Can medium-scale farms support smallholder commercialisation and improve welfare? Evidence from Nigeria

In spite of mounting evidence about the growth of medium-scale farms (MSFs) across Africa, there is limited empirical evidence on their impact on neighbouring small-scale farms (SSFs). We examine the relationships between MSFs and SSFs, with particular focus on the specific mechanisms driving potential spillover effects. First, we develop a theoretical model explaining two propagating mechanisms: learning effects (training) and cost effects (reduced transactions cost). An empirical application to data from Nigeria shows that SSFs with training from MSFs tend to use higher levels of modern inputs (have higher productivity), and receive higher prices and income. The results also show that purchasing inputs from MSFs reduces the costs of accessing modern inputs and is associated with higher inorganic fertiliser use by SSFs. Our results suggest that the benefits of receiving training and purchasing inputs from MSFs are particularly important for very small-scale producers, operating less than 1 hectare of land. This implies that policies which promote the efficient operation of MSFs and encourage their interaction with SSFs can be an effective mechanism for improving the productivity and welfare of smallholder farms, hence reducing their vulnerability to extreme poverty.     

流转地经营权稳定性对家庭农场耕地保护行为的影响——以增施有机肥及测土配方施肥为例

目的 从法律、事实和感知3个维度阐释和表征流转地经营权稳定性，实证分析流转地经营权稳定性对家庭农场耕地保护行为的影响及影响的分样本异质性。方法 文章采用二元Proibt回归对山东省312家种植类家庭农场的微观样本进了行实证检验；为规避有机肥和测土配方施肥两类行为的替代互补效应，采用Mvprobit回归进行了模型稳健性检验。结果 （1）流转地经营权稳定性对家庭农场采纳耕地保护措施有积极影响，具体而言，增加正式合同签订备案率、减少流转地权能限制、提升流转地续约预期，均显著提高了家庭农场增施有机肥和测土配方施肥的概率。（2）流转地经营权稳定性对不同特征家庭农场的影响存在分化，更能激励规模较大、种植果蔬类作物和风险偏好程度较高家庭农场采纳耕地保护行为。（3）除地权稳定性变量外，农场纯收入、耕地质量、机械化适应度、“三品一标”认证和有机肥补贴对家庭农场采纳耕地保护措施亦产生显著影响。结论 为激励家庭农场开展耕地质量保护，应聚焦规范合同签订、保障权能完整和提升续约预期三方面政策，保证流转地经营权的稳定性。并且在激励家庭农场开展耕地保护实践时，要特别关注不同群体的响应分化。     

Analysing inefficiency in a non-parametric spatial-dynamic by-production framework: A k-nearest neighbour proposal

This paper accounts for spatial effects by benchmarking farms against their k-nearest neighbours (KNN) and measuring their inefficiency in a non-parametric dynamic by-production setting. The optimal number of neighbours $k$ against which farms are compared corresponds to the value of $k$ that maximises the Moran I test for spatial autocorrelation of the good and the bad output of the farms' two sub-technologies. The inefficiency scores for farms' good output, variable inputs, investments and bad outputs are then computed and compared with those calculated based on a global technology, which benchmarks all farms together. The application focuses on an unbalanced panel of specialised Dutch dairy farms over the period 2009–2016 that contains information on their exact geographical locations. The results suggest that the inefficiency scores exhibit statistically significant differences between the KNN and the global model. Specifically, the inefficiencies are generally deflated when a KNN technology is considered, suggesting that ignoring spatial effects can overestimate inefficiency.