Characterizing patterns of seasonal drought stress for use in common bean breeding in East Africa under present and future climates

Common bean (Phaseolus vulgaris L.) is the second most important source of dietary protein and the third most important source of calories in Africa, especially for the poor. In East Africa, drought is an important constraint to bean production. Therefore, breeding programs in East Africa have been trying to develop drought resistant varieties of common bean. To do this, breeders need information about seasonal drought stress patterns including their onset, intensity, and duration in the target area of the breeding program, so that they can mimic this pattern during field trials. Using the Decision Support for Agrotechnology Transfer (DSSAT) v4.7 model together with historical and future (Coupled Model Inter-comparison Project 6, CMIP6) climate data, this study categorized Ethiopia, Tanzania, and Uganda into different target population of environments (TPEs) based on historical and future seasonal drought stress patterns. We find that stress-free conditions generally dominate across the three countries under historical conditions (50–80% frequency). These conditions are projected to increase in frequency in Ethiopia by 2–10% but the converse is true for Tanzania (2–8% reduction) and Uganda (17–20% reduction) by 2050 depending on the Shared Socioeconomic Pathway (SSP). Accordingly, by 2050, terminal drought stresses of various intensities (moderate, severe, extreme) are prevalent in 34% of Uganda, around a quarter of Ethiopia, and 40% of the bean growing environments in Tanzania. The TPEs identified in each country serve as a basis for prioritizing breeding activities in national programs. However, to optimize resource use in international breeding programs to develop genotypes that are resilient to future projected stress patterns, we argue that common bean breeding programs should focus primarily on identifying genotypes with tolerance to severe terminal drought, with co-benefits in relation to adaptation to moderate and extreme terminal drought. Little to no emphasis on heat stress is warranted by 2050s

Stepwise model parametrisation using satellite imagery and hemispherical photography: tuning AquaCrop sensitive parameters for improved winter wheat yield predictions in semi-arid regions

Crop models are complex with many parameters, which has limited their application. Here we present an approach which both removes the model complexity through reducing the parameter dimensionality through sensitivity analysis, and presents a subsequent efficient approach to model parameterisation using swarm optimisation. We do this for two key model outputs, crop canopy and yield, and for two types of observational data, hemispheric photographs and Landsat7 imagery. Importantly we compare the usefulness of these two sources of data in terms of accurate yield prediction. The results showed that the dominant model parameters that predict canopy cover were generally consistent across the fields, with the exception of those related water stress. Although mid-season canopy cover extracted from Landsat7 was underestimated, good agreement was found between the simulated and observed canopy cover for both sources of data. Subsequently, less accurate yield predictions were achieved with the Landsat7 compared to the hemispherical photography-based parametrizations. Despite the small differences in the canopy predictions, the implications for yield prediction were substantial with the parametrization based on hemispherical photography providing far more accurate estimates of yield. There are, however, additional resource implications associated with hemispherical photography. We evaluate these trade-offs, providing model parametrization sets and demonstrating the potential of satellite imagery to assist AquaCrop, particularly on large scales where ground measurements are challenging.This work is part of the SAFA (Sustainable Agriculture For Africa) project which is funded by OCP, Morocco

A comparison of empirical and mechanistic models for wheat yield prediction at field level in Moroccan rainfed areas

