Many countries experience the negative impacts of climate change especially in the decline of agricultural productivity leading to decreased national and household food security. This study assessed smallholder farmers' perception of climate variability and change and their adaptation strategies in Masaba South Sub-County, Kisii County, Kenya. A multi-stage sampling technique was used to collect data from 196 smallholder farmers. Additionally, focused group discussions and key informant interviews were used. The study revealed that most farmers perceived climate changes. 88.3% of the respondents noted a decrease in rainfall, 79.1% reported poor rainfall distribution, 88.3% perceived a late onset of rainfall while 76.6% perceived an increase in temperature. The farmers' perception mirrored the actual climatic data trends for the area obtained from the meteorological department. The major climate-smart agriculture practices adopted by farmers in the area included; diversification of crops, change of planting time and crop rotation/mixed cropping. The adoption of climate-smart agriculture practices significantly correlated with the household size, monthly income, access to credit and farmers’ perception of climate change. The study recommends the incorporation and prioritization of climate change in the county and government development agenda as a means of enhancing the uptake of climate-smart agricultural practices.

Laser-induced breakdown spectroscopy (LIBS) and visible near-infrared spectroscopy (vis-NIRS) are spectroscopic techniques that offer promising alternatives to traditional laboratory methods for the rapid and cost-effective determination of soil properties on a large scale. Despite their individual limitations, combining LIBS and vis-NIRS has been shown to enhance the prediction accuracy for the determination of soil properties compared to single-sensor approaches. In this study, we used a comprehensive Danish national-scale soil dataset encompassing mostly sandy soils collected from various land uses and soil depths to evaluate the performance of LIBS and vis-NIRS, as well as their combined spectra, in predicting soil organic carbon (SOC) and texture. Firstly, partial least squares regression (PLSR) models were developed to correlate both LIBS and vis-NIRS spectra with the reference data. Subsequently, we merged LIBS and vis-NIRS data and developed PLSR models for the combined spectra. Finally, interval partial least squares regression (iPLSR) models were applied to assess the impact of variable selection on prediction accuracy for both LIBS and vis-NIRS. Despite being fundamentally different techniques, LIBS and vis-NIRS displayed comparable prediction performance for the investigated soil properties. LIBS achieved a root mean square error of prediction (RMSEP) of <7% for texture and 0.5% for SOC, while vis-NIRS achieved an RMSEP of <8% for texture and 0.5% for SOC. Combining LIBS and vis-NIRS spectra improved the prediction accuracy by 16% for clay, 6% for silt and sand, and 2% for SOC compared to single-sensor LIBS predictions. On the other hand, vis-NIRS single-sensor predictions were improved by 10% for clay, 17% for silt, 16% for sand, and 4% for SOC. Furthermore, applying iPLSR for variable selection improved prediction accuracy for both LIBS and vis-NIRS. Compared to LIBS PLSR predictions, iPLSR achieved reductions of 27% and 17% in RMSEP for clay and sand prediction, respectively, and an 8% reduction for silt and SOC prediction. Similarly, vis-NIRS iPLSR models demonstrated reductions of 6% and 4% in RMSEP for clay and SOC, respectively, and a 3% reduction for silt and sand. Interestingly, LIBS iPLSR models outperformed combined LIBS-vis-NIRS models in terms of prediction accuracy. Although combining LIBS and vis-NIRS improved the prediction accuracy of texture and SOC, LIBS coupled with variable selection had a greater benefit in terms of prediction accuracy. Future studies should investigate the influence of reference method uncertainty on prediction accuracy.

Evaluating the performance of handheld LIBS for determination of soil properties across a continental scale.