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The livelihoods made possible by the infrastructure contribute to infection risk in this landscape

Parasitic worms of the genus Schistosoma inhabit the urogenital or the intestinal tract of the human host, with parasite eggs excreted in human urine and feces, respectively. Freshwater snails of the genera Bulinus serve as the intermediate host of S. haematobium while Biomphalaria transmit S. mansoni. These snail genera have distinct ecologies: Bulinus snails are able to withstand prolonged periods of drying while Biomphalaria are sensitive to saline conditions. Species from both genera, however, can colonize irrigation canals. Expansion of snail habitat and increased human activity in the aquatic environment both increase transmission in dammed and irrigated areas. However, few studies disentangle the environmental and socio-behavioral mechanisms of schistosomiasis occurrence in areas where water resources are actively managed. Of the approximately 800 million people at risk for schistosomiasis worldwide, 100 million live in close proximity to dams and irrigation schemes. An estimated 200 million are infected, the majority of which live in sub-Saharan Africa. Parasites exiting snail hosts penetrate the skin of people who are in direct contact with water. Acute symptoms of schistosomiasis include hematuria for S. haematobium infection and diarrhea and abdominal pain for S. mansoni infection. Prolonged infection can lead to anemia, organ damage, and cancer. While lethal pathologies are linked to infection with both S. haematobium and S. mansoni, deaths are not often officially attributed to infection, and as a result, are likely underestimated. Because available treatments do not prevent re-infection, regular contact with water can lead to chronic infection and severe disease. In some settings, prevalence and intensity of infection remain high even in the presence of treatment programs. Existing evidence linking irrigated agriculture and schistosome infection relies on village- or landscape-level aggregations of disease occurrence and agricultural activity ,plastic pots plants but finer-scale processes associated with irrigation influence exposure to parasites present in surface water.

The household is a particularly relevant unit for both agricultural activity and water contact behavior. However, few studies investigate whether household-level circumstances compound infection risk present in the environment. In this study, we investigated whether participation in agriculture at the household level was associated with individual-level schistosome infection. We assumed that in rural areas, the majority of schistosome exposure would occur at water access sites within a village and hypothesized that household-level cultivation of irrigated crops may represent additional exposure beyond the village-based exposure that occurs for most people. As a result, we suspected that children living households that cultivated irrigated land would be more likely to be infected with both species of schistosome. We further reasoned that, because larger areas of irrigated land are served by greater lengths of irrigation infrastructure, the occurrence of schistosomiasis would further increase in households that were cultivating larger areas of land. Larger fields served by more irrigation infrastructure would require more person-time to manage, harbor more snails, produce more parasites and, ultimately, increase schistosome exposure. With this rationale, we examined the relationship between area of irrigated land reported at the household level and individual-level infection outcomes, focusing on school-aged children who are often the target of mass drug administration campaigns. While studies of both schistosomiasis ecology and water contact behavior often focus on the water access sites within a village, human contact with agricultural water sources could play an important role in sustaining schistosome transmission in a way that threatens the success of ongoing schistosomiasis control efforts. While the spatial distribution of agricultural water sources makes them difficult to monitor, processes of exposure and contamination in agricultural water sources may contribute to the contamination of village water sources and lead to re-infection after treatment. Such processes may contribute to the development of persistent hot spots of schistosomiasis transmission. For schistosome transmission to be successfully suppressed, the implementation of interventions needs to account for the spatial scale of all the transmission sites in and around a village and the human movement between them. Te lower basin of the Senegal River became hyperendemic for schistosome transmission following the construction of the Diama dam in 1986, which was designed as a saltwater barrier to support agricultural development.

