Is closing the agricultural yield gap a “risky” endeavor?

CONTEXT: Sub-Saharan Africa (SSA) has the climatic and biophysical potential to grow the crops it needs to meet rapidly growing food demand; however, agricultural productivity remains low. While potential maize yields in Zambia are 9 t per hectare (t/ha), the average farmer produces only 1–2. OBJECTIVE: We evaluate the contribution of responses to weather risk to that gap by decomposing the yield gap in maize in Zambia. While we know that improved seed and fertilizer can expand yield and profit, they may also increase the variance of yield under different weather outcomes, reducing their adoption. METHODS: We use a novel approach combining crop modeling and statistical analysis of survey data to obtain the yield gap components in Zambia driven by input cost and input risk. We use a crop model to simulate district-level marginal effects of fertilizer and seed maturity choice on the mean and variance of expected yield and profit under all-weather outcomes for each district for the past 30 years. We compare input levels that maximize expected yield to those that maximize expected profit and maximize the expected mean-variance trade-off assuming risk-aversion. To determine how much farmers' input choices are made to reduce risk, we then quantify differences in the expected riskiness of inputs by district. RESULTS AND CONCLUSIONS: We find approximately one-quarter of the yield gap can be explained by risk-reducing behavior, albeit with a substantial geographic variation. Given this finding, under present conditions, we expect that the average maximum yield that farmers can obtain without increasing risk is 6.75 t/ha compared to a potential profit-maximizing level of 8.84 t/ha. SIGNIFICANCE: The risk-related yield gap is only expected to increase with weather extremes driven by climate change. Promoting “one-size-fits all” solutions to closing the yield gap could underestimate the effect of risk mitigation on agricultural production while increasing farmers.CONTEXTO: El África subsahariana (ASS) tiene el potencial climático y biofísico para aumentar los cultivos que necesita para satisfacer la creciente demanda de alimentos; sin embargo, la productividad agrícola sigue siendo baja. Si bien los rendimientos potenciales del maíz en Zambia son de 9 t por hectárea (t/ha), el agricultor promedio produce sólo 1-2. OBJETIVO: Evaluamos la contribución de las respuestas al riesgo climático a esa brecha descomponiendo la brecha de rendimiento del maíz en Zambia. Si bien sabemos que las semillas y los fertilizantes mejorados pueden aumentar el rendimiento y las ganancias, también pueden aumentar la variación del rendimiento en diferentes condiciones climáticas, lo que reduce su adopción. MÉTODO: Utilizamos un enfoque novedoso que combina modelos de cultivos y análisis estadístico de datos de encuestas para obtener los componentes de la brecha de rendimiento en Zambia impulsados por el costo y el riesgo de los insumos. Utilizamos un modelo de cultivo para simular los efectos marginales a nivel de distrito de la elección de la madurez de las semillas y los fertilizantes sobre la media y la varianza del rendimiento y la ganancia esperados bajo resultados en cualquier condición climática para cada distrito durante los últimos 30 años. Comparamos los niveles de insumos que maximizan el rendimiento esperado con aquellos que maximizan el beneficio esperado y maximizan la compensación esperada entre media y varianza suponiendo aversión al riesgo. Para determinar en qué medida los agricultores eligen insumos para reducir el riesgo, luego cuantificamos las diferencias en el riesgo esperado de los insumos por distrito. RESULTADOS Y CONCLUSIONES: Encontramos que aproximadamente una cuarta parte de la brecha de rendimiento puede explicarse por un comportamiento de reducción de riesgos, aunque con una variación geográfica sustancial. Dado este hallazgo, en las condiciones actuales, esperamos que el rendimiento máximo promedio que los agricultores pueden obtener sin aumentar el riesgo sea de 6,75 t/ha en comparación con un nivel potencial de maximización de ganancias de 8,84 t/ha. SIGNIFICADO: Sólo se espera que la brecha de rendimiento relacionada con el riesgo aumente con los extremos climáticos impulsados por el cambio climático. Promover soluciones únicas para cerrar la brecha de rendimiento podría subestimar el efecto de la mitigación de riesgos en la producción agrícola y al mismo tiempo aumentar los agricultores.Centro de Investigación en Economía y ProspectivaFil: Gatti, Nicolás. Instituto Nacional de Tecnología Agropecuaria (INTA). Centro de Investigación en Economía y Prospectiva (CIEP); ArgentinaFil: Gatti, Nicolás. Universidad del Centro de Estudios Macroeconómicos de Argentina (UCEMA); ArgentinaFil: Cecil, Michael. Clark University. Department of Geography; Estados UnidosFil: Baylis, Kathy. University of California Santa Barbara. Department of Geography; Estados UnidosFil: Estes, Lyndon. Clark University. Department of Geography; Estados UnidosFil: Blekking, Jordan. Indiana University. Bloomington Department of Geography; Estados UnidosFil: Heckelei, Thomas. Universitaet Bonn. Institute for Food and Resource Economics; AlemaniaFil: Vergopolan, Noemi. Princeton University. Atmospheric and Oceanic Sciences Program; Estados UnidosFi: Evans, Tom. University of Arizona. School of Geography, Development & Environment; Estados Unido

Is closing the agricultural yield gap a risky endeavor?

