Unravelling the effects of soil and crop management on maize productivity in smallholder agricultural systems of western Kenya—An application of classification and regression tree analysis

Abstract

To guide soil fertility investment programmes in sub-Saharan Africa, better understanding is needed of the relative importance of soil and crop management factors in determining smallholder crop yields and yield variability. Spatial variability in crop yields within farms is strongly influenced by variation in both current crop management (e.g. planting dates, fertilizer rates) and soil fertility. Variability in soil fertility is in turn strongly influenced by farmers' past soil and crop management. The aim of this study was to investigate the relative importance of soil fertility and crop management factors in determining yield variability and the gap between farmers' maize yields and potential yields in western Kenya. Soil fertility status was assessed on 522 farmers' fields on 60 farms and paired with data on maize-yield and agronomic management for a sub-sample 159 fields. Soil samples were analysed by wet chemistry methods (1/3 of the samples) and also by near infrared diffuse reflectance spectroscopy (all samples). Spectral prediction models for different soil indicators were developed to estimate soil properties for the 2/3 of the samples not analysed by wet chemistry. Because of the complexity of the data set, classification and regression trees (CART) were used to relate crop yields to soil and management factors. Maize grain yields for fields of different soil fertility status as classified by farmers were: poor, 0.5-1.1; medium, 1.0-1.8; high, 1.4-2.5 t ha À1. The CART analysis showed resource use intensity, planting date, and time of planting were the principal variables determining yield, but at low resource intensity, total soil N and soil Olsen P became important yield-determining factors. Only a small group of plots with high average grain yields (2.5 t ha À1; n = 8) was associated with use of nutrient inputs and good plant stands, whereas the largest group with low average yields (1.2 t ha À1; n = 90) was associated with soil Olsen P values of less than 4 mg kg À1. This classification could be useful as a basis for targeting agronomic advice and inputs to farmers. The results suggest that soil fertility variability patterns on smallholder farms are reinforced by farmers investing more resources on already fertile fields than on infertile fields. CART proved a useful tool for simplifying analysis and providing robust models linking yield to heterogeneous crop management and soil variables. #

Nutrient constraints to tropical agroecosystem productivity in long‐term degrading soils

“…However, high fertility is short-lived (Solomon et al, 2007) if the export of nutrients through harvest and leaching is not balanced by fertilizer input (Zingore et al, 2005;Lal, 2007). The depletion rates of specific nutrients depend on a number of factors including management, soil type and climate (Davidson & Ackerman, 1993;Wopereis et al, 2006;Ngoze, 2008;Tittonell et al, 2008).…”

Identification of commercial blocks of outstanding performance of sugarcane using data mining

“…The patterns associated with blocks of superior performance can be used to confirm that certain measures result in greater productivity, while low performance patterns indicate the need for improvement in management, or the identification of specific conditions that should be avoided (TANUSKA et al., 2012;VAZAN et al, 2011). The performance of a given block is considered to be the difference between the yield of the block and the average yield of the homogeneous group that it belongs to.…”

“…Despite the benefits of this type of analysis (LAWES & LAWN, 2005), the methods used to determine the potential yield groups and the identification of outstanding blocks are subjective, therefore, it is highly dependent of the interpretation of each specialist (TITTONELL et al, 2008).…”

“…In the search for more efficient agricultural production systems, research studies have been performed with the aim to identify patterns in environmental and management factors that influence the yield variability from block-to-block in diverse crops (ANDRIANASOLO et al, 2014;RENAUD-GENTIÉ et al, 2014;ZHANG, 2012;TITTONELL et al, 2008).…”

“…Given the large number of variables submitted for analysis in this type of study (TANUSKA et al., 2012;VAZAN et al, 2011;LAWES & LAWN, 2005), data mining techniques are a promising alternative due to their ability to address complex databases with noise (HAN et al, 2012), a common feature of commercial block databases. In particular, the regression tree induction technique is able to identify and rank the factors that influence a production system (WITTEN & FRANK, 2011), and may be useful for establishing potential yield groups to determine the performance of blocks.…”