Prior to dam construction, S. haematobium infections occurred seasonally at low levels, while S. mansoni was absent from the region. By preventing saltwater intrusion, stabilizing water levels, promoting vegetation growth and disrupting the life histories of snail predators, the dam triggered environmental changes that favored the snail-borne transmission cycles of both S. haematobium and S. mansoni. Salt-sensitive Biomphalaria have since become established in the perennially freshwater environment, while the irrigation infrastructure provides new habitat for both Bulinus and Biomphalaria. The prevalence and intensity of both infections remain high today in human populations, and agricultural practices have increasingly shifted to the cultivation of irrigated crops. Given the environmental changes affecting both parasite transmission and livelihoods in this setting, we aimed to understand whether household engagement in irrigated agriculture compounds the infection risk created by the dam, and whether agricultural sites of water contact may be involved in schistosome transmission in this setting. Specifically, we investigated whether schistosome infection increased in school-aged children living.This is study used cross-sectional data collected as part of a longitudinal study of schistosome infection in school aged children and the socio-economic conditions of the households where those children resided. Sixteen villages along the Senegal River, its tributaries and the Lac de Guiers in northwest Senegal were chosen to represent rural, high-transmission sites common in the region . Village selection criteria are described in detail elsewhere, but included proximity to freshwater and presence of water access sites, presence of a school with sufficient enrollment in target grades and a non-zero prevalence of self-reported infection as well as accessibility in the rainy season. School-aged children were recruited from grades 1–3 in village schools. Agriculture was common in all villages with cultivation of irrigated rice using constructed irrigation infrastructure undertaken primarily in river villages and gardening and monocropping supported by hand-dug infrastructure in lake villages.Parasitological data were derived from a single year of a longitudinal, school-based parasitological study in all 16 villages.

A total of 1480 school-aged children were enrolled at baseline in February–April 2016. Of those, 1479 remained enrolled in January–April 2017 and 1414 successfully produced urine or stool samples on the two testing days that year . On each testing day, one urine and one stool sample were collected from each child enrolled in the study. Urine and stool sample collection was organized at the school by trained personnel from the Biomedical Research Center Espoir Pour La Sante. Sampling pots were provided to each participant 24 h in advance, and samples were kept in isothermal boxes during transport back to the laboratory. Samples were analyzed by urine fltration for S. haematobium infection and duplicate Kato-Katz examination of stool samples for S. mansoni infection by standard methods. In each year of the longitudinal study, all children were treated with 40 mg/kg of praziquantel following sample collection. The cross-sectional parasitological data from 2017 data used in this study, thus, refect post-treatment re-infection over the preceding year.Household survey were data collected in August 2016, during the rainy season preceding the 2017 parasitology data collection. We aimed to reach all the households where school-aged children enrolled in the parasitology study resided. The household survey instrument included six modules . The modules used in this analysis included demographic and occupational information for every member of the household,plastic nursery pots agricultural land use for all parcels owned and/or cultivated by household members, and data on building materials and durable assets, which were used to approximate socio-economic status. Surveys were completed in 655 households . The questionnaire was developed in English and translated to French by native speaking members of the field team. A team of eight Senegalese enumerators were trained to obtain verbal informed consent, pose survey questions in Wolof and record data in French. Prior to data collection, all survey questions were reviewed in French and the proper Wolof translations of key terms and ideas were discussed at length and agreed upon by all members of the enumerator team.We find evidence that the occurrence of S. haematobium but not S. mansoni infections in a dammed landscape is compounded by engagement in agricultural livelihoods. In the lower basin of the Senegal River, the presence of S. haematobium infections in school-aged children increase with irrigated area cultivated by members of their households. This may result from greater contact with Bulinusand S. haematobium-laden water among children whose families use and manage infrastructure for irrigating crops, compared to those whose contact occurs primarily at village water access sites. The observed association between irrigated area on S. mansoni infection presence was smaller and more uncertain, as were the associations of irrigated area with both measures of infection intensity, preventing frm conclusions about these outcomes. The contrast between S. haematobium and S. mansoni outcomes may reflect different sources of contamination in agricultural surface waters, such that the circulation of S. haematobium is more easily sustained by the input of urine than S. mansoni by the input of feces.

