# Maize Cropping Systems Mapping Using RapidEye Observations in Agro-Ecological Landscapes in Kenya
## 5 Discussion

Table 5: Random forest classification confusion matrix for mapping cropping systems using the most important 15 RapidEye spectral variables and 30% of the reference data. cropping system classes could have numerous reasons such as heterogeneity of the landscape, variation in crop age (planting dates) and other agronomic practices (e.g., ploughing) [10; 61]. Field heterogeneity is largely affected by within field spectral variations that are larger than inter-field spectral variations due to the crop morphological and physiological properties [62]. This confusion is exacerbated by the amount of weed infestation per cropped area where a weed-infested mono-cropped maize field could exhibit a similar spatial arrangement, and thus spectral response, to a maize field inter-cropped with a legume like cowpea or bean [63]. Some sample mono-cropped fields in our study area were badly managed and infested by weeds that could have caused the spectral confusion with inter-cropped fields as previously mentioned. Another reason for heterogeneity and spectral confusion could be the fact that some farmers maintain trees within their fields and since we applied a per-pixel classification accuracy assessment, spectral confusion between trees and crops could have been exacerbated by this [16]. In other words, some mono-cropped and inter-cropped maize fields could have had similar spectral features as a result of woody vegetation that can be found within maize fields. Also, in Machakos, the majority of the farmers cultivate crops around hamlets, and in many cases the cultivated fields are surrounded by pockets of natural vegetation [64]. However, we employed an empirical classification approach that could have been more robust in terms of practical and operational cropping system mapping. In addition, since our mapping results were produced using the most important variables (Figure 4), we assumed that collinearity is somewhat accounted for [65]. Furthermore, we tested the collinearity between the indices selected for final mapping (RE1_NDVI, RE1_NDRE, RE2_DVledge, RE1_LCI and RE1_SIPE3) and found that they were not correlated. Maize is generally vulnerable to numerous pests and diseases [66]. Choosing appropriate cropping systems can be a valuable alternative to the use of synthetic pesticides [67]. For instance, inter-cropping has been used as a buffer against the spread of plant pests and pathogens by attracting pests away from their host plant and also increasing the distance between plants of the same species, making it more exigent for the pest to target their main crop [68]. A good example is inter-cropping maize with cowpea/beans has been proven to reduce the maize stem borer [69] and cowpea/bean thrips [70] densities significantly. As a result, accurate baseline information on cropping systems could be used to better understand the relationship between cropping patterns and pest and disease propagation mechanisms such as the occurrence of the maize lethal necrosis (MLN) disease, first reported in Kenya in 2012, which is hypothesized to be linked to the spatial distribution of the cropping systems [71]. Essentially, \"traditional\" agricultural land use mapping often renders information on the spatial distributions or averages of certain crops without further details of the actual underlying astronomical cropping systems [14]. Information on the cropping systems is vitally needed as a spatial descriptor (parameter) within commonly used crop modeling schemes such as the Decision Support System for Agrotechnology Transfer (DSSAT), since these crop systems are key determinants for agricultural production and food supply, given that mono-cropped systems generally may exhibit different yield cycles than mixed cropping systems. With the advent of new satellite constellations with better pixel and temporal resolution, not only crop mapping but also crop systems characterization can be performed. This will be of great use to crop scientists and decision makers.