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Soil and management-related factors contributing to maize yield gaps in western Kenya

机译:土壤与管理相关因素导致玉米玉米产量差距在西部肯尼亚

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The solution to reducing existing yield gaps on smallholder farms lies in understanding factors limiting yield in areas with agricultural intensification potential. This study applied an integrated analysis approach comprising Classification and Regression Tree (CART), generalized linear mixed model (GLMM), and factor analysis (FA), to explain soil and management-related factors influencing maize yield gaps, in order to enhance yields. The study was conducted in Mukuyu and Shikomoli in western Kenya, sites with, respectively, high and low agroecological potential regarding soil fertility. Maize yield gaps were quantified by comparing yields on the 90th percentile of farms to yields determined in 189 fields on 70 randomly sampled smallholdings. Soil and management-related factors were determined at early and late maize development stages. Maize yield on the 90th percentile of farms in Mukuyu and Shikomoli was 5.1 and 4.8 t/ha, respectively, and the average yield gap was 1.8 and 2.6 t/ha, representing 35% and 54% unachieved yield for Mukuyu and Shikomoli, respectively. In FA, soil was revealed to be the main factor influencing maize yield gaps at both sites, rather than management-related variables. The CART method identified maize density, chlorophyll values, maize height, and depth to compact layer as consistent factors affecting yield at both sites, while GLMM identified soil texture (silt content) as important. According to CART, weed cover at early stages and maize density at late stages were the most limiting factor in maize production in Mukuyu and Shikomoli, respectively. Generalized linear mixed model analysis identified agroecology-specific factors influencing maize yield gaps as soil-available phosphorus and zinc, plus weed pressure at early maize stages in Mukuyu, and plus soil cation exchange capacity and exchangeable magnesium in Shikomoli. Through an integrated approach, it was possible to identify both consistent and agroecology-specific factors limiting crop yields. This can increase the applicability of the findings to smallholder farms.
机译:降低小农农场现有收益差距的解决方案在于了解农业强化潜力区域限制产量的因素。本研究应用了一种综合分析方法,包括分类和回归树(推车),广义线性混合模型(GLMM)和因子分析(FA),以解释影响玉米产量间隙的土壤和管理相关因素,以提高产量。该研究在肯尼亚西部的穆库尤和Shikomoli进行,分别为土壤肥力的高和低生态学潜力。通过比较90百分之九个农场的产量在70个随机采样的小农的屈服中测量的90百分位数的产量来量化玉米产量差距。在早期和晚期玉米发展阶段确定了土壤和管理相关因素。 Mukuyu和Shikomoli的第90百分位农场玉米产量分别为5.1和4.8吨/公顷,平均产量差距为1.8和2.6吨/公顷,分别代表Mukuyu和Shikomoli的35%和54%未分别的产量。在FA中,土壤被揭示为影响两个站点的玉米产量间隙的主要因素,而不是与管理相关的变量。推车方法鉴定了玉米密度,叶绿素值,玉米高度和深度到紧凑层的深度,作为影响两个地点产量的一致因素,而GLMM确定了土壤质地(淤泥含量)重要。根据购物车,晚期的杂草覆盖在后期的早期阶段和玉米密度分别是Mukuyu和Shikomoli的玉米产量最有限的因素。广义线性混合模型分析确定了影响玉米产量差距作为土壤可用磷和锌的特异性因素,加上穆库鲁早期玉米阶段的杂草压力,以及什莫醇的土壤阳离子交换能力和可交换镁。通过综合方法,可以识别限制作物产量的一致和农业学特定因素。这可以将研究员的适用性提高到小农农场。

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