CONTEXT: Sub-Saharan Africa (SSA) has the climatic and biophysical potential to grow the crops it needs to meet rapidly growing food demand; however, agricultural productivity remains low. While potential maize yields in Zambia are 9 t per hectare (t/ha), the average farmer produces only 1–2. OBJECTIVE: We evaluate the contribution of responses to weather risk to that gap by decomposing the yield gap in maize in Zambia. While we know that improved seed and fertilizer can expand yield and profit, they may also increase the variance of yield under different weather outcomes, reducing their adoption. METHODS: We use a novel approach combining crop modeling and statistical analysis of survey data to obtain the yield gap components in Zambia driven by input cost and input risk. We use a crop model to simulate district-level marginal effects of fertilizer and seed maturity choice on the mean and variance of expected yield and profit under all-weather outcomes for each district for the past 30 years. We compare input levels that maximize expected yield to those that maximize expected profit and maximize the expected mean-variance trade-off assuming risk-aversion. To determine how much farmers\u27 input choices are made to reduce risk, we then quantify differences in the expected riskiness of inputs by district. RESULTS AND CONCLUSIONS: We find approximately one-quarter of the yield gap can be explained by risk-reducing behavior, albeit with a substantial geographic variation. Given this finding, under present conditions, we expect that the average maximum yield that farmers can obtain without increasing risk is 6.75 t/ha compared to a potential profit-maximizing level of 8.84 t/ha. SIGNIFICANCE: The risk-related yield gap is only expected to increase with weather extremes driven by climate change. Promoting “one-size-fits all” solutions to closing the yield gap could underestimate the effect of risk mitigation on agricultural production while increasing farmers\u27 risk exposure. © 2023 The Author

Cognitive Biases about Climate Variability in Smallholder Farming Systems in Zambia

Given the varying manifestations of climate change over time and the influence of climate perceptions on adaptation, it is important to understand whether farmer perceptions match patterns of environmental change from observational data. We use a combination of social and environmental data to understand farmer perceptions related to rainy season onset. Household surveys were conducted with 1171 farmers across Zambia at the end of the 2015/16 growing season eliciting their perceptions of historic changes in rainy season onset and their heuristics about when rain onset occurs. We compare farmers' perceptions with satellite-gauge-derived rainfall data from the Climate Hazards Group Infrared Precipitation with Station dataset and hyper-resolution soil moisture estimates from the HydroBlocks land surface model. We find evidence of a cognitive bias, where farmers perceive the rains to be arriving later, although the physical data do not wholly support this. We also find that farmers' heuristics about rainy season onset influence maize planting dates, a key determinant of maize yield and food security in sub-Saharan Africa. Our findings suggest that policy makers should focus more on current climate variability than future climate change.National Science Foundation [SES-1360463, BCS-1115009, BCS-1026776]6 month embargo; published online: 29 March 2019This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors

Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as "open-loop" models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo (Merged soil Moisture) and six estimates from the HBV (Hydrologiska Byrans Vattenbalansavdelning) model with three precipitation inputs (ERA5, IMERG, and MSWEP) with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5 cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. We found that application of the Soil Wetness Index (SWI) smoothing filter resulted in improved performance for all satellite products. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3E(SWI), SMOSSWI, AMSR2(SWI), and ASCAT(SWI), with the L-band-based SMAPL3ESWI (median R of 0.72) outperforming the others at 50% of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCISWI), MeMo performed better on average (median R of 0.72 versus 0.67), probably due to the inclusion of SMAPL3ESWI. The best-to-worst performance ranking of the six openloop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by C0 :12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by C0:06, suggesting that data assimilation yields significant benefits at the global scale

Global-scale evaluation of 23 precipitation datasets using gaugeobservations and hydrological modeling

Abstract. We undertook a comprehensive evaluation of 23 gridded (quasi-)global (sub-)daily precipitation (P) datasets for the period 2000–2016. Thirteen non-gauge-corrected P datasets were evaluated using daily P gauge observations from 76 086 gauges worldwide. Another ten gauge-corrected datasets were evaluated using hydrological modeling, by calibrating the conceptual model HBV against streamflow records for each of 9053 small to medium-sized

Dynamic geospatial modeling of mycotoxin contamination of corn in Illinois: unveiling critical factors and predictive insights with machine learning

