{"title":"Measuring the estimation bias of yield response to N using combined on-farm experiment data","authors":"Qianqian Du, Taro Mieno, David S. Bullock","doi":"10.1002/jaa2.70015","DOIUrl":null,"url":null,"abstract":"<p>Accurately evaluating yield response to nitrogen is essential for increasing farm profitability. Data often come from randomized experiments, ensuring nitrogen is independent of other factors. However, when data from multiple experiments are combined, as many studies do, correlations between nitrogen and unobserved field heterogeneity can arise, potentially leading to biased results if the endogeneity problem is not addressed in regression analysis. This study examines the bias caused by this endogeneity using data from 41 large-scale on-farm precision experiments. We find that this bias can be both statistically and economically significant.</p>","PeriodicalId":93789,"journal":{"name":"Journal of the Agricultural and Applied Economics Association","volume":"4 3","pages":"321-332"},"PeriodicalIF":0.0000,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jaa2.70015","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Agricultural and Applied Economics Association","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jaa2.70015","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Accurately evaluating yield response to nitrogen is essential for increasing farm profitability. Data often come from randomized experiments, ensuring nitrogen is independent of other factors. However, when data from multiple experiments are combined, as many studies do, correlations between nitrogen and unobserved field heterogeneity can arise, potentially leading to biased results if the endogeneity problem is not addressed in regression analysis. This study examines the bias caused by this endogeneity using data from 41 large-scale on-farm precision experiments. We find that this bias can be both statistically and economically significant.