Drivers of maize yield variability at household level in northern Ghana and Malawi

Open Access Journal; Published online: 03 Jul 2023Maize is a staple food, but productivity has stagnated due to limited access to advanced farming methods and knowledge. To promote sustainable agriculture, understanding the factors affecting maize yield at the farm level is crucial. This study used panel data on maize yield and agronomic practices in Northern Ghana and Malawi from 2014 to 2020. Satellite-based environmental variables were extracted at household locations, and Random Forest modeling was used to identify factors influencing maize yield variability. The models performance was sub-par with low R2 values (∼0.1 and ∼0.24 for Northern Ghana and Malawi). Fertilizer and precipitation were the most important factors explaining maize yield variability. Spatial maps showed that Malawi’s maize yield can increase with more fertilizer, but rainfall is essential. In Northern Ghana, relying solely on fertilizer may not be enough to boost maize production

Satellite-based modelling of potential tsetse (Glossina pallidipes) breeding and foraging sites using teneral and non-teneral fly occurrence data

BACKGROUND: African trypanosomiasis, which is mainly transmitted by tsetse flies (Glossina spp.), is a threat to public health and a significant hindrance to animal production. Tools that can reduce tsetse densities and interrupt disease transmission exist, but their large-scale deployment is limited by high implementation costs. This is in part limited by the absence of knowledge of breeding sites and dispersal data, and tools that can predict these in the absence of ground-truthing. METHODS: In Kenya, tsetse collections were carried out in 261 randomized points within Shimba Hills National Reserve (SHNR) and villages up to 5 km from the reserve boundary between 2017 and 2019. Considering their limited dispersal rate, we used in situ observations of newly emerged flies that had not had a blood meal (teneral) as a proxy for active breeding locations. We fitted commonly used species distribution models linking teneral and non-teneral tsetse presence with satellite-derived vegetation cover type fractions, greenness, temperature, and soil texture and moisture indices separately for the wet and dry season. Model performance was assessed with area under curve (AUC) statistics, while the maximum sum of sensitivity and specificity was used to classify suitable breeding or foraging sites. RESULTS: Glossina pallidipes flies were caught in 47% of the 261 traps, with teneral flies accounting for 37% of these traps. Fitted models were more accurate for the teneral flies (AUC = 0.83) as compared to the non-teneral (AUC = 0.73). The probability of teneral fly occurrence increased with woodland fractions but decreased with cropland fractions. During the wet season, the likelihood of teneral flies occurring decreased as silt content increased. Adult tsetse flies were less likely to be trapped in areas with average land surface temperatures below 24 °C. The models predicted that 63% of the potential tsetse breeding area was within the SHNR, but also indicated potential breeding pockets outside the reserve. CONCLUSION: Modelling tsetse occurrence data disaggregated by life stages with time series of satellite-derived variables enabled the spatial characterization of potential breeding and foraging sites for G. pallidipes. Our models provide insight into tsetse bionomics and aid in characterising tsetse infestations and thus prioritizing control areas. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13071-021-05017-5

Accuracy Assessment of the ESA CCI 20M Land Cover Map: Kenya, Gabon, Ivory Coast and South Africa

This working paper presents the overall and spatial accuracy assessment of the European Space Agency (ESA) 20 m prototype land cover map for Africa for four countries: Kenya, Gabon, Ivory Coast and South Africa. This accuracy assessment was undertaken as part of the ESA-funded CrowdVal project. The results varied from 44% (for South Africa) to 91% (for Gabon). In the case of Kenya (56% overall accuracy) and South Africa, these values are largely caused by the confusion between grassland and shrubland. However, if a weighted confusion matrix is used, which diminishes the importance of the confusion between grassland and shrubs, the overall accuracy for Kenya increases to 79% and for South Africa, 75%. The overall accuracy for Ivory Coast (47%) is a result of a highly fragmented land cover, which makes it a difficult country to map with remote sensing. The exception was Gabon with a high overall accuracy of 91%, but this can be explained by the high amount of tree cover across the country, which is a relatively easy class to map

