Abstract
Abstract. Poor crop yields remain one of the main causes of chronic food insecurity in Africa. This is largely caused by insect pests, weeds, unfavourable climatic conditions and degraded soils. Weed and pest control, based on the climate-adapted ‘push-pull’ system, has become an important target for sustainable intensification of food production adopted by many small-holder farmers. However, essential baseline information using remotely sensed data is missing, specifically for the ‘push-pull’ companion crops. In this study, we investigated the spectral uniqueness of two of the most commonly used ‘companion’ crops (i.e. greenleaf Desmodium (Desmodium intortum) and Brachiaria (Brachiaria cv Mulato) with co-occurring soil, green maize, and maize stover. We used FieldSpec® Handheld 2™ analytical spectral device to collect in situ hyperspectral data in the visible and near-infrared region of the electromagnetic spectrum. Random forest was then used to discriminate among the different companion crops, green maize, maize stover and the background soil. Experimental ‘push-pull’ plots at the International Centre of Insect Physiology and Ecology (icipe) in Kenya were used as test sites. The in-situ hyperspectral reflectance data were resampled to the spectral waveband configurations of four multispectral sensors (i.e. Landsat-8, Quickbird, Sentinel-2, and WorldView-2) using spectral response functions. The performance of the four sensors to detect the ‘push-pull’ companion crops, maize and soil was compared. We were able to positively discriminate the two companion crops from the three potential background endmembers i.e. soil, green maize, and maize stover. Sentinel-2 and WorldView-2 outperformed (> 98% overall accuracy) Landsat-8 and Quickbird (96% overall accuracy), because of their added advantage of the strategically located red-edge bands. Our results demonstrated the unique potential of the relatively new multispectral sensors’ and machine learning algorithms as a tool to accurately discern companion crops from co-occurring maize in ‘push-pull’ plots.
Highlights
1.1 BackgroundFood security remains a challenge to millions of households in Africa and this is likely to worsen due to climate change and population growth (FAO, IFAD, UNICEF, WFP, 2019)
We investigated the spectral uniqueness of two most prominent companion plants used in the climate-adapted ‘push-pull’ system with the co-occurring green maize crop and maize stover; and examined the potential of using the relatively new multispectral sensors to detect their spectral characteristics
The Desmodium had the lowest reflectance in the red region of the spectrum, it had the steepest gradient in the red edge region compared to the Brachiaria and the green maize
Summary
1.1 BackgroundFood security remains a challenge to millions of households in Africa and this is likely to worsen due to climate change and population growth (FAO, IFAD, UNICEF, WFP, 2019). One of the main sources of the chronic food insecurity observed in Africa is poor crop yields, largely caused by insect pests, weeds, unfavourable climatic conditions, and degraded soils (Khan et al, 2016). In the ‘push-pull’ strategy, chosen companion plants are grown in between and around the main crop (Pickett et al, 2014), in our case it is maize. These companion plants release semiochemicals that (i) repel insect pests from the main crop using an intercrop which is the ‘push’ component; and (ii) attract insect pests away from the main crop using a trap crop which is the ‘pull’ component (Khan et al, 2016). Many small-scale farmers in Africa have welcomed and adopted the ‘push-pull’ system (Midega et al, 2015), identifying and evaluating the adoption rate and the improvement in yields has been conducted using rigorous and expensive field surveys, which have proven inconsistent, timeous and inaccurate (Khan et al, 2008; Midega et al, 2018)
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