Our use of individual- and household-level data suggest that irrigated agriculture contributes to increased infection risk beyond the environmental consequences of infrastructure development. Previous meta-analysis on the topic revealed a greater increase in the occurrence of S. mansoni compared to S. haematobium in irrigated areas at the landscape scale. This and other studies that have examined landscape-scale measures of disease occurrence and land use support the notion that human-mediated environmental change is associated with elevated infection prevalence, but do not shed light on the finer-scale mechanisms that influence individual infection risk. The relationship between disease risk and environmental exposures depends on the scales at which the relevant biotic, abiotic and human factors operate, such that individual- and landscape level processes of disease and land use are not interchangeable and may represent distinct constructs. In the lower basin of the Senegal River, dam construction in support of agricultural development has altered the landscape by stabilizing water levels, preventing saltwater intrusion and expanding the aquatic habitat available to the snails that transmit schistosomes. Our use of finer-scale data establishes that processes related to household land use also play a role in determining risk for acquiring infection from the environment.These findings also suggest that—beyond the in-village water access sites that are the typical focus of studies of schistosome ecology and water contact behavior—agricultural water sources play a role in sustaining schistosome transmission and connecting transmission sites to each other. Te frequent use of irrigation canals for a wide variety of activities is likely to result in both snail-to human and human-to-snail transmission in water sources outside a village. This may be particular true for S. haematobium, whose eggs can be introduced more easily into the environment through urination compared to S. mansoni, whose eggs get introduced into the environment through defecation. If exposure and contamination occurs in both village and agricultural water sources, the human movement and water contact behaviors that connect these water sources will inevitably expand the spatial scope of transmission and the interventions needed to interrupt it. Networks of water sources may ensure continuous introduction of parasites into a village, perpetuating transmission, threatening the success of both MDA and environmental interventions and potentially leading to the formation of persistent hot spots. In this way, the design of interventions must account for the influence of human behavior on the ecological processes that affect infection risk at the proper scales. As calls continue for environmental interventions to complement mass drug administration, the development of implementation guidelines should consider for the full spectrum of water contact activity and the disperse water sources that might contribute to transmission in a particular setting. The ability of water, sanitation and hygiene interventions to reduce both exposure- and contamination-related behaviors, for example, may not be effective if agricultural water sources are disregarded. Environmental complements to MDA interventions may include cleaning aquatic vegetation to reduce snail habitat and chemical and biological control of snail populations in both water access points and irrigation canals. This research has some limitations. Odds only approximate risk when outcomes are rare. Because the outcomes in this study are not rare, our estimates are biased away from the null compared to prevalence ratios. We attempted to directly estimate prevalence ratios by fitting log-binomial models, but these models did not converge.

Most economic models make simple profit maximization assumptions

Management data tend to be sparsely available and representing continuity of plant populations is challenging. Advancing our ability to understand how grasslands are managed – to understand, for example, what species are planted, what inputs are provided, what grazing management is applied – is centrally important for improving our ability to model pasture and range land systems. Planted pastures and native grazing lands both contain a variety of species, some of which are more palatable, nutritious, grazing-resistant, or fire-resilient than others. A more open, data-rich environment could facilitate evaluation of a variety of approaches for representing long-term dynamics, which could address several important grassland management and assessment issues. Managing grass swards to maintain desirable plants is a primary goal of grassland management, but one for which modeling tools have offered limited assistance. Models that represent vegetation dynamics are also desirable for understanding longer-term changes in species that can impact productive capacity, sensitivity to degradation, and carbon dynamics . Year-to-year variability is a key component for understanding potential utility and risk of relying on grassland forage resources. Next generation models that enhance our ability to forecast this risk would mark a substantial and meaningful advance. There is a need for better links between the agricultural modeling communities and ecological researchers studying long-term vegetation dynamics. The primary use of forage resources is for grazing animals, yet most grassland models are only loosely coupled with grazers . Better integration through grazing effects on grasslands, grazer distributions across landscapes,plastic pots plants forage demand/consumption, livestock/wildlife movement, etc., would enhance the ability of models to contribute to important emerging issues.

