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This study examines the variations of climate variables and rice yield and quantifies the relationships among them using multiple linear regression, principal component analysis, and support vector machine (SVM) analysis in southwest Nigeria. The climate and yield data used was for a period of 36 years between 1980 and 2015. Similar to the observed decrease (P < 0.001) in rice yield, pan evaporation, solar radiation, and wind speed declined significantly. Eight principal components exhibited an eigenvalue > 1 and explained 83.1% of the total variance of predictor variables. The SVM regression function using the scores of the first principal component explained about 75% of the variance in rice yield data and linear regression about 64%. SVM regression between annual solar radiation values and yield explained 67% of the variance. Only the first component of the principal component analysis (PCA) exhibited a clear long-term trend and sometimes short-term variance similar to that of rice yield. Short-term fluctuations of the scores of the PC1 are closely coupled to those of rice yield during the 1986-1993 and the 2006-2013 periods thereby revealing the inter-annual sensitivity of rice production to climate variability. Solar radiation stands out as the climate variable of highest influence on rice yield, and the influence was especially strong during monsoon and post-monsoon periods, which correspond to the vegetative, booting, flowering, and grain filling stages in the study area. The outcome is expected to provide more in-depth regional-specific climate-rice linkage for screening of better cultivars that can positively respond to future climate fluctuations as well as providing information that may help optimized planting dates for improved radiation use efficiency in the study area.
The management of water resources in a river basin experiencing the expansion of agricultural activities requires a proper understanding of impacts on its hydrologic cycle. This study focused on the analysis of impacts of infield rainwater harvesting (IRWH) and future agricultural expansion as land and water uses change (LWUC) on the hydrologic cycle in the Wami River basin (Tanzania) using the Soil and Water Assessment Tool (SWAT). In the SWAT model, IRWH was implemented by fragmenting rainwater harvesting hydrological response units (HRUs) from cropland HRUs and assigning them as potholes for rainwater impoundment. LWUC was implemented by customizing land cover types and their corresponding model parameters in all original HRUs, and introducing projected water uses in the model. The study thus demonstrated the successful modelling of IRWH and land use change in the SWAT model using HRU fragmentation and customization approaches, respectively. The results indicated that IRWH applications in croplands led to a large increase in evapotranspiration (ET) and the soil water content, and a decrease in percolation, especially in the dry years. However, the average annual streamflow showed negligible changes when IRWH was implemented, even in 50% of current low-coverage croplands in the river basin. Thus, IRWH applications in the river basin are recommended. The results also indicated that LWUC caused huge changes in ET, the soil water content, percolation and the streamflow from the river basin. The average annual streamflow was predicted to decrease by 26% due to LWUC. However, land use change alone without projected water uses was predicted to cause a minor decrease of about 1% in the average annual streamflow. Therefore, further studies on the eco-hydrology of the river basin under various water use scenarios are recommended prior to the expansion of agricultural areas.