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Understanding the adoption of system technologies in smallholder agriculture: the system of rice intensification (SRI) in Timor Leste.

机译:了解小农农业中系统技术的采用:东帝汶的稻米集约化(SRI)系统。

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Against the background of rising food demand, decreasing productivity growth, and environmental degradation, natural resource management technologies, such as the system of rice intensification (SRI), have been propagated, especially in a smallholder farm context. However, system technologies are often location specific and characterized by partial adoption and disadoption. Previous studies were often not able to fully explain this, because they mostly relied on farm and household level data, neglecting plot level differences that may be important. We address this limitation, using SRI adoption in Timor Leste as an example. Regression models are specified and estimated to explain the farmers' decision-making processes. Participation in training programs and household labor availability increase the probability and intensity of adoption, as SRI is knowledge and labor intensive. However, many other household variables are not significant, while plot level characteristics, such as proximity to the homestead, water control capacity, slope, and soil conditions, have more explanatory power. The results suggest that plot level data are important to understand the adoption of system technologies. Moreover, technology adaptation to different plot conditions seems to be a precondition for widespread diffusion.Digital Object Identifier http://dx.doi.org/10.1016/j.agsy.2012.01.003
机译:在粮食需求上升,生产率增长下降和环境恶化的背景下,自然资源管理技术(例如稻米集约化(SRI)系统)得到了推广,特别是在小农户的情况下。但是,系统技术通常是特定于位置的,并且具有部分采用和不采用的特征。以前的研究通常不能完全解释这一点,因为它们主要依靠农场和家庭的数据,而忽略了可能重要的地块水平差异。我们以东帝汶采用SRI为例解决此限制。指定回归模型并进行估计,以解释农民的决策过程。由于社会责任投资是知识和劳动密集型产业,因此参加培训计划和家庭劳动力可获得性增加了采用的可能性和强度。但是,许多其他家庭变量并不重要,而地块级别的特征(如距宅基地的距离,水控制能力,坡度和土壤条件)具有更大的解释力。结果表明,地块级数据对于了解系统技术的采用非常重要。此外,技术适应不同地块条件似乎是广泛传播的前提。数字对象标识符http://dx.doi.org/10.1016/j.agsy.2012.01.003

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