For example, holistic grazing management, in which several aspects of management vary in response to a variety of different cues from the land and expectations about future conditions, can be impossible to evaluate with current modeling frameworks. A system that integrated user demand into the model development process could lead to implementation of new data-management feedback loops within models. Such interactions between users and producers of information could direct data collection to facilitate model use. Models that better represent grazer-grassland interaction are also crucial for understanding how efficiently livestock use forage resources, what is necessary to sustain wildlife populations, and how much grassland output might be available for other uses .The Intergovernmental Panel on Climate Change has reviewed the existing evidence for how climate change may affect weeds, pests, and diseases . One issue with this evidence base is that there is a clear publication bias towards reports of increased threats – people often do not bother to write up no-effect results. There is a general recognition that we need good models to help tease out different effects that changing weather will have simultaneously on both crops and the organisms that compete with or attack them. There has already been some work applying crop physiology-type models to weeds, and developing more mechanistic models of the effect of temperature on insect pests. There is an opportunity and need for more integrated models that include interactions between organisms, for example between weeds and crops, and between pests and the predators and parasites that attack them. A variety of different approaches are possible, and there is a need for an AGMIP-type approach to help the community decide how best to move forward.Highly contagious diseases of livestock present a major threat to agriculture, both in the developed and developing worlds.

Diseases may be chronic in livestock populations, emerge from wildlife reservoirs, or possibly be introduced deliberately by man as an act of bio-terrorism. Models are required to help understand how a disease will spread, and to help policymakers design optimal interventions. These models must encompass not only the epidemiology of the disease but also how it is affected by agricultural practices and in particular the movement of livestock by farmers. There have been significant recent advances in this area, often building on work on human diseases. For example, it is now possible to take livestock movement data and use it to parameterize an epidemiological model . There are the beginnings of a model comparison movement in human epidemiology; livestock disease epidemiology would also benefit from this approach.There is intense current research activity into novel genetic methods of insect control. Most of this work is currently directed at the insect vectors of human diseases such as malaria, though the same methodology can be applied to insect pests of crops and of course the vectors of livestock diseases. The greatest advantage of these approaches is that they involve self-sustaining interventions that spread naturally through a pest population, although because they are nearly all classified as genetically modified, the regulatory issues surrounding them are complex. Cutting-edge modeling work in this field involves joint population and genetic dynamic models, many of which are explicitly spatial. This topic is likely to be one of the most important and exciting areas of modeling as applied to agriculture over the next few decades.Integrated agricultural technologies, defined as the integration of improved genetics, agronomic input, information technology, sensors, and intelligent machinery, will play a pivotal role in agriculture in the years to come. These innovations will be driven by economic forces, by the need to produce more food with limited land and water for the increasing population, and at the same time by the push to save resources to reduce the environmental impact associated with food production. While these changes are occurring now in the commercial scale industrialized agricultures of the world, many of these technologies have the capability to be adapted to conditions in other parts of the world.

