Targeting conservation agriculture in the context of livelihoods and landscapes
Targeting conservation agriculture in the context of livelihoods and landscapes
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在生计和景观的背景下瞄准保护性农业
DOI：
10.1016/j.agee.2013.11.011
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
E. Luedeling
中科院分区：
文献类型：
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作者：
T. Rosenstock;M. Mpanda;J. Rioux;E. Aynekulu;A. Kimaro;H. Neufeldt;K. Shepherd;E. Luedeling
在整个撒哈拉以南非洲地区推广保护性农业（CA）时，发展计划通常忽略了结果和社会生态环境方面的不确定性和可变性。我们开发了一个简单的基于蒙特卡洛的决策模型，校准到全球数据集和参数化的当地条件，预测农民在东非两个正在进行的农业发展项目中采用CA时可能获得的产量效益的范围。我们的通用模型预测采用CA相关措施的产量效应平均为-0.60 ± 2.05（sd）Mg玉米公顷-1年-1，表明对产量产生正面和负面影响的机会几乎相等。当使用特定地点、社会经济和生物物理数据时，产量的平均变化更负（-1.29和-1.34兆毫克公顷-1年-1）。此外，实际上，潜在产量影响的整个分布是负面的，这表明保护性耕作极不可能为这两个地区的农民带来产量效益。尽管在这两个网站的可比的总影响，如土地保有权，获得信息，牲畜压力的对比鲜明的因素，突出需要量化的生计和景观影响的范围时，评估技术的适用性。这一分析说明了将不确定性纳入对农业发展干预措施的快速评估的潜力。虽然本研究审查了关于一个特定干预措施的项目级决策，但该方法同样适用于解决多个干预措施、多个尺度和多个标准的决策（例如，因此，这是一个重要的工具，可以支持将知识与行动联系起来。
Development programs have typically neglected uncertainty and variability in terms of outcomes and socio-ecological context when promoting conservation agriculture (CA) throughout sub-Saharan Africa. We developed a simple Monte Carlo-based decision model, calibrated to global data-sets and parameterized to local conditions, to predict the range of yield benefits farmers may obtain when adopting CA in two ongoing agricultural development projects in East Africa. Our general model predicts the yield effects of adopting CA-related practices average −0.60 ± 2.05 (sd) Mg maize ha−1year−1, indicating a near equal chance of positive and negative impacts on yield. When using site-specific, socio-economic, and biophysical data, mean changes in yield were more negative (−1.29 and −1.34 Mg ha−1year−1). Moreover, practically the entire distributions of potential yield impacts were negative suggesting CA is highly unlikely to generate yield benefits for farmers in the two locations. Despite comparable aggregate effects at both sites, factors such as land tenure, access to information, and livestock pressure contrast sharply highlighting the need to quantify the range of livelihood and landscape effects when evaluating the suitability of the technology. This analysis illustrates the potential of incorporating uncertainty in rapid assessments of agricultural development interventions. Whereas this study examines project-level decisions on one specific intervention, the approach is equally relevant to address decision-making for multiple interventions, at multiple scales, and for multiple criteria (e.g., across ecosystem services), and thus is an important tool that can support linking knowledge with action.