Abstract

To quantify, integrate and assess the impacts from weather and climate change/variability on crop growth and productivity, crop models have been used for several years as decision support tools in the world. This paper is reviewed to assess applications of Aqua crop model as a decision support tool for simulating and validating crop management practices and climate change adaptation strategies. This model is devised by the FAO irrigation and drainage team. This model is very important especially, to guide as a decision support tool for dry land areas where soil moisture is very critical to affect crop productivity. It maintains the balance between simplicity, accuracy and robustness. The model has been calibrated and validated to simulate growth and productivity of crops, soil moisture balance, water use efficiency, evapo-transpiration and climate change impact assessment in different climate, management (water, fertilizer, sowing date, spacing etc.) practices around the world, especially in areas where soil moisture stress prevails. Maize, wheat, barley, tee, sorghum, pulse crops such as groundnut, soybean, vegetables (tomato, cabbage) have been tested using this model. The model comprehensively uses stress coefficients (water stress, fertilizer and temperature coefficients) to compute the effect of the factors on crop canopy, dry matter, stomatal closure, flowering, pollination and harvest index build up.

Aqua crop model is calibrated for canopy cover expansion (development), dry matter accumulation and Soil Moisture Content (SMC) in the root zone and initial Harvest Index (HIo) under optimal growth conditions, to simulate the crop canopy development, dry matter and soil moisture content under actual growth conditions. The model is validated for its performance to simulate the crop canopy, dry matter, soil moisture balance grain yield (using normalized water productivity function and dry matter and harvest index). There are different statistical indices used to measure the performance or model goodness of fit such as the Root Mean Square Error (RMSE), normalized-root mean square error, index of agreement (d), model efficiency (E) and coefficient of determination (R2). Hence, application of Aqua crop model as a decision support tool to assess impact of climate change, water management strategies (rain-fed, soil water conservation practices like mulching and bunds), sowing date, plant spacing and fertilizer management strategies under different climatic conditions.

Keywords: Aqua crop; Field management; Crop canopy; Above ground biomass; Soil moisture content; Statistical indices