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首页> 外文期刊>Tropical Animal Health and Production >Machine learning models for predicting the use of different animal breeding services in smallholder dairy farms in Sub-Saharan Africa
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Machine learning models for predicting the use of different animal breeding services in smallholder dairy farms in Sub-Saharan Africa

机译：机器学习模型，用于预测亚撒哈拉非洲小农乳制品农场不同动物养殖服务

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摘要

This study is concerned with developing predictive models using machine learning techniques to be used in identifying factors that influence farmers' decisions, predict farmers' decisions, and forecast farmers' demands relating to breeding service. The data used to develop the models comes from a survey of small-scale dairy farmers from Tanzania (n = 3500 farmers), Kenya (n = 6190 farmers), Ethiopia (n = 4920 farmers), and Uganda (n = 5390 farmers) and more than 120 variables were identified to influence breeding decisions. Feature engineering process was used to reduce the number of variables to a practical level and to identify the most influential ones. Three algorithms were used for feature selection, namely: logistic regression, random forest, and Boruta. Subsequently, six predictive models, using features selected by feature selection method, were tested for each country-neural network, logistic regression, K-nearest neighbor, decision tree, random forest, and Gaussian mixture model. A combination of decision tree and random forest algorithms was used to develop the final models. Each country model showed high predictive power (up to 93%) and are ready for practical use. The use of ML techniques assisted in identifying the key factors that influence the adoption of breeding method that can be taken and prioritized to improve the dairy sector among countries. Moreover, it provided various alternatives for policymakers to compare the consequences of different courses of action which can assist in determining which alternative at any particular choice point had a high probability to succeed, given the information and alternatives pertinent to the breeding decision. Also, through the use of ML, results to the identification of different clusters of farmers, who were classified based on their farm, and farmers' characteristics, i.e., farm location, feeding system, animal husbandry practices, etc. This information had significant value to decision-makers in finding the appropriate intervention for a particular cluster of farmers. In the future, such predictive models will assist decision-makers in planning and managing resources by allocating breeding services and capabilities where they would be most in demand.
机译：本研究涉及利用机器学习技术开发预测模型，以识别影响农民决策的因素，预测农民的决策，预测农民与育种服务有关的要求。用于开发模型的数据来自坦桑尼亚（N = 3500名农民），肯尼亚（N = 6190名农民），埃塞俄比亚（N = 4920名农民）和乌干达（N = 5390名农民）的小型乳业农民的调查并确定了超过120个变量来影响育种决策。功能工程过程用于将变量数量减少到实际水平，并识别最有影响力的变量。三种算法用于特征选择，即：Logistic回归，随机林和Boruta。随后，针对每个国家 - 神经网络，逻辑回归，K最近邻，决策树，随机林和高斯混合模型测试了六种预测模型使用特征选择方法选择的特征。决策树和随机林算法的组合用于开发最终模型。每个国家模型都显示出高预测力量（高达93％）并准备好实际使用。使用ML技术辅助识别影响可以采用和优先考虑改善国家乳制品部门的育种方法的关键因素。此外，它为政策制定者提供了各种替代方案，以比较可以协助确定任何特定选择点的替代方案的不同行动课程的后果，鉴于与育种决策相关的信息和替代方案，可以获得高概率。此外，通过使用ML，结果鉴定了基于农场的不同农民群，以及农民的特征，即农场地点，饲养系统，畜牧业实践等。此信息具有重要价值决策者寻找适当的农民群体干预。在未来，这种预测模型将通过分配育种服务和能力来协助决策者规划和管理资源，在那里他们最需要的能力。

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