Use of classification and regression trees (CART) for analyzing determinants of winter wheat yield variation among fields in Poland

Abstract

SummaryWheat is one of the modern world’s staple food sources. Its production requires good environmental conditions, which are not always available. However, agricultural practices may mitigate the effects of unfavorable weather or poor-quality soils. The influence of environmental and crop management variables on yield can be evaluated only based on representative long-term data collected on farms through well-prepared surveys.The authors of this work analyzed variation in winter wheat yield among 3868 fields in western and eastern Poland for 12 years, as dependent on both soil/weather and crop management factors, using the classification and regression tree (CART) method. The most important crop management deficiencies which may cause low wheat yields are insufficient use of fungicides, phosphorus deficiency, non-optimal date of sowing, poor quality of seeds, failure to apply herbicides, lack of crop rotation, and use of cultivars of unknown origin not suitable for the region. Environmental variables of great importance for the obtaining of high yields include large farm size (10 ha or larger) and good-quality soils with stable pH. This study makes it possible to propose strategies supporting more effective winter wheat production based on the identification of characteristics that are crucial for wheat cultivation in a given region.

Analysis of the Impact of Environmental and Agronomic Variables on Agronomic Parameters in Soybean Cultivation Based on Long-Term Data

“…The response of the dependent variable is explained by a set of independent continuous or categorical variables. The produced result for regression trees consists of a set of rules used for predicting the outcome continuous variable [ 51 ]. Here, the dependent variable was soybean yield, and the independent variables were the treatments and parameters investigated within the field trials.…”

“…Here, the dependent variable was soybean yield, and the independent variables were the treatments and parameters investigated within the field trials. Based on the optimal tree, the importance of each independent variable in the final CART tree can be calculated [ 26 , 51 ].…”

The Analysis of Wheat Yield Variability Based on Experimental Data from 2008–2018 to Understand the Yield Gap

“…This is how all independent variables are ranked. The independent variable that causes the greatest reduction in variance of the dependent variable wins, and it is later presented in the final CART tree [9,24,25]. There are many agronomic studies where the CART method was successfully used [8,[26][27][28][29][30].…”

“…The yield of winter wheat in Poland has been steadily improving for last several years due to cultivar genetic improvement and more intensive agricultural practices, but remains very low in comparison to wheat yield obtained in western Europe [9,10]. Low values of yield gaps are mainly seen in northwestern Europe in contrary to south-western and eastern Europe [6,11].…”

Diversified Farming Systems – An evaluation of ecological benefits, economic costs & risks farmers face

“…This shows that high quality soils result in lower costs, which leads us to the conclusion that the implementation of cover crops and direct seeding on high quality soils can be generally considered less expensive. From a political point of view, it can be assumed that cover crops and direct seeding on good sites is more favorable and, therefore, more likely to be accepted by farmers (Bergtold et al, 2017;Iwańska et al, 2018). On poor sites, cover crops and direct seeding leads to high costs for the farmers, which are already in an economically less favorable position, and are viewed rather skeptically.…”