The cell phone now allows farmers in rural areas almost everywhere in the world to have low-cost information about prices, for example. Similarly, it is likely that unmanned aerial vehicles will rapidly be adapted to conditions around the world and used to carry out activities such as monitoring crop growth and pest occurrence, and improve management decisions. In large-scale, capital-intensive agricultural systems, these technologies are rapidly leading to the automation of many production activities, particularly machinery operation and decisions about input application rates. The automation of agriculture began in the mid-nineties, resulting in large amounts of data available to farmers and agribusiness companies. Farm machinery are now often equipped with high precision global positioning system controllers, which allow all activity on the farm to be recorded, geo-referenced, and stored on remote computers: “in the cloud.” All modern tractors collect data on a continuous basis and are equipped with wireless connectivity for data transmission. Harvesters record the yield at a particular location, planters can vary the plant spacing or type of seed by location, and sprayers can adjust quantity and type of fertilizer, fungicide or pesticide by location; all to a granularity of just a few square meters. Yield monitoring can now be linked to unmanned aerial vehicle imagery to produce a prescription map for the farmer to implement. These private data could also provide tremendous benefit to the researcher community, should access be increased. Producers in some regions of the world now have historical crop yield data for their fields at very high resolution. Combined with advanced satellite-based imagery, high-resolution spectral and thermal data obtained from UAVs,plastic nursery pots and weather forecasts, growers have most of the critical inputs required to convert this “big data” into an actionable management plan with equipment that can vary fertilizer and other inputs spatially within a field. Despite these rapid advances in the sophistication and automation of farm equipment, a vital piece of the equation is still lacking: the analysis of the vast amount of newly available data in order to provide the farmer with a map of what action to take where and when. Most variable rate application is currently managed by farmers, using rule-of-thumb and empirical approaches, and not by using a systems approach that accounts for the interaction of soil, crop, management, and weather. Thus much of the power of automation remains unexploited. In order to realize the full potential of more sophisticated equipment, new modeling systems for precision agriculture are needed. These systems could be based on comprehensive predictive crop yield models that combine publicly available data, such as soil type, weather, and prices, along with location-specific data from farmers’ yield maps of their fields, to provide a prescriptive crop management plan at high spatial resolution, as in Fig. 2. This type of system could deliver automated crop simulations, crop management strategy recommendations, process-based variable rate prescriptions, risk assessments, continual in season simulations, integration of in-season crop scouting UAVs flight information, pest management prescriptions and accurate harvest recommendations via simple-to-use apps, websites, or smart phones. In addition to the farm-to-landscape scale analysis represented in Fig. 2, there will be a growing demand for agricultural systems models to simulate and integrate the different components of the agricultural value chain, to meet both policy requirements and corporate sustainability goals . Genetics, agronomic management , weather, soil, information technology and machinery will need to be linked in a system approach to address these informational needs. This is a new frontier for agricultural system modeling that would extend to the broader food system and raise additional data and analytical challenges.

As illustrated in Fig. 2 and discussed in the previous section, various management and production data are becoming available through mobile technologies . An example of this analytical capability is the AgBizLogic™ software developed by several university extension programs, which allows managers to calculate short-term profitability and rates of return on long-term investments . Similar proprietary software tools are being developed and used. These analytical tools could be linked with modules that track or predict environmental outcomes such as soil erosion and net greenhouse gas emissions . Low-bandwidth versions of these tools need to be developed for use in areas where mobile phone technology is a limiting factor. Analytical tools need to be adapted to fit small-holder systems as indicated by the NextGen Use Cases. The flood of data on physical land-use, water availability and use, and yields coming from mobile devices and remote sensing systems suggest that both the biophysical and behavioral aspects of farm production at specific locations can be estimated by sequential learning processes. The use of advances in computational methods such as machine learning and remote sensing data is illustrated by analysis of the impact of the 2009 and 2014 droughts on California agriculture, which demonstrated the advantages of better data .To facilitate the use of models for various locations and systems, and to link to crop and livestock system simulation models, economic models need to be incorporated into modules with standardized inputs and outputs. Various types of economic models are available in the literature, including farm-level optimization models, regional positive quadratic programming models, econometric land-use models, and regional impact assessment models . User needs should dictate which types of models should be used depending on informational needs. Methods and protocols are required to link regional economic models with market equilibrium models . Some progress has been made on this front but much more development is needed to address various aggregation and dis-aggregation issues . Generalization of behavioral assumptions and investigation of their effects on investment and policy analysis is also needed.There is a rich literature on risk modeling which could be incorporated. Recent advances in the expectations formation literature and the behavioral economics literature could be investigated for use in agricultural systems models.The application of different farm improvement methods has explicit winners but also unintended ‘casualties’ and perverse incentives. From a development standpoint, it is essential to understand these dynamics to ensure that appropriate policies are developed to maintain equal opportunities for all sectors of society. For example, in many cases, rich farmers are the ones who adopt technologies early. This factor could potentially disrupt power relationships in markets, thus affecting poorer farmers. In this case it is essential to design alternative options and safety nets for poorer farmers to prevent widening the gap and making them more vulnerable. New models should improve our understanding of these processes, as we move from single farm models to multi-farm and regional models. Methods utilizing population-based data are providing improved capability to represent distributional impacts and vulnerability .Current agricultural system models typically operate at the point/ field scales with an emphasis on vertical fluxes of energy, water, C, N and nutrients between the atmosphere, plant and soil root zone continuum.