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With climate extreme events increasing in frequency and intensity in Malawi, the future of local food production faces serious threats, necessitating renewed efforts to build the adaptive capabilities of the majority poor smallholder farmers. In this context, seed security is critical to improving rural livelihoods and agrobiodiversity; however, knowledge of its role in climate change resilience is sparse. Drawing insights from vulnerability and resilience literature, this paper examines the role of seed security in enhancing climate change resilience in northern Malawi. Using a cross-sectional survey of 1,090 smallholder farmers and applying logistic regression analysis, the study found that households that are seed-secure were significantly more likely to report stronger resilience to climate change than those that were not seed-secure, even after controlling for theoretically relevant variables (OR = 1.89; p < .01). Other noteworthy predictors of climate change resilