This study explored the suitability of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectrometer (MODIS) obtained for six sugar management zones, over nine years (2002–2010), to forecast sugarcane yield on an annual and zonal base. To take into account the characteristics of the sugarcane crop management (15-month cycle for a ratoon, accompanied with continuous harvest in Western Kenya), the temporal series of NDVI was normalized through an original weighting method that considered the growth period of the sugarcane crop (wNDVI), and correlated it with historical yield datasets. Results when using wNDVI were consistent with historical yield and significant at P-value = 0.001, while results when using traditional annual NDVI integrated over the calendar year were not significant. This correlation between yield and wNDVI is mainly drawn by the spatial dimension of the data set (R² = 0.53, when all years are aggregated together), rather than by the temporal dimension of the data set (R² = 0.1, when all zones are aggregated). A test on 2012 yield estimation with this model realized a RMSE less than 5 t·ha⁻¹. Despite progress in the methodology through the weighted NDVI, and an extensive spatio-temporal analysis, this paper shows the difficulty in forecasting sugarcane yield on an annual base using current satellite low-resolution data. This is particularly true in the context of small scale farmers with fields measuring less than the size of MODIS 250 m pixel, and in the context of a 15-month crop cycle with no seasonal cropping calendar. Future satellite missions should permit monitoring of sugarcane yields using image resolutions that facilitate extraction of crop phenology from a group of individual plots.

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.

The up-surging population in sub-Saharan Africa (SSA) has led to the conversion of more land for agricultural purposes. Resilient land utilization types that input carbon to the soil are key in enhancing climate change mitigation. However, there are limited data on different land utilization types’ contribution to climate mitigation through carbon input to soils. The study aims to quantify carbon stock across different land utilization types (LUT) practiced in Western Kenya. The following land utilization types were studied: agroforestry M (agroforestry with Markhamia lutea), sole sorghum, agroforestry L (agroforestry with Leucaena leucocephalaI), sole maize, and grazing land replicated thrice. To determine soil bulk density, SOC concentration, and soil carbon stock, soil samples were collected at depths of 0–5, 5–10, 10–20, and 20–30 cm from different LUTs. A PROC ANOVA was used to determine the difference in soil bulk density, SOC, and SOC stock between different LUTs and depths. The four variables differed across the LUTs and depths. A high soil bulk density was observed at 0–5 cm under grazing land (1.6 g cm⁻³) and the lowest under agroforestry M (1.30 g cm⁻³). Conversely, the soil bulk density was low at 20–30 cm under grazing land. The 0–5 cm depth accounted for a high share of SOC and SOC stock under Agroforestry M, while the 10–20 and 20–30 cm depth accounted for the high share of SOC stock under agroforestry L. The study showed differences in SOC across the different depths and LUTs. The findings highlight that agroforestry L and agroforestry M are promising interventions toward climate mitigation through carbon induction to soils.

The world is experiencing climate crisis that is threatening lives. Those in low lands are displaced by floods loosing possessions and food crops; while those in hilly areas experience land degradation and vegetation loss leading to poverty. Monitoring such occurrence through time is inevitable as it enables governments to make critical decisions for environmental conservation, enhanced resilience and minimize risks of food insecurity. This study explored the use of geospatial technology to investigate climate risk indicators within Nyando catchment. Geospatial technology provides a platform to monitor occurrences of extreme weather conditions, facilitates the understanding of end to end temporal changes of the environment and provides an environment for modelling future scenarios. We derived indicators of climate change within Nyando catchment over the period 2013 to 2021 and inferred their impact on livelihoods. We collected data on rainfall, vegetation cover and land use from Landsat 8 30m satellite images and in situ data using the Mobile mapper global positioning system (GPS) within Nyando catchment. We overlaid these data, characterized them and performed change detection using Erdas Imagine software. Results showed 9.9% reduction in forest cover, 54.1% sedimentation of water areas, 3.9% loss of agricultural land and 2.5% increase in built up areas. The main land use in Nyando catchment is agriculture followed with settlement. We inferred that loss of forest cover is the reason for sedimentation of the water areas, subjecting lowlands to flooding. Moreover, conversion of forest land to agricultural land and settlement were fund to be the main drivers for climate crisis. Indeed agro-ecological approaches to farming will increase forest area while maximizing on vertical farming for increased productivity to meet the ever growing demand for food. We recommend adoption of geospatial technology in monitoring of the landscape for informed decision making to avert risks of climate change.