Evidence-based advice on timing and location of tsetse control measures in Shimba Hills National reserve, Kenya

Controlling tsetse flies is critical for effective management of African trypanosomiasis in Sub-Saharan Africa. To enhance timely and targeted deployment of tsetse control strategies a better understanding of their temporal dynamics is paramount. A few empirical studies have explained and predicted tsetse numbers across space and time, but the resulting models may not easily scale to other areas. We used tsetse catches from 160 traps monitored between 2017 and 2019 around Shimba Hills National Reserve in Kenya, a known tsetse and trypanosomiasis hotspot. Traps were divided into two groups: proximal ( 1.0 km) from the outer edge of the reserve boundary. We fitted zero-inflated Poisson and generalized linear regression models for each group using as temporal predictors rainfall, NDVI (Normalized Difference Vegetation Index), and LST (land surface temperature). For each predictor, we assessed their relationship with tsetse abundance using time lags from 10 days up to 60 days before the last tsetse collection date of each trap. Tsetse numbers decreased as distance from the outside of reserve increased. Proximity to croplands, grasslands, woodlands, and the reserve boundary were the key predictors for proximal traps. Tsetse numbers rose after a month of increased rainfall and the following increase in NDVI values but started to decline if the rains persisted beyond a month for distant traps. Specifically, tsetse flies were more abundant in areas with NDVI values greater than 0.7 for the distant group. The study suggests that tsetse control efforts beyond 1.0 km of the reserve boundary should be implemented after a month of increased rains in areas having NDVI values greater than 0.7. To manage tsetse flies effectively within a 1.0 km radius of the reserve boundary, continuous measures such as establishing an insecticide-treated trap or target barrier around the reserve boundary are needed

Use of earth observation satellite data to guide the implementation of integrated pest and pollinator management (IPPM) technologies in an avocado production system

Insect pollinators provide an important ecosystem service by improving agricultural productivity. However, their populations have been declining in recent years due to excessive use of synthetic pesticides, climate and land use/land cover (LULC) changes. Climate and LULC changes have resulted in land fragmentation and consequently pollinator habitat loss. To conserve pollinators, there is a need for sustainable agricultural practices such as integrated pest and pollinator management (IPPM), which is a holistic landscape management approach that minimizes pesticides use while conserving pollinator abundance and diversity. This study aimed to use earth observation (EO) data to characterize landscape dynamics in terms of vegetation productivity to guide the implementation of IPPM interventions in an avocado production system in Murang'a (Kenya). Specifically, we utilized Sentinel-2 (S-2)-derived normalized difference vegetation index (NDVI) as a proxy for vegetation productivity to assess IPPM implementation sites. The NDVI was calculated using multi-date S-2 data acquired during the dry and wet seasons and categorized into three vegetation productivity classes - low, medium, and high - using a K-means unsupervised clustering method. We also collected socio-economic baseline data from 410 farmers with a focus on their perception to implement one of four avocado pest management and pollination options: (1) IPPM, (2) integrated pest management (IPM), (3) pollinator supplementation (P), and (4) no intervention (control). The three landscape vegetation productivity classes were then linked with the four farmer preferences with regards to the implementation options. Criteria based on the distances among the sites for implementing the different four options were set for farmer selection and the experiment was replicated three times in each vegetation productivity class (i.e. in total 12 farmers in each class). The results showed that the K-means method was successful in characterizing the landscape vegetation productivity with an overall accuracy of 86.2%. One the other hand, we successfully selected the 36 (12 in each of the 3 vegetation productivity classes) out of 410 farmers who met our distance-based criteria and participated in the implementation of one of the four technology options (i.e. IPPM, IPM, P, and control). In conclusion, NDVI proved to be a vital proxy for assessing the landscape dynamics as it provided a robust view of vegetation productivity patterns, which enabled a well-representative distribution of the four pest management and pollination options across the landscape. Overall, the study shows the utility of integrating EO and socio-economic data in selecting sites for implementing agro-technologies at a landscape scale