peer reviewedIn the context of climate change, in-season and longer-term yield predictions are needed to anticipate local and regional food crises and propose adaptations to farmers’ practices. Mechanistic models and machine learning are two modelling options to consider in this perspective. In this study, regression (MR) and Random Forest (RF) models were calibrated for wheat yield prediction in Morocco, using data collected from 125 farmers’ wheat fields. Additionally , MR and RF models were calibrated both with or without remotely-sensed leaf area index (LAI), while considering all farmers’ fields, or specifically to agroecological zoning in Morocco. The same farmers’ fields were simulated using a mechanistic model (APSIM-wheat). We compared the predictive performances of the empirical models and APSIM-wheat. Results showed that both MR and RF showed rather good predictive quality (NRMSEs below 35%), but were always outperformed by APSIM model. Both RF and MR selected remotely-sensed LAI at heading, climate variables (maximal temperatures at emergence and tillering), and fertilization practices (amount of nitrogen applied at heading) as major yield predictors. Integration of remotely-sensed LAI in the calibration process reduced NRMSE of 4.5% and 1.8 % on average for MR and RF models respectively. Calibration of region specific models did not significantly improve the predictive. These findings lead to the conclusion that mechanistic models are better at capturing the impacts of in-season climate variability and would be preferred to support short term tactical adjustments to farmers’ practices, while machine learning models are easier to use in the perspective of mid-term regional prediction.SoilPhorLife-Projet

Assessing long-term conservation of groundwater resources in the Ogallala Aquifer Region using hydro-agronomic modeling

2022 Spring.Includes bibliographical references.Groundwater is vital for domestic use, municipalities, agricultural irrigation, industrial processes, etc. Over the past century, excessive groundwater depletion has occurred globally and regionally, notably in arid and semi-arid regions, often due to providing irrigation water for crop cultivation. The High Plains Aquifer (HPA) is the largest freshwater aquifer in the United States and has experienced severe depletion in the past few decades due to excessive pumping for agricultural irrigation. There is a need to determine management strategies that conserve groundwater, thereby allowing irrigation for coming decades, while maintaining current levels of crop yield within the context of a changing climate. Numerical models can be useful tools in this effort. Hydrologic models can be used to assess current and future storage of groundwater and how this storage depends on system inputs and outputs, whereas agronomic models can be used to assess the impact of water availability on crop production. Linking these models to jointly assess groundwater storage and crop production can be helpful in exploring management practices that conserve groundwater and maintain crop yield under future possible climate conditions. The objectives of this dissertation are: i) to develop a linked modeling system between DSSAT, an agronomic model, and MODFLOW, a groundwater flow model to be used for evaluating long-term impacts of climate and management strategies on water use efficiency and farm profitability of agricultural systems while managing groundwater sustainably; ii) to use the DSSAT-MODFLOW modeling system in a global sensitivity analysis framework to determine the system factors (climate, soil, management, aquifer) that control crop yield and groundwater storage in a groundwater-stressed irrigated region, thereby pointing to possibilities of efficient management; and iii) to quantify the effect of groundwater conservation strategies and climate on crop yield and groundwater storage to identify irrigation and planting practices that will maintain adequate crop yield while minimizing groundwater depletion. These three objectives are applied to the hydro-agronomic system of Finney County, Kansas, which lies within the HPA. Major findings include: 1) climate-related parameters significantly affect crop yields, especially for maize and sorghum, and soybean and winter wheat yields are sensitive to a combination of cultivar genetic parameters, soil-related parameters, and climate-related parameters; 2) Climatic parameters account for 44%, 29%, 40%, and 36% variation in yield of maize, soybean, winter wheat, and sorghum; 3) Hydrogeologic parameters (aquifer hydraulic conductivity, aquifer specific yield, and riverbed conductance) have a relatively low influence on crop yields; 4) water table elevation, recharge, and irrigation pumping are considerably sensitive to soil- and climate-related parameters, while ET, river leakage, and groundwater/aquifer discharge are highly influenced by hydrogeological parameters (e.g., riverbed conductance, and specific yield); 5) the best management practice is the combination of implementing drip irrigation and planting quarter plots under both dry and wet future climate conditions. Other irrigation systems (sprinkler) and planting decisions (half-plots) can also be implemented without severe groundwater depletion. If crop yield is to be maintained in this region of the HPA, groundwater depletion can be minimized but not completely prevented. Results highlight the need for implementing new irrigation technologies, and likely changing crop type decisions (e.g., limiting corn cultivation) in coming decades in this region of the HPA. Results from this dissertation can be used in other groundwater-irrigated regions facing depletion of groundwater