How much control do smallholder maize farmers have over yield?
How much control do smallholder maize farmers have over yield?
复制标题
小农玉米种植者对产量有多少控制权？
DOI：
10.1016/j.fcr.2023.109014
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发表时间：
2023
影响因子：
5.8
通讯作者：
Konar, Megan
中科院分区：
文献类型：
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作者：
Cecil, Michael;Chilenga, Allan;Chisanga, Charles;Gatti, Nicolas;Krell, Natasha;Vergopolan, Noemi;Baylis, Kathy;Caylor, Kelly;Evans, Tom;Konar, Megan
小农农业对当前和未来的粮食安全至关重要，但量化小农产量差异的来源仍然是一项重大挑战。将产量差异归因于农民管理，而不是土壤和天气的限制，是理解农民决策影响的重要一步，因为小农使用了广泛的管理实践，获得肥料的机会可能有限。本研究使用基于过程的作物模型来模拟赞比亚地区小农玉米（Zea mays）的产量，并量化归因于土壤、天气和三种管理投入（品种、肥料、种植日期）的产量方差百分比（效应大小）。效应量通过方差分析计算。此外，为了更好地理解管理实践的治疗效果，计算了所有年份和单个年份的效应量。我们发现，农民管理决策解释了赞比亚不同农业生态区总产量差异的27 - 82%，主要是由于肥料的影响。化肥解释了平均地区45%的产量差异，尽管其影响在通常具有较高降水的赞比亚北部地区要大得多，在那里它解释了平均产量差异的72%。在确定特定肥料用量时，不同种植日期和品种的“低成本”管理方案解释了20 - 28%的产量差异，并存在一定的区域差异。为了更好地理解为什么管理实践在特定年份对产量的影响更大，我们进行了相关分析，将年度管理效应大小与四个基于气象学的变量进行了比较：生长季总降水量、雨季开始、极端高温天数和最长干旱期。结果表明：有利天气条件下，化肥的影响普遍增大，不利天气条件下，植树期的影响普遍增大。本研究展示了如何利用国家产量方差分解来了解具体管理干预措施在哪些地方会产生更大的影响，并为政策制定者提供土壤、天气和管理效果的量化。此外，方差组成可以很容易地适应不同范围的管理投入，如其他品种或肥料数量，也可以用来评估气候变化下管理适应的效应大小。
Smallholder agriculture is critical for current and future food security, yet quantifying the sources of smallholder yield variance remains a major challenge. Attributing yield variance to farmer management, as opposed to soil and weather constraints, is an important step to understanding the impact of farmer decision-making, in a context where smallholder farmers use a wide range of management practices and may have limited access to fertilizer. This study used a process-based crop model to simulate smallholder maize (Zea mays) yield at the district-level in Zambia and quantify the percent of yield variance (effect size) attributed to soil, weather, and three management inputs (cultivar, fertilizer, planting date). Effect sizes were calculated via an ANOVA variance decomposition. Further, to better understand the treatment effects of management practices, effect sizes were calculated both for all years combined and for individual years. We found that farmer management decisions explained 27–82 % of total yield variance for different agro-ecological regions in Zambia, primarily due to fertilizer impact. Fertilizer explained 45 % of yield variance for the average district, although its effect was much larger in northern districts of Zambia that typically have higher precipitation, where it explained 72 % of yield variance on average. When fixing a specific fertilizer amount, the “low-cost” management options of varying planting dates and cultivars explained 20–28 % of yield variance, with some regional variation. To better understand why management practices impact yield more in particular years, we performed a correlation analysis comparing yearly management effect sizes with four meteorologically based variables: total growing season precipitation, rainy season onset, extreme heat degree days, and longest dry spell. Results showed that fertilizer’s impact generally increased under favorable weather conditions, and planting date’s impact increased under adverse weather conditions. This study demonstrates how a national yield variance decomposition can be used to understand where specific management interventions would have a greater impact and can provide policymakers with quantification of soil, weather, and management effects. In addition, the variance composition can easily be adapted to a different range of management inputs, such as other cultivars or fertilizer quantities, and can also be used to assess the effect size of management adaptations under climate change.