water supply and projected demand under one baseline and three hypothetical adaptation scenarios.Between 1970 and 2008, total irrigated agricultural area in the county averaged 332,000 acres, varying between a maximum of 395,000 acres in 1980 and a low of 280,000 in 1982 . As indicated in the economics section above, there has been an overall downward trend in total agricultural area.

The county covers a portion of two geomorphic provinces: the Coastal Range and Central Valley. Surface water supply comes from a number of drainages: the eastern and northern parts of the county depend on the Sacramento River, Colusa Basin Drain, and Yolo Bypass, while the western part depends on Cache Creek . Most of the water in Putah Creek supplies neighboring Solano County. Agriculture accounts for almost 95 percent of the approximately 1 million acre feet of the county’s total water demand. About 70 percent of that water is estimated to be supplied by surface water; the remaining is pumped from groundwater . The Yolo County Flood Control and Water Conservation District service area covers 41 percent of the county’s irrigated area and is located in the western and central portion of the county . The District was established in 1951 and supplies surface water for irrigation from Cache Creek. The upstream reaches of the Cache Creek watershed are wetter and cooler than the valley floor. For example, average annual rainfall and temperature in areas upstream of Clear Lake are 988 millimeters and 13.3 °C respectively, compared to 560 mm of precipitation and 16.5 °C respectively in the valley. Snow does not occur in the watershed, except intermittently in high elevations. Upland soils to the west are well drained but shallow to bedrock composed of marine shales, silt stones, and sand stones. Lowland soils are part of alluvial fans, underlain by the Tehama formation . In the District, alfalfa, tomatoes, wheat, almonds, walnuts, wine grapes, and rice are the dominant crops.Two reservoirs located upstream in neighboring Lake County are critical for District water deliveries: Clear Lake and Indian Valley. The District purchased water rights from Lake County in 1967, amounting to a maximum of 150,000 acre feet annually. The actual amount available for District release in any given year is strictly controlled by the stipulations of the Solano Decree . In 1976, the Indian Valley reservoir was completed. Since it is owned and operated by the District, it allows greater flexibility in supplying water to its downstream customers. Water is delivered to customers via a network of canals and ditches downstream of Capay diversion dam. The District does not own or operate any groundwater wells for the purpose of meeting customer demands. However, many privately owned wells exist throughout the District, and landowners rely on these wells for domestic purposes and to add flexibility to their farming operations. The groundwater basin experienced some depletion of storage in the 1960s and early 1970s. The increased storage and provision of surface by Indian Valley Reservoir has been identified as a key factor in the recovery of groundwater levels in Yolo County in recent decades .

Although this model appears to be prevalent in the development of climate change adaptation content in Malawi as evident by the select few organizations that were referenced as content developers, it is not evident that this strategy leads to higher rates of adoption than more participatory approaches. In fact, several recent studies have affirmed that the innovation diffusion model used to disseminate new technologies to farmers does not necessarily lead to adoption in Malawi. Hermans et al. 2021 and Engler et al.2016 found that the adoption of climate smart agricultural practices in Malawi is a dynamic, multidimensional, and complex process. Additionally, this hierarchical process does not appear to allow for effective feedback from farmers who receive and interact with new technologies. My analysis also revealed that social network analysis is a useful tool to understand which extension providers in Malawi are central to the development of content and transfer of information and which organizations are on the edge or periphery of the network. The majority of organizations referenced in this study do not generate climate adaptation information, but are involved in the transfer of this information. It also appears as though clusters of organizations exist within the information sharing network. These clusters include government departments and select international and local NGOs, private sector partners involved in providing inputs to farmers, and religious-affiliated organizations. Social network analysis is a promising tool for evaluating the relationships and clusters present within extension networks in order to evaluate the strengths, weaknesses,plastic flower pots and power imbalances between organizations operating within a network. Future social network analyses should seek to incorporate an analysis of the ways in which hierarchies between organizations impact power imbalances as well as the transfer of information within an extension network. Within DLEC’s conceptual framework for analyzing Malawi’s extension system, governance structures, partnerships, linkages, and networks are recognized as crucial characteristics that impact the performance and effectiveness of EAS.

This study has reaffirmed the importance of strong relationships and ties among different types of organizations operating within Malawi’s extension network. This study has also revealed that these linkages are not only essential among high-level actors such as government departments and international NGOs, but also among farmers and farmer associations. In DLEC’s conceptual framework, the knowledge, behaviors, and adoption of agricultural technologies among farming households are seen as outcomes of Malawi’s EAS. Yet, strong relationships and networks formed by farmers may be just as important, if not more, in impacting the uptake of technologies. In order to strengthen DLEC’s EAS framework for Malawi, farmer networks should be included as a key component of the agricultural innovation system in addition to the existing components which include governance structures, organizational capacities, advisory methods, market engagement, livelihood strategies, and community engagement. I propose the following recommendations and areas of emphasis for future agricultural extension research to address climate change impacts in Malawi. First, there is a need for improved integration of organizations from lower governance levels in order to diversify the types of organizations operating in Malawi’s core extension network. Government representatives should also continue to facilitate platforms like the NACDC that involve diverse extension providers and allow for the vertical integration of information sharing among actors within different levels of government and farmers themselves. The increased diversification of organizations within the core network and facilitation of collaborative platforms will help to increase access to information, facilitate the transfer of knowledge, improve collaboration among extension providers, and increase the communication of consistent climate adaptation messages to farmers. In addition, extension providers should also focus on supporting farmers with specific and consistent agricultural technologies that will address climate change risks. The delivery of consistent climate adaptation practices such as conservation agriculture and good agriculture practices should be a top priority for extension providers. Future studies should also seek to analyze the efficacy of different advisory methods in disseminating information to farmers and rates of adoption of specific CSA practices. In terms of content development, increased engagement of farmers in the co-production of agricultural knowledge can help to facilitate greater adoption of climate adaptation practices.

