Abstract
Over the recent past, there has been a growing concern on the need for mapping cropping practices in order to improve decision-making in the agricultural sector. We developed an original method for mapping cropping practices: crop type and harvest mode, in a sugarcane landscape of western Kenya using remote sensing data. At local scale, a temporal series of 15-m resolution Landsat 8 images was obtained for Kibos sugar management zone over 20 dates (April 2013 to March 2014) to characterize cropping practices. To map the crop type and harvest mode we used ground survey and factory data over 1280 fields, digitized field boundaries, and spectral indices (the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI)) were computed for all Landsat images. The results showed NDVI classified crop type at 83.3% accuracy, while NDWI classified harvest mode at 90% accuracy. The crop map will inform better planning decisions for the sugar industry operations, while the harvest mode map will be used to plan for sensitizations forums on best management and environmental practices.
Highlights
International agreements like the Rio Convention, as well as national legislation and regional policies, require the management of rural areas both for agricultural production and for other uses on a large scale
A color-composition of 15 m Normalized Difference Vegetation Index (NDVI) Landsat images is displayed in Figure 4 showing varied cropping practices such as fields with young crop whose germination commenced in May, those harvested in November, mature crop that is due for harvest and other cover crops within Kibos-Miwani
These results have revealed multiple planting and harvesting dates at pixel level, between fields in the area with different types of crops, vegetated and harvested fields exemplified on the image composite
Summary
International agreements like the Rio Convention, as well as national legislation and regional policies, require the management of rural areas both for agricultural production and for other uses (reduction of greenhouse gas emission, carbon sequestration, biodiversity conservation, etc.) on a large scale. A key advantage of remote sensing is the capability to perform synoptic, spatially continuous and frequent observations resulting in large data volumes and multiple datasets at varying spatial and temporal resolutions [1]. Farming practices directly affect the provision of ecosystem services. Mapping these practices is a challenge both for researchers in agro-ecology and decision-makers. Tillage systems for instance drastically impact on greenhouse gas emission by agriculture [2], and influence the development of classification method for mapping tillage practices at a regional scale [3]. In the case of sugar cane, mulching crop residues at harvest, instead of burning it, significantly contributes to sustained land productivity, increased organic matter in soils [4], and decrease in greenhouse-gas emission [5,6]
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