article · Experimental Agriculture
AquaCrop, the Food and Agriculture Organization water productivity model, can accurately predict barley production under water-limited conditions using a limited set of input variables. The model was calibrated and validated using field experiment datasets spanning seven locations across Ethiopia, Italy, Syria, and the United States, covering various climates, years, and five barley cultivars. Evaluations showed strong statistical alignment between simulated and observed green canopy cover, soil water content, biomass, and grain yield. The validated model was subsequently applied to assess sowing approaches in northern Ethiopia's semi-arid environment. Simulations revealed that dry sowing carried a 47 per cent probability of germination failure due to false starts of the rainy season. Furthermore, while sowing might be delayed to eliminate germinating weeds, this delay must be kept as short as possible because water stress on shallow soils severely diminishes late-season grain yields.
Farmers in water-scarce regions face difficult decisions regarding when to plant their crops. By demonstrating that AquaCrop accurately simulates barley yields under varying climatic conditions with minimal data inputs, this work helps agricultural planners and advisory services determine optimal sowing times to avoid crop failure and maximise harvest yields under unpredictable rainfall.
The validated modelling approach provides an applied, tested decision-support tool for agricultural extension services, farm managers, and regional agronomists. It can inform seasonal planning guidelines and planting calendars in semi-arid barley-growing regions. As the software requires only a limited set of inputs, it is close to operational deployment for farm advisory and risk-mitigation services.
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SUMMARY AquaCrop, the FAO water productivity model, is used as a tool to predict crop production under water limiting conditions. In the first step AquaCrop was calibrated and validated for barley ( Hordeum vulgare L.). Data sets of field experiments at seven different locations in four countries (Ethiopia, Italy, Syria and Montana, USA) with different climates in different years and with five different cultivars were used for model calibration and validation. The goodness-of-fit between observed and simulated soil water content, green canopy cover, biomass and grain yield was assessed by means of the coefficient of determination ( R 2 ), the Nash–Sutcliff efficiency ( E ), the index of agreement ( d ) and the root mean square error (RMSE). The statistical parameters indicated an adequate accuracy of simulations (validation regression of yield: R 2 = 0.95, E = 0.94, d = 0.99, RMSE = 0.34). Subsequently, sowing strategies in the semi-arid environment of northern Ethiopia were evaluated with the validated model. Dry sowing had a probability of 47% germination failure attributable to false start of the rainy season. On the other hand, delay sowing at the start of the rainy season to eliminate germinating weeds should be kept as short as possible because grain yields strongly reduce in the season due to water stress when sowing is delayed on shallow soils. This research demonstrates the ability of AquaCrop to predict accurately crop performance with only a limited set of input variables, and the robustness of the model under various environmental and climatic conditions.
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DOI: 10.1017/s0014479711001190
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