Co-production processes allow for a participatory approach to content development through a combination of collaborative scientific review, dialogue, input from farmers, and joint decision making by researchers and participating farmers . Participatory research approaches can support collaborative farmer learning and innovative problem-solving. Participatory methods also value the institutional knowledge of local farming communities and can help to better understand the social interactions at play that influence the information available to farmers. This approach can be used to collaboratively develop agricultural improvements that allow farmers to effectively adapt to climate change. Additionally, women’s contributions to Malawi’s agriculture sector are vitally important to the success of the industry and the ability of farmers to adapt to climate change. Therefore, future studies should also incorporate an analysis of the gendered nature of EAS delivery and the role of women farmers in the co-production of agricultural content. Finally, organizations should continue to address resource challenges by providing tailored trainings for their staff and leveraging partnerships within the extension network to fill gaps in staffing capacity. New partnerships with donors and within the private sector could also help to increase funding for the delivery of EAS in Malawi. This research has several limitations that readers should be aware of as they interpret study findings and conclusions. First, due to the qualitative nature of this research and limited number of study participants, findings cannot be generalized the full population of extension providers operating in Malawi. This study included 19 participants who consented to participate in virtual interviews and is therefore not representative of all individuals or organizations providing EAS in Malawi. Once travel is permitted, this study should be replicated with in-person interviews with extension providers operating in Malawi and farmers that receive EAS. Second, due to travel restrictions imposed from the Covid-19 pandemic, in-person travel to Malawi was not possible during this research process. Due to the virtual nature of these interviews, only participants with access to internet were able to participate. A third limitation was the study protocol and questionnaire I developed.

Although I prompted participants to elaborate on their answers, the responses shared by participants were framed by my questionnaire. I strove to maintain an unbiased perspective of the responses provided by participants and the analysis of data by receiving input from local partners in Malawi. However, this study reflects my Western worldviews and positionality as a 27-year-old, Caucasian woman from the United States. A final limitation was the lack of scholarly research on social network analysis and climate change adaptation content development and dissemination in Malawi. This knowledge gap limited the my ability to draw comparisons between other researcher’s findings and form recommendations. Net tropical forest loss of 7 million hectares per year occurred between 2000 and 2010, with conversion to agriculture accounting for 86% of deforestation . Annual deforestation in tropical Asia during the 1990s reached up to 5.6 million ha yr−1 ,plastic garden container resulting in the emission of 1.0 Pg C yr−1 to the atmosphere . In Indonesia, the total forest area of 117 million ha in 1990 dropped to 89 million ha in 2011–2012 with primary, secondary and plantation forests occupying 45.2, 40.8 and 3.0 million ha, respectively . The average forest loss of 1.3 million ha yr−1 from 1990 to 2012 resulted from burning and conversion to agriculture, mining and infrastructure with Indonesia contributing to ∼10% of total global forest loss each year. Short-term changes in soil properties following conversion of tropical forests to agricultural land use are often pronounced and in most cases detrimental to sustainable agricultural production. In contrast to the Amazon rainforests supported by Oxisols and Ultisols , Indonesia’s rainforests are largely supported by volcanic soils, primarily Andisols. These Andisols support high agricultural productivity with some of the world’s highest human-carrying capacity being found on volcanic soils in Indonesia . With respect to greenhouse gases, Andisols are notable for having the highest soil carbon storage capacity among the mineral soil orders in temperate and tropical climatic regimes with an average carbon stock of 25.4 kg C m−2 . Matus et al reviewed soil carbon storage and stabilisation in andic soils and concluded that the most important mechanism of sorption of soil organic matter by short range ordered amorphous minerals is the ligand exchange. While short-term changes in properties of tropical rainforest soils have been extensively studied, there is a paucity of information concerning long-term changes in soil properties resulting from changing land use and management practices, especially with respect to Andisols. Greenhouse gas emissions from agriculture are reported to contribute up to 30% of anthropogenic emissions . Soils can be a major source or sink of GHG from terrestrial ecosystems depending on the ecosystem disturbance regime and soil management practices. Soil carbon storage is dependent on soil mineral constituents, with volcanic ash soil stypically having exceptionally high potential C stocks owing to their high content of active Al and Fe constituents . In Andisols, Chevallier et al. showed organic matter transformation to CO2 via microbial respiration was lower as allophane content increased. In addition, changes in land use/land cover alter organic matter quantity and quality, which are major factors controlling soil microbial biomass and activity . Given the high C stocks in Andisols, it is important to assess the fate of soil C following land-use conversion from forest to intensive agricultural production, especially with regard to rapid deforestation in the tropics.

