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Geostatistical analysis of fruit yield and detachment force in coffee

机译:咖啡中水果产量和脱离力的地统计学分析

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The aim of this study was to use geostatistical analysis to evaluate the spatial variation in the detachment force of coffee fruit and coffee yield by variograms and kriging for precision agriculture. This study was conducted at Brejão farm, Três Pontas, Minas Gerais, Brazil. The detachment force of green and mature coffee fruit was measured with a prototype dynamometer and georeferenced. The yield data were obtained from manual harvesting and were georeferenced. The data were evaluated by variograms estimated by residual maximum likelihood (REML), which provided a satisfactory approach for modeling all the variables with a small sample size. Spherical and exponential models were fitted, the first provided the better fit to mature fruit detachment force and the latter provided the better fit to coffee yield and green fruit detachment force. They were used to describe the structure and magnitude of spatial variation in the variables studied. Kriged estimates were obtained with the best fitting variogram models and mapped. The statistical and geostatistical analyses enabled us to characterize the spatial variation of the detachment force of green and mature coffee fruit and coffee yield and to visualize the spatial relations among these variables. The precision agriculture techniques used in this paper to collect, map and analyze the variables studied will help coffee farmers to manage their fields. Maps of coffee yield will enable farmers to apply nutrients site-specifically and manage harvesting either manually or mechanically. In addition, maps of detachment force of coffee fruit can enable farmers to harvest coffee selectively by choosing the appropriate places and the right time to start. This will improve the quality of the final product and also increase profits.
机译:这项研究的目的是使用地统计分析来通过变量图和克里格法对精密农业评估咖啡水果和咖啡产量的分离力的空间变化。这项研究是在巴西米纳斯吉拉斯州TrêsPontas的Brejão农场进行的。使用原型测功机测量绿色和成熟咖啡水果的脱离力并进行地理参考。产量数据是从人工收割获得的,并已地理参考。通过残差最大似然(REML)估计的方差图评估数据,这为使用小样本量的所有变量建模提供了令人满意的方法。拟合了球形和指数模型,第一个模型更适合成熟水果的脱离力,而第二个模型更适合咖啡产量和绿色水果的脱离力。它们被用来描述所研究变量中空间变化的结构和大小。使用最佳拟合变异函数模型获得Kriged估计值并进行映射。统计和地统计学分析使我们能够表征绿色和成熟咖啡果和咖啡产量的分离力的空间变化,并可视化这些变量之间的空间关系。本文使用的精确农业技术来收集,绘制和分析所研究的变量,将有助于咖啡农管理田地。咖啡产量图将使农民能够在特定地点应用养分并手动或机械管理收割。此外,咖啡果的分离力图可以使农民通过选择合适的地点和正确的开始时间来有选择地收获咖啡。这将改善最终产品的质量并增加利润。

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