近日，我院程昊天副教授及美國北卡農工州立大學John N. Ng'ombe助理教授、韓國釜山國立大學Yejun Choi助理教授、贊比亞大學Thomson H. Kalinda教授、中國人民大學鄭適教授相助論文“Understanding the drivers of smallholder dairy cooperative participation in developing countries: Evidence from rural Zambia”在期刊Agricultural Systems在線發表。Agricultural Systems是農林科學的頂級學術期刊，位于農林科學大類Q1區，農林科學大類所有期刊中位列第二位（亦是中科院期刊分區一區Top），最新影響因子6.1。

全文引用：

Cheng, H., Ng’ombe, J. N., Choi, Y., Kalinda, T. H., & Zheng, S. (2025). Understanding the drivers of smallholder dairy cooperative participation in developing countries: Evidence from rural Zambia. Agricultural Systems, 224, 104261

中文摘要：

配景：小規模奶農是發展中國家的主要奶制品生産者之一。在贊比亞，他們貢獻了該國超過 80% 的牛奶産量，每年約達 8000 萬美元。了解影響小規模奶農加入相助社決策的因素對于提高該地區的相助社參與度和奶制品生産效率至關重要。

目的：本研究的主要目標是探究小規模奶農加入相助社決策的影響因素，同時比較隨機效應邏輯模型和隨機森林模型在識別這些因素方面的預測性能。

要领：從贊比亞 515 名農村小規模奶農那裏收集了數據。分析接纳隨機效應邏輯模型和隨機森林模型來識別影響奶農加入奶制品相助社決策的因素。

結果與結論：觀察到三個主要發現。首先，隨機森林模型展現出比隨機效應邏輯模型更精彩的預測准確性，這與現有文獻中關于機器學習技術增強預測能力的觀點一致。其次，從隨機效應邏輯模型中確定了幾個關鍵因素，包罗與相助辦公室的物理距離、教育水平和奶牛養殖經驗，這些因素對當前農民決定加入奶牛相助社的決策具有顯著影響。第三，隨機森林模型讲明，在未來的情景中，人口和經濟特征——特別是戶主年齡、家庭規模、奶牛總擁有量、撫養比和農業經驗——預計將是相助社成員資格的最重要預測因素。

意義：研究結果讲明，在發展中國家，有须要在農村農業社區四周設立相助社辦公室，以提高可及性並鼓勵相助社參與。政策應側重于通過政府和非政府舉措提高教育水平，並提供可獲取的知識來源，以促進相助社成員資格。解決富足農民不願加入相助社的問題，需要接纳有針對性的措施，好比激勵措施、開展宣傳活動，或者進行有重點的推廣事情，強調相助社成員身份在差异資源水平下的種種好處。

英文摘要：

CONTEXT: Smallholder dairy farmers are among the primary dairy producers in developing countries. In Zambia, they contribute more than 80 % of the country’s milk production, which amounts to approximately $80 million annually. Understanding the factors that influence smallholder dairy farmers’ decisions to join cooperatives is crucial for enhancing cooperative participation and improving dairy production efficiency in the region.

OBJECTIVE: The primary goal of this study is to investigate the determinants of smallholder dairy farmers’ decisions to join cooperatives, while also comparing the predictive performance of the random effects logit model and the random forest model in identifying these factors. METHODS: Data were collected from 515 rural smallholder dairy farmers in Zambia. The analysis utilizes a random effects logit model and a random forest model to identify the factors influencing farmers’ decisions to join dairy cooperatives.

RESULTS AND CONCLUSIONS: Three primary findings were observed. First, the RF model exhibited superior predictive accuracy compared to the random effects logit model, aligning with existing literature on the enhanced predictive capabilities of machine learning techniques. Second, several key factors, including physical proximity to cooperative offices, educational attainment, and dairy farming experience, were identified from the random effects logit model as significantly influencing current farmers’ decisions to join dairy cooperatives. Third, the random forest model indicated that demographic and economic characteristics—specifically age of the household head, household size, total cow ownership, dependency ratio, and farming experience—are expected to be the most influential predictors of cooperative membership in future scenarios.

SIGNIFICANCE: Findings suggest the need for establishing cooperative offices closer to rural farming communities in developing countries to enhance accessibility and encourage cooperative participation. Policies should focus on improving educational levels and providing accessible knowledge sources through governmental and non-governmental initiatives to foster cooperative membership. Addressing the reluctance of wealthier farmers to join cooperatives requires tailored interventions such as incentives, awareness campaigns, or targeted outreach efforts emphasizing the benefits of cooperative membership across different resource levels

文章鏈接：https://www.sciencedirect.com/science/article/abs/pii/S0308521X25000010