Mycotoxin contamination of corn is a pervasive problem that negatively impacts human and animal health and causes economic losses to the agricultural industry worldwide. Historical aflatoxin (AFL) and fumonisin (FUM) mycotoxin contamination data of corn, daily weather data, satellite data, dynamic geospatial soil properties, and land usage parameters were modeled to identify factors significantly contributing to the outbreaks of mycotoxin contamination of corn grown in Illinois (IL), AFL >20 ppb, and FUM >5 ppm. Two methods were used: a gradient boosting machine (GBM) and a neural network (NN). Both the GBM and NN models were dynamic at a state-county geospatial level because they used GPS coordinates of the counties linked to soil properties. GBM identified temperature and precipitation prior to sowing as significant influential factors contributing to high AFL and FUM contamination. AFL-GBM showed that a higher aflatoxin risk index (ARI) in January, March, July, and November led to higher AFL contamination in the southern regions of IL. Higher values of corn-specific normalized difference vegetation index (NDVI) in July led to lower AFL contamination in Central and Southern IL, while higher wheat-specific NDVI values in February led to higher AFL. FUM-GBM showed that temperature in July and October, precipitation in February, and NDVI values in March are positively correlated with high contamination throughout IL. Furthermore, the dynamic geospatial models showed that soil characteristics were correlated with AFL and FUM contamination. Greater calcium carbonate content in soil was negatively correlated with AFL contamination, which was noticeable in Southern IL. Greater soil moisture and available water-holding capacity throughout Southern IL were positively correlated with high FUM contamination. The higher clay percentage in the northeastern areas of IL negatively correlated with FUM contamination. NN models showed high class-specific performance for 1-year predictive validation for AFL (73%) and FUM (85%), highlighting their accuracy for annual mycotoxin prediction. Our models revealed that soil, NDVI, year-specific weekly average precipitation, and temperature were the most important factors that correlated with mycotoxin contamination. These findings serve as reliable guidelines for future modeling efforts to identify novel data inputs for the prediction of AFL and FUM outbreaks and potential farm-level management practices

Towards Locally Relevant Global Soil Moisture Monitoring Leveraging Remote Sensing and Modeling for Water Resources Applications

Accurate and detailed soil moisture estimates can critically shape cross-sectoral water resources decision-making. From local to regional scales, monitoring of agricultural water demands, droughts, floods, landslides, and wildfires can benefit from high-resolution soil moisture information. However, soil moisture highly varies in space and time, and as a result, it is challenging to obtain detailed information at the stakeholder-relevant spatial scales. This dissertation leverages advances in satellite remote sensing, hyper-resolution land surface modeling, high-performance computing, and machine learning to bridge this data gap. Chapter 2 introduces a novel cluster-based Bayesian merging scheme that combines NASA's SMAP satellite observations and hyper-resolution land surface modeling for obtaining satellite-based surface soil moisture retrievals at an unprecedented 30-m spatial resolution. This approach's scalability and accuracy are demonstrated in Chapter 3 by introducing SMAP-HydroBlocks, the first satellite-based surface soil moisture dataset at a 30-m resolution over the United States (2015-2019). Using this dataset, Chapter 4 assesses the multi-scale properties of soil moisture spatial variability and the persistence of this variability across spatial scales. This analysis maps where detailed information is critical for solving water, energy, and carbon scale-dependent processes and how much variability is lost when data is only available at coarse spatial scales. Using machine learning, Chapter 5 demonstrates the value of high-resolution soil moisture for drought monitoring and crop yield prediction at farmer's field scales (250-m resolution). This dissertation provides a novel pathway towards global monitoring of water resources' dynamics at locally relevant spatial scales

HPC simulations of brownout: A noninteracting particles dynamic model

Helicopters can experience brownout when flying close to a dusty surface. The uplifting of dust in the air can remarkably restrict the pilot’s visibility area. Consequently, a brownout can disorient the pilot and lead to the helicopter collision against the ground. Given its risks, brownout has become a high-priority problem for civil and military operations. Proper helicopter design is thus critical, as it has a strong influence over the shape and density of the cloud of dust that forms when brownout occurs. A way forward to improve aircraft design against brownout is the use of particle simulations. For simulations to be accurate and comparable to the real phenomenon, billions of particles are required. However, using a large number of particles, serial simulations can be slow and too computationally expensive to be performed. In this work, we investigate an message passing interface (MPI) + graphics processing unit (multi-GPU) approach to simulate brownout. In specific, we use a semi-implicit Euler method to consider the particle dynamics in a Lagrangian way, and we adopt a precomputed aerodynamic field. Here, we do not include particle–particle collisions in the model; this allows for independent trajectories and effective model parallelization. To support our methodology, we provide a speedup analysis of the parallelization concerning the serial and pure-MPI simulations. The results show (i) very high speedups of the MPI + multi-GPU implementation with respect to the serial and pure-MPI ones, (ii) excellent weak and strong scalability properties of the implemented time-integration algorithm, and (iii) the possibility to run realistic simulations of brownout with billions of particles at a relatively small computational cost. This work paves the way toward more realistic brownout simulations, and it highlights the potential of high-performance computing for aiding and advancing aircraft design for brownout mitigation