Andisols have several unique properties that affect agricultural productivity, such as high P fixation, high organic matter concentrations, a clay-size fraction dominated by pH dependent variable charge, low bulk density, high porosity, high water retention capacity and high mesopore content . In particular, high P retention in Andisols can limit agricultural productivity by limiting plant availability of P. Currently, there is little information on how P retention and availability in tropical Andisols change with different land use and agricultural practices. Nitrate leaching characteristics in Andisols are also strongly affected by variable charged constituents as positive charges can retain nitrate enabling higher plant utilization efficiency. In southern Chile, Huygens et al. reported NH4 + and NO3 − retention of 84 and 69% of N fertilizer additions, respectively, after one year based on 15N pool-dilution and 15N tracer studies of forested Andisols. In Japan, the maximum nitrate adsorption by Andisols ranged from 0.4 to 7.0 cmolc kg−1 with the highest values occurring in soil horizons with high allophane content and low organic carbon content . Furthermore, Deng et al. evaluated the denitrification rates from eight Andisols under three different cropping systems in an intensive livestock catchment of central Japan and reported that N loss via denitrification from upland fields was almost negligible in spite of substantial N inputs . In addition to retention of NO3 − by positively charged colloids, a laboratory study by Matus et al. reported high retention of NO3 − in Andisols through transformation of NO3 − to dissolved organic nitrogen . In Indonesia, land use/land cover of Andisols is primarily native rainforest, tea plantation, horticultural crops, terraced paddy fields and other food crops. Land-use conversion from tropical rainforest to agriculture has taken place over long periods of time ; however, no rigorous studies have examined changes to Andisol soil properties over these time periods. In addition, several studies have examined microbial biomass carbon and CO2 measurements in topsoil horizons, however, MBC and CO2 measurements in subsoil horizons have been ignored although these measurements are crucial for explaining the exceptionally high carbon stocks in Andisols. Given the several unique properties of Andisols, it may be expected that these soils are more resilient to land-use change and agricultural management practices. Therefore, we hypothesize that the unique soil properties of Andisols lessen the negative impacts of land-use change from tropical forest to agriculture on soil physical, chemical and biological properties. The objective of this study was to take advantage of long term, land-use/land management changes to examine changes in several physical, chemical and biological properties of Andisols in tropical Indonesia following conversion of rainforest to tea plantation and horticultural crops.Samples were pretreated with H2O2 to remove organic matter and dispersed with dilute Na-hexametaphosphate. Silt- and clay-sized fractions were measured after sedimentation according to Stokes law.

The increase in variability of rainfall patterns, sudden and severe floods, prolonged droughts, and changing temperatures severely impact Malawi’s ability to grow food. Since 1960, the mean annual temperature has increased by 0.9 ° C . Recent studies conducted in Malawi show alarming evidence of rapidly warming temperatures with projected temperature increases of between 1.9 to 2.5 °C by 2055 . See Figure 1 from Sova et al., 2018 for a country-wide analysis of projected temperature and precipitation changes by 2050. Climate change poses addition challenges to maize production in Malawi. Researchers have discovered that warming temperatures in Malawi could lead to reduced planting seasons and significantly reduced maize yields . Researchers also find that yields decline for all major maize cultivars in Malawi using combined temperature and precipitation projections . GoM’s National Climate Change Management Policy identifies increased adoption of climate smart agricultural practices as a critical need for farmers in Malawi. According to the FAO, Climate Smart Agriculture practices encompass an approach to agriculture that helps to guide actions needed to transform agricultural systems to support sustainable food production given changing climatic conditions . CSA aims to sustainably increasing agricultural productivity, support adaption to climate change, and reduce greenhouse gas emissions through improved agricultural practices .CSA focuses on the implementation of sustainable interventions including improved soil management, soil and water conservation practices, development of resilient crop varieties, and agroforestry practices. Improving soil management includes practices such as conservation agriculture , soil fertility management, and diversifying farming systems to include multiple crops in order to reduce soil erosion and retain nutrients in the soil . CA encompasses farming practices including minimal soil disturbance through low- or no-tillage planting, maintenance of carbon-rich organic matter to cover and feed soils, and crop rotations . Soil- and water-conservation practices for agriculture emphasize crop residue management, mulching, terracing, rainwater harvesting,french flower bucket and efficient irrigation management . Planting resilient crop and early maturing varieties also supports the development of high-yielding, heat, drought, and pest-resistant crops.

Agroforestry is another CSA practice that involves integrating trees or shrubs into agricultural production systems. Agroforestry includes practices to improve fallows, grow crops alongside forest plantations, establish home gardens, grow multipurpose trees or shrubs, and integrate trees into animal pastures . The use of trees in agricultural systems reduces vulnerability to extreme weather events by improving soil fertility and moisture content, reducing erosion and diversifying production for farmers in case of crop failure . According to the FAO, the CSA interventions described above allow farmers to sustainably increase yields while adapting to impacts of climate change on agricultural systems . Adopting CSA practices is critical for developing effective climate change responses and continuing to support sustainable food production in Malawi. Extension and Advisory Services support rural development, improve food security, and enhance agricultural production systems across the world. Birner and colleagues p. 342, define EAS as, “the entire set of organizations that support and facilitate people engaged in agricultural production to solve problems and to obtain information, skills, and technologies to improve their livelihoods and well-being.” These services operate within a larger system of agricultural knowledge and information systems with actors that generate and share knowledge about agricultural technologies with farmers and information generators. Rivera and colleagues have categorized the key actors within agricultural knowledge systems into three types: education, research, and extension .At the center of the Agricultural Knowledge System are the farmers that act as the key clients of agricultural innovations, but also share information with extension institutions, research organizations, and agricultural educators as they field test new technologies and develop new agricultural innovations themselves. According to Lubell and colleagues p.1093, “agricultural extension enhances adaptive capacity when it manages knowledge systems in ways that help farmers react to changes in economic, social, and environmental processes.”

