Improving Cereal Production in the Terai Region of Nepal: Assessment of Field Management Strategies through a Model Based Approach

Rice (Oryza sativa L.), maize (Zea mays L.) and wheat (Triticum aestivum L.) are the major cereal crops in Nepal accounting for more than 95% of cereal production in Nepal. The focus of the research are these three cereal crops grown in the Terai region of Nepal, which contributes to more than 70% of national cereal production. However, yield is reported generally low as result of low rainfall and/or fertilizer applications. For improving the cereal production in Terai, a sound and balanced management of fertilizer and water application encompassing the local limitations and needs of farmers is required. The major objective of this research is to understand the impact of inorganic fertilizer and irrigation application on the yield of the cereal crops in Terai region of Nepal. To study the crop response, the AquaCrop model was selected and fine tuned for rice, maize and wheat. The calibrated and validated AquaCrop model was used to formulate a realistic water and fertility management for the grain crops to improve and stabilize the yield. The possibility to forecast the yield with the fine-tuned and validated AquaCrop model was also investigated as an objective. Field experiments were set up in Rampur (Chitwan), taken as a representative area of Terai, for two years (2009-2011) to investigate crop response of rice, wheat and maize for different fertilizer and water treatments and to collect data for calibration and validation of AquaCrop. From field experiments, it was observed that there was a significant increase in yield of all crops with higher fertilizer application. In the dry season, the water regime played a significant role in the increase of the above ground biomass and consequently final grain yields. However, the small sample size makes such conclusion speculative; the data collected from field experiments were used for fine-tuning the non-conservative crop parameters of rice, wheat and maize in AquaCrop to the local conditions in Terai (Chitwan). The calibrated model was able to simulate accurate soil water content, canopy development, dry aboveground biomass and grain yield in fertility stressed and non-stressed fields. The fine-tuned crop parameters were used to validate AquaCrop for different water and fertilizer treatments. The AquaCrop model was able to simulate accurately the effect of the different soil fertility levels on biomass production and ultimately crop yield for different water management (rainfed and irrigated) and various climatic conditions. The statistical analysis of the comparison between the observed and simulated final grain yields yielded very good Coefficients of Variation of Root Mean Squared Deviation (CV(RMSD)), Coefficients of Determination (R²) and Nash-Sutcliffe Efficiencies (EF) of respectively 0.05, 0.89 and 0.84 for rice, 0.10, 0.75 and 0.72 for wheat and 0.08, 0.97 and 0.96 for maize.A regional farmer household survey was performed to understand the local regional crop management. Analysis of the farmer household survey showed very low application of fertilizers for the grain crops. The amount of fertilizer used depended on the availability of the water. However, chemical fertilizer use was always below the National Recommended Fertilizer Dose (NRFD). The major cause of the lower use of chemical fertilizer was mainly due to an assumed negative effect (because of unbalanced use) and high costs. The amount of irrigation applied decreases as the dry season progresses. Irrigating in the dry seasons depended on the availability of groundwater.A regional soil survey was performed to determine the variation of the regional soil characteristics. Analysis of textural class, bulk density and soil organic matter (SOM) content were performed on the soil samples collected. Comparison among three pedo transfer functions showed that pedo transfer function developed by Saxton and Rawls (2006) has the lowest CV(RMSD) of 27%, and hence was chosen to determine the representative soil physical characteristics required by AquaCrop.The fine-tuned and validated AquaCrop model was used to simulate and develop different management scenarios for the Chitwan region. To reduce the simulation time, the soil was categorized into five soil classes based on the soil characteristics (total available water), which was most influential to crop yield. The analysis of the spatial variability of the rainfall and evapotranspiration in the region showed minimal variability. The climatic data from Rampur meteorological station was used to represent the Chitwan region. AquaCrop was run on the five soil classes to obtain realistic irrigation and soil fertility strategies for the region by considering the existing water availability and soil fertility constraints of the farmers. Results of the management strategies indicated that the monsoon rice yield was mainly constrained by soil fertility and can be increased with improved soil fertility management. For the crops in the dry seasons, yields were mainly constrained by water stress. The yields in the dry season can be stabilized and increased with deficit irrigation strategies. The amount of irrigation to be applied depended on the fertilizer application, so they should be managed accordingly to maximize the yield.Analysis of the ability of AquaCrop to perform simulations with 10-daily data showed that the average soil water content was well simulated and can be used to simulate the stress affecting the canopy cover and crop transpiration. However, in simulations where soil water content induced early crop senescence, the use of 10-daily rainfall data showed some discrepancy and underestimated the lower yields. Hence, 10-daily data was not considered for use in yield forecast. The simulations with historical daily data to predict the yield of maize within the season showed some promising results. The update of the climatic data as the season progresses allowed AquaCrop to predict the possibility of crop failure already by the mid of season. The process for yield prediction can be used for crops with similar drought sensitive stages. This willallow the farmer to react and the government to prepare for yield failure.status: publishe