Poor agricultural productivity has led to food shortages for smallholder farmers in Ethiopia. Agroforestry may improve food security by increasing soil fertility, crop production, and livelihoods. Agroforestry simulation models can be useful for predicting the effects of tree management on crop growth when designing modifications to these systems. The Agricultural Production Systems sIMulator (APSIM) agroforestry tree-proxy model was used to simulate the response of maize yield to N fertilizer applications and tree pruning practices in the parkland agroforestry system in the Central Rift Valley, Ethiopia. The model was parameterized and tested using data collected from an experiment conducted under trees and in crop-only plots during the 2015 and 2016 growing seasons. The treatments contained three levels of tree pruning (100% pruned, 50% pruned, and unpruned) as the main plots, and N fertilizers were applied to maize at two rates (9 or 78 kg N ha−1) as sub-plots. Maize yield predictions across two years in response to tree pruning and N applications under tree canopies were satisfactorily simulated (NSE = 0.72, RSR = 0.51, R2 = 0.8). Virtual experiments for different rates of N, pruning levels, sowing dates, and cultivars suggest that maize yield could be improved by applying fertilizers (particularly on crop-only plots) and by at least 50% pruning of trees. Optimal maize yield can be obtained at a higher rate of fertilization under trees than away from them due to better water relations, and there is scope for improving the sowing date and cultivar. Across a 34-year range of recent climate, small increases in yields due to optimum N-fertilizing and pruning were probably limited by nutrient limitations other than N, but the highest yields were consistently in the 2–4 m zone under trees. These virtual experiments helped to form hypotheses regarding fertilizers, pruning, and the effects of trees on soil that warrant further field evaluation.
Tag: fertilizer
Soil spatial variation to guide the development of fertilizer use recommendations for smallholder farms in western Kenya
A farm survey was conducted within a 100 km2 sampling block to collect data on the spatial variation in unfertilized maize biovolume and grain yields in relation to soil organic carbon (SOC), total nitrogen, phosphorus and extractable cations. Key soil factors associated with crop performance were identified using stepwise multiple linear regression modelling. The spatial variation of key soil factors and crop performance indicators (CPIs) was described in terms of spatial dependency. An analysis of variance indicated the variation explained by soil types, sampling units, and administrative units. Soil properties displayed high variability with coefficients of variation of in the range of 50% to 89% for extractable nutrients. Grain yield ranged widely from 0.1 to 11.3 t ha−1, with 31% of the variation being accounted for by measured soil properties. SOC was identified as key soil factor associated with variation in crop performance. SOC displayed moderate spatial dependency with a range of 523 m. Analysis of variation indicate that variation in SOC was sufficiently described by small spatial units (fields). These insights were used to provide a framework for determining an appropriate scale for developing digital soil maps or distance for soil sampling in heterogenous smallholder farming systems. Strategies aimed at refining fertilizer use recommendation can therefore use this guideline.