These knowledge systems are strengthened when actors collaborate to develop and deliver relevant information in order to enhance resilience and support sustainable livelihoods for farmers. There are numerous approaches or methods utilized by actors within agricultural knowledge systems to support farmers with EAS. The advisory methods utilized by extension providers vary depending on actor’s paradigms, goals, and resources. EAS providers use a variety of extension methods including the model village approach, demonstrations, field days, lead farmers, farmer field schools, mass media and participatory farmer research. Village meetings or the model village approach, are commonly used to create awareness about important agricultural issues, obtain approval from village leadership for proposed projects, and mobilize farmers to participate in new initiatives . Through this approach, community leaders work with extension agents, catalyze community buy-in for new projects, prioritize actions with local leaders, learn about key issues in the community, and design tailored extension plans to improve community management structures . Demonstrations are widely used by extension officers to disseminate information on new agricultural technologies to farmers. Demonstrations are conducted at research stations, training centers, and on farmer’s fields. This method is used by the public sector, NGOs, and the private sector to promote new seeds or agricultural inputs to farmers. Demonstrations show farmers how to implement a technology and the result of that technology on local crop systems . Field days are also used as an advisory method and are coordinated amongst extension workers and farmers to promote a meaningful learning opportunity between organizations, extension staff, and farmers. These field days allow extension and subject matter experts to receive feedback on new technologies and agricultural practices.

Field days may also, “attract a wide range of stakeholders who include input suppliers, donors, policymakers project staff civil society, and extension service providers” . Within this approach, participatory farmer research programs allow farmers to co-develop new technologies and innovations with researchers in order to increase adoption of those technologies. The Lead Farmer, or Farmer-to-Farmer approach, is used to help disseminate information and new technologies from fellow farmers who have adopted certain practices or gained new information. Lead Farmers have been shown to substantially increase rates of technology adoption, increase the number of farmers receiving extension services, and reduce the cost of extension services for farmers because they are often viewed as trustworthy and credible sources of information within a community. Farmer Field Schools are another method used to educate farmers and typically include groups of 20 – 25 farmers who meet regularly to discuss, modify, and experiment with new production practices. During Farmer Field Schools, farmers receive training from experienced facilitators. This method allows farmers to observe and test their own ideas while building agricultural content and skills. Finally, mass media or Information Communications Technology platforms are widely used to provide information to farmers. ICT platforms including radio are widely used by governments and NGOs to disseminate information to large groups of farmers.Agricultural extension and advisory services in Malawi date to 1903 when GoM began advising farmers on improved methods of cotton to be exported to Britain. In 1949, a severe drought led to widespread famine across Malawi. This disaster resulted in the development of a more centralized approach to advisory services by the government. Then in 1964, the Department of Agricultural Extension and Training was established to provide comprehensive training, agriculture, husbandry, home economics, irrigation, and credit services to farmers. The DAET was eventually separated into various departments and nongovernmental organizations became increasingly important dual extension providers. The Department of Agricultural Extension and Training eventually evolved to become the Department of Agricultural Extension Services and operates as one of the six departments within the Ministry of Agriculture, Irrigation, Water, and Development . DAES is the main provider of extension services to farmers throughout Malawi and coordinates activities with district-level government partners. Additional departments under MoAIWD that support the dissemination of extension information in Malawi include Animal Health, Crop Production, Fisheries, Irrigation, and Land Resources and Conservation Departments. The six departments within MoAIWD including DAES are represented by eight Agricultural Development Divisions . These ADDs are further divided into twenty-eight District Agriculture Development Offices , one-hundred and eighty-seven Extension Planning Areas under the DADOs, and finally Sections which each comprise 5-15 villages and represent the smallest administrative unit . Staff at the EPA level are called Extension Agents and are tasked with, “conveying technical messages to farmers, forming farmer groups to carry out farmer demonstrations,bucket flower and linking farmers to credit institutions” . Technical experts from Malawi’s research institutions including Lilongwe University of Agriculture and Natural Resources , the University of Malawi, Mzuzu University, and Malawi University of Science and Technology also support the development of new technologies and outreach messages to improve Malawi’s public extension system .

In its effort to decentralize Malawi, in 2000, GoM and DAES introduced a new agricultural extension policy. This new policy termed, “Agricultural Extension in the New Millennium: Towards Pluralistic and Demand-Driven Services in Malawi” promotes a pluralistic extension system that allows for the delivery of specialized services to farmers through multiple extension providers. This policy was introduced to allow for the participation of other extension providers apart from the government to more effectively respond to environmental, social, economic challenges impacting the development of the agriculture sector in Malawi . Through this policy, NGOs, farmer groups, and private industry could operate extension services to farmers throughout the country to complement government extension activities. In Malawi, agricultural extension providers also include non-profit organizations, farmer groups, and private companies. Dozens of local and international nongovernmental organizations provide extension services throughout Malawi and many are members of the Civil Society Agricultural Network . CISANET has a membership of over one-hundred organizations and provides policy advocacy support in programmatic areas including, “climate smart agriculture, markets and international trade, livestock and dairy development, governmental budget accountability, and nutrition and social protection” . NGOs providing extension services operate across Malawi and often utilize government extension staff to implement their program activities at the local level. The majority of NGO activities are, “funded by external donors through implementation contracts with predetermined targets and centralized control. The relatively small size of NGO efforts and the drive to differentiate themselves technically and operationally from other EAS service providers competing for the same contracts lead to an operational context characterized by a large number of actors employing variations of the same approaches and technical themes, all attempting to work with the DAES to achieve impact” . There are also several large donor-funded projects operating within the context of Malawi’s extension system. For example, the United States Agency for International Development has funded many multi-year, multi-million dollar projects such as Strengthening Agriculture and Nutrition Extension Services Activity implemented by the University of Illinois and United in Building and several projects implemented by Catholic Relief Services .The Farmers Union of Malawi is the main umbrella organization representing farmer interests and includes 93 member organizations that represent an estimated 350,000 smallholder farmers . In addition, the National Smallholder Farmers’ Association of Malawi is a member-owned association of 108,000 farmers organized into approximately 43 farmer associations across Malawi. Private sector extension providers include actors supporting the production of agricultural commodities as well as, “agricultural input companies , and agricultural input retailers” . Private sector extension providers have been categorized by Simpson and colleagues as utilizing either push or pull business models. Push business models are utilized by agricultural input supplies and, “focus on the provision of additional value-added advisory services, such as advising related to consumers’ input purchasing decisions” . In contrast, pull business models are utilized by companies focusing on the production of agricultural commodities such as maize and often provide extension services to farmers in exchange for purchasing the commodities that the farmer grows. These extension services support the adoption of new technologies, practices, knowledge, and information that can help farmers overcome barriers to increasing crop yields, adapting to changing climatic conditions, and ensuring sustainable livelihoods of farmers. According to GoM, challenges remain in implementing effective services for maize farmers. These include a lack of coordination and communication amongst extension providers, conflicting messages disseminated to farmers by various stakeholders, and inadequate opportunities and support for engagement among stakeholders . Inconsistent recommendations provided by the extension system, particularly regarding climatic viability and best practices for the sustainable intensification of agriculture have remained significant challenges in Malawi.

The sample was stored in a 2 mL chromatography vial at 4 °C. For the quality control, all the experimental steps were carried out with the blank sample, the final sample solution was also stored in the 2 mL vial for further analysis.For the microplastics ranging 10–500 μm, the solution containing microplastics was ultrasonicated for 10–20 min. 20 μL of the sample was dropped on a glass slide each time until all the liquid was transferred. After the ethanol was evaporated, the slide was analyzed by the automated LDIR Imaging system . The automated particle analysis protocol within the Agilent Clarity software was used for all analysis. In the selected test area, the software used a fixed wave number at 1800 cm−1 to quickly scan the selected area and identified the particles . The software automatically selected a non–particle area as the background, collected the background spectrum, and performed morphological identification and infrared full spectrum acquisition on the identified particles. Sensitivity was set to the maximum. After obtaining the particle spectrum, the software automatically made a qualitative analysis with the standard spectra in the self-established database of Agilent. The setup was tested with standard PE pellets , and the hit quality index was >90 %. Considering the aging of MPs in environmental samples, hit quality was set to 65 % for identifying polymer compositions. Additionally, the information including the picture, size, and area of each particle was displayed in the quantitative results. For the 500 μm–5 mm microplastics, the suspected microplastic particles were selected under a stereoscope . ATR–FTIR was used to further identify the polymer composition. The spectrum range was 400–4000 cm−1 with a spectral resolution of 4 cm−1 ; 24 scans were performed. The spectra were compared to the standard spectra in the siMPle database . The polymer type, size, and shape were recorded by the software.Due to the limitation of Agilent 8700 LDIR imaging, that is, the thickness of MPs could not be detected, and fragments were classified as films. As shown in Fig. 5,procona florida container the abundance of microplastics with different shapes was film ≫pellet > fiber , with film accounting for 88.2 %, pellet accounting for 9.0 %, and fiber accounting for 2.8 %.

However, all the detected particles were films in the previous visual results in similar cotton fields , which meant that the detection method could affect the findings of MPs shapes. As shown in Fig. 6, PVC, PP, PE, and PA accounted for a relatively high proportion of the three shapes in all the soil samples. For instance, the proportions of PVC were 37.7 %, 25.3 %, and 13.8 %, respectively in fibrous, film, and pellet microplastics in the soil with 5-year mulching. PTFE also accounted for a relatively high proportion in the fibrous form in the soil with mulching years of 10 and >30 years. For all three shape categories of microplastics, the compositions of polymer types were greatly distinct. For example, in all the soil samples, the proportion of PA in the pellet was higher than that in the fiber and film, while the proportion of PP in the fiber was slightly higher than that in the film and pellet. In the soil with 20 years of mulching, the proportion of PVC in the pellet was more than those in the other shapes, while the proportion of PVC in other samples was fiber > film > pellet. The proportion of PTFE in the film was slightly higher than that in the fiber and the pellet. No clear pattern was observed for the rest of the polymer types.As is shown in Fig. 2, the exponential increase of microplastic abundances with the decrease of their sizes was observed, which is consistent with other studies . This may be caused by the further fragmentation of microplastics over time. Since microplastics in small sizes account for the vast majority, the detection limits of different quantification methods can significantly influence the findings of microplastics. To further understand the ranges of microplastic contaminations in agricultural soils, we performed literature research with respect to microplastic detection in farmlands . The highest abundance in previous studies was 320–12,560 particles/kg soil , accounting for <1 % of this study. The abundance of microplastics in this study was 100–106 times higher than that in other regions. In addition to the different regions of sampling, the quantitative method also greatly impacts the results. For example, visual identification under stereoscope which is most commonly used in soil microplastics studies can cause high false-positive circumstances when it comes to small sizes .

It is generally believed that one can correctly identify microplastics only for particles above 100 μm , and the false detection rates grow with the size decrease. Although the FTIR, Raman spectroscopy, or heating method has been used to assist the microplastic identification, most studies did this process after visual detection, which may still ignore the particles with small sizes. We have previously conducted a microplastic quantitative study with the visually microscopical method in the same place . The result showed that the abundances of microplastics were 80.3 ± 49.3, 308 ± 138.1, 1075.6 ± 346.8 particles/kg soil, respectively, in the cotton fields with 5, 15, and 24 years of film mulching, and all particles were PE identified by FTIR. In the current study, different methods were used to quantify the microplastics in the soils located in the sameregion, planted with the same crop, and mulched with a similar period. A total of 26 polymer types of microplastics were detected, and the abundance was approximately 103 times higher than those reported in our previous study. Therefore, with a different detection method, our finding suggested that the previous quantitative studies of soil microplastics may seriously underestimate the abundances and types of soil microplastics. Previous studies showed that the PE film mulching was a source of microplastics in farmland . The current study also observed that almost all microplastics with the size of 500 to 5,000 μm were PE film residual microplastics , which confirmed that mulching film was an important source of microplastics in agricultural soils. In the sampling region, where the sunshine is intense and the temperature difference between day and night is large, the plastic film was more susceptible to the harsh environmental conditions, become brittle, and fragmented into microplastics. The abundance of PE MPs ranging from 10 to 500 μm was about 100 times as much as that of PE MPs ranging from 500 μm −5 mm . The abundance of PE microplastics in the soil with film mulching for >30 years was significantly higher than that in the fields with less film mulching time, suggesting that the residual microplastics from the film may continuously accumulate in the soil. However, there was no significant increase of PE films in the smaller size than in the larger size in all samples.

This may be due to the dynamic equilibrium of MPs fragmentation as well as the detection limit. New films are applied every year thus MPs with relatively large sizes continuously enter the fields, and meanwhile, MPs constantly break into smaller pieces. Due to the detection limit of LDIR, MPs smaller than 10 μm are undetectable. If MPs’ detection technology breaks through the limitation of detection limit one day, the increase of PE films in smaller sizes may be observed. Considering that plastic film plays an irreplaceable role in agricultural production, future development of biodegradable film material would be essential. However, the polymer types of microplastics in 10–500 μm showed a significant difference from larger sizes , which suggested that microplastics with smaller sizes had other dominant sources. For example, irrigation was believed to be an important source of microplastics in farmlands , and may explain the high proportions of PP and PVC in this study. PP is one of the plastic types with the highest yield and consumption in the world , which has been widely used in daily life, such as small appliances, toys, plastic bags, clothing, water supply, and heating systems. Therefore, previous studies have observed PP microplastics in the wastewater treatment plants. For instance, Wang et al. investigated the microplastics in the influents and effluents from approximately 25 wastewater treatment plants and reported that PP, PE, and PS made up almost 83 % of the total microplastics. In this study, the irrigation water was from the Moguhu reservoir, the confluence of the effluents of several sewage wastewater treatment plants. Even though we did not investigate the microplastics in this reservoir, considering the wide application and frequent detection, we may conclude that the PP microplastics detected in the cotton fields were from the irrigation water. Parallelly,procona London container all the buried pipelines in the drip irrigation system were PVC plastic. The small particles falling off from the drip system may contribute to the PVC microplastics in the soils. This study indicated that the microplastics in soil were mainly distributed on the size of 10–50 μm, which could not be detected by visual counting methods. However, many studies have shown that fine-grained microplastics have a more serious negative impact on soil ecosystems . To establish the ecological baseline of microplastics, it is essential to establish a more precise standard detection method, and simultaneously study the environmental impact of microplastics with different particle sizes.The agricultural sectors of the United States and other developed countries have been subjected to a myriad of policies and regulations that have contributed to unsatisfactory production patterns and resource allocations both within and between countries. Furthermore, such policies have imposed heavy financial burdens on governments that have transferred substantial resources to support the farm sector. The General Agreement on Tariffs and Trade strives to improve the efficiency of agricultural trade and production patterns globally. It is proposed that GATT will reduce the set of permissible agricultural policy instruments, thereby eliminating some policy options that have contributed to several of the undesired consequences in the past. Used correctly, the feasible set of policies is believed to allow for a gradual down scaling of agriculture’s excess supply and to make the sector more flexible and progressive. Ultimately, once the restricted set of policies is introduced, it is expected that a sustainable growth path will be achieved.

A framework for assessment and setting of agricultural policy instruments is introduced in this paper It is used to investigate the impacts of some of the instruments considered for the policy reform following GATT; to analyze operational principles that allow effective implementation of these policies; and to consider issues of eligibility criteria, monitoring, and enforcement. This framework is derived from a political economic perspective on the characteristics of agriculture in developed countries, the causes for past policy interventions in agriculture and their shortcomings, and the ingredient for effective design and implementation of policy reform. This perspective is based mostly on the findings of research on political economics and is presented in the next two sections. It is followed by an analysis of the objective of the agricultural policy form , J model of setting specific policy instruments, and criteria for their analysis. These will be used in the last two sections to analyze a subset of proposed policy instruments and to address dynamic adjustment and implementation aspects of the policy reform. New innovations and practices have been introduced almost continuously. They have altered market conditions and have led changes in the structure of agriculture. Both public and private research contribute to this technological evolution. Hayami and Ruttan have demonstrated that economic conditions induce innovations, and the direction and nature of new technologies are affected by resource scarcities, relative prices, and regulations. The importance of economic incentives and conditions in affecting the evolution of agricultural technology in the United States is emphasized in Cochrane’s book. He argues that labor scarcity was the main problem of U.S. agriculture during the 19th century and that the major innovations during this period were mostly laborsaving devices StIch as reapers, thrashers, combines, and steel plows. These innovations allowed for fast expansion of the land base with relatively small numbers of settlers. While the yields per year of did not change much during the 19th century, U.S. output grew substantially as acreage increased.

Compared to unauthorized workers, citizens eam 14 percent higher wages, legal permanent residents earn 9 percent more, and anmesty workers eam 7 percent more. Season has no statistically significant effect on wages.Amnesty workers are the group with the strongest attachment to U. S. farm work, followed by LPRs, citizens, and unauthorized workers in descending order. This result underscores the importance of amnesty workers to U. S. agriculture. Not only are they the largest legal status group in the farm worker population, but they devote more time to farm work than any other legal status group. Despite their devotion to farm work, amnesty workers do not earn the highest wages among legal-status groups. Agricultural wages rise as their legal status becomes more permanent. Citizens earn the highest wages at $6.02, followed by LPRs at $5.74, amnesty workers at $5.66, and unauthorized workers at $5.27. The greatest gender differences concern the probabilities of unemployment and that of staying abroad. Where female workers experience a 32 percent probability of unemployment, comparable male workers only have a 13 percent chance of unemployment. Women only have a 17 percent chance of staying abroad, whereas men have a 30 percent chance of doing so. Women have a 48 percent probability of working on a farm compared to men at 52 percent. Men are not statistically significantly more likely to do non-farm work than women. Women’s wages are not statistically significantly different from men’s. Workers who live with their spouses have the lowest probability of staying abroad at 18 percent. Those who are not married have the second lowest probability of doing so at 25 percent. Workers with spouses are the most likely to stay abroad at 30 percent, presumably because some of the married workers leave their spouses in their horne countries.Workers who live with their spouses spend 57 percent of their time in farm work while unmarried workers spend 54 percent of their time in farm work. Married workers in general spend 52 peroent of their time in farm work. Workers who live with their spouses are also the most likely to experience unemployment at 17 percent Unmarried workers are next at 15 percent.

Married workers in general are the least likely to experience unemployment at 13 percent. Family household composition has no statistically significant effect on the probability of doing non-farm work. Unmarried workers eam the highest wages at $6.23,plastic planter pot while workers who live with their spouses and married workers in general eam $6.05 and $5.66, respectively. The effects of farm work experience on various probabilities are the greatest during the first 10 years. During this period, the typical worker’s probability of doing farm work increases from 30 percent to 56 percent, while the probability of staying abroad plummets from 53 percent to 26 percent. The probability of doing non-farm work also drops from 9 percent to 4 percent in this period. During the second 10 years, the probability of farm work continues to climb, but at a much slower pace, from 56 percent to 67 percent. The drop in the probability of staying abroad also continues at a slower rate from 26 percent to 16 percent. The probability of non-farm work declines from 4 percent to 2 percent in the second 10 years. After the first 20 years, farm work experience has almost no effect on any of the probabilities. Farm work experience has no statistically significant effect on the probability of unemployment. There seem to be at least two reasons for farm workers’ demonstrated ability to rapidly increase the probability of farm work in the first 10 years of their careers. First, additional experience during the first few years is likely 10 raise productivity, which makes workers more desirable to employers. Second, during the first few years of their U. S. farm experience, farm workers gain knowledge of the job market and develop contacts. Tlms, farm workers with more experience are better equipped to find additional agricultural jobs. Farm work experience raises wages for the first 25 years. Workers with no experience earo only $5.06 while those with 25 years of experience eam $6.05, a wage gain of almost 20 percent Among the three work history variables in the wage equation, only the probability of unemployment has a statistically significant effect on current agricultural wages. We increase the probability of unemployment from 0 percent to 100 percent in increments of 20, and evaluate what happens to current agricultural wages. We assume that workers perform farm work when they are not unemployed.

The probability of unemployment and current agricultural wages have an almost linear negative relationship. As the probability of unemployment drops from 100 to 80 percent, wages rise from $5.07 to $5.21 – a 15i per hour or 2.76 percent increase. The next 20 percent dec1ine in unemployment brings an additional 15i per hour rise in wages. Thereafter, each 20 percent reduction in unemployment results in a 16i increase in wages. To take an extreme example, a typical worker who spent the previous two years in farm work eams 15 percent more in wages than a worker who was unemployed the entire two years with otherwise identical characteristics.Plants have evolved complex cell type-specific regulatory processes to respond and adapt to dynamic environments. In certain cell types, such processes allow the formation of constitutive and inducible apoplastic diffusion barriers that regulate mineral, nutrient and water transport, pathogen entry, and have the capacity to alleviate water-deficit stress . The Arabidopsis thaliana root endodermis contains both lignified and suberized diffusion barriers, of which the latter is extremely responsive to nutrient deficiency . Many of the molecular players associated with suberin biosynthesis and the transcriptional regulation of this biosynthetic process have been elucidated using the Arabidopsis root endodermis as a model. Suberin is a complex hydrophobic biopolymer, composed of phenylpropanoid-derived aromatic and aliphatic constituents, which is deposited between the primary cell wall and the plasma membrane as a lamellar structure . While the order of the enzymatic reactions that produce suberin is not entirely understood, many of the enzymes associated with suberin biosynthesis have been identified to function in the Arabidopsis root endodermis. Many of the suberin biosynthetic enzymes acting in the root, periderm or seed were identified on the basis of their co-expression, leading to the hypothesis that a simple transcriptional module coordinates their transcription. Although the overexpression of several transcription factors can drive suberin biosynthesis in either Arabidopsis leaves or roots, the transcription of suberin biosynthetic genes is redundantly determined. It is only when a set of four Arabidopsis transcription factors—MYB41, MYB53, MYB92 and MYB93—are mutated that suberin is largely absent from the Arabidopsis root endodermis.

Although not studied in roots, the Arabidopsis MYB107 and MYB9 transcription factors are required for suberin biosynthetic gene expression and suberin deposition in seeds. These data demonstrate that multiple transcription factors coordinate the expression of suberin biosynthesis genes in Arabidopsis, dependent on the organ. Furthermore, components of these transcriptional regulatory modules are probably conserved across plant species, as orthologues of many of these transcription factors and their target genes are strongly co-expressed across multiple angiosperms. While the Arabidopsis root endodermis is well-characterized anatomically and molecularly, an additional root cell type deposits an apoplastic diffusion barrier during primary growth in other species. This cell layer is found below the epidermis, is the outermost cortical cell layer of the root and has been referred to as either the hypodermis or the exodermis. The latter term was used given observations of a potential Casparian Strip . Indeed, in 93% of angiosperms studied, the exodermal layer was reported to possess an apoplastic barrier composed of suberin or lignin. Given the nature of these features, the exodermis is hypothesized to function similarly to the endodermis, although the need for two potential barrier layers is less clear. The Solanum lycopersicum root contains both an exodermis and an endodermis. At its first stage of differentiation, a lignified cap is deposited on the outmost face of exodermal cell walls as well as on its anticlinal walls. During its second stage of differentiation,30 litre plant pots suberin is deposited around the entire surface of the exodermal cells. The drought or abscisic acid -inducibility of tomato exodermal suberin is unknown as is the influence of root exodermal suberization on environmental stress responses. Given this similarity in timing and appearance of suberin between the tomato exodermis and Arabidopsis endodermis, two plausible hypotheses regarding their regulation are that they use the same regulatory networks or that they utilize distinct cell type-specific programmes. In the absence of a suberized endodermis, the plant may be more drought-susceptible, or the exodermal barrier may be sufficient to serve as the sole functional barrier. To address these hypotheses, we profiled the transcriptional landscape of the tomato exodermis at cellular resolution and characterized suberin accumulation in response to the plant hormone ABA and in response to water deficit. We identified a co-expression module of potential suberin-related genes, including transcriptional regulators, and validated these candidates by generating multiple CRISPR–Cas9 mutated tomato hairy root lines using Rhizobium rhizogenes and tomato plants stably transformed with Agrobacterium tumefaciens, and screened them for suberin phenotypes using histochemical techniques. The validated genes included a MYB transcription factor whose mutant has a reduction in exodermal suberin, and the SlASFT whose mutant has a disrupted exodermis suberin lamellar structure with a concomitant reduction in root suberin levels. To test the hypothesis that suberin is associated with tomato’s drought response, we exposed slmyb92 and slasft mutant lines to water-deficit conditions. Both mutants displayed a disrupted response including perturbed stem water potential and leaf water status.

This work describes a regulatory network with conserved parts and rewiring to yield distinct spatial localization, and contributions of specific factors to produce this environmentally responsive functional barrier.We previously quantified exodermis suberin deposition along the longitudinal axis of the tomato root using the histochemical stain Fluorol Yellow . In Arabidopsis roots, suberin is absent from the endodermal cells in the root meristem and elongation zones, begins to be deposited in a patchy manner in the late differentiation zone after the CS has become established, and is then followed by complete suberization in the distal differentiation zone. Quantification of exodermal suberin in 7-day-old tomato roots demonstrated the same three categories of deposition . Electron microscopy further demonstrated that within the completely suberized zone, suberin lamellae are deposited primarily on the epidermal and inter-exodermal faces of the exodermal cell . Suberin was consistently absent within the root endodermis throughout all developmental zones. Monomer profiling of cell wall-associated and polymer-linked aliphatic suberin monomers in 1-month-old tomato roots revealed a predominance of α,ω-dicarboxylic acids, similar to potato. Compared with Arabidopsis roots, which mostly feature ω-OH acids and a maximum chain length of 24 carbons, additional C26 and C28 ω-OH acids and primary alcohols were observed in tomato . This phenomenon of inter-specific variation in suberin composition has been previously observed.To map the tomato root suberin biosynthetic pathway and its transcriptional regulators, we leveraged previous observations of relative conservation of transcriptional co-regulation of the suberin pathway across angiosperms. In the Arabidopsis root, suberin levels increase upon treatment with ABA, a hormone which is a first responder upon water-deficit stress. Exodermal suberin deposition in tomato is similarly increased upon ABA treatment, both in terms of the region that is completely suberized as well as in the intensity of the signal , with the continued absence of endodermal suberin . S. lycopersicum’s wild relative, Solanum pennellii , is drought tolerant, and enhanced suberin deposition in Arabidopsis via mutation of ENHANCED SUBERIN1 confers drought tolerance, although esb1 also shows enhanced endodermal lignin and interrupted CS formation. Hence, we tested and confirmed the hypotheses that S. pennellii has higher suberin deposition than M82 even in water-sufficient conditions and shows no changes in the magnitude or location of suberin deposition in response to ABA in seedlings . S. pennellii suberin levels are thus constitutive. Therefore, we utilized a gene expression dataset profiling transcription in M82 roots as well as across roots from 76 tomato introgression lines derived from S. lycopersicum cv. M82 and S. pennellii with M82 as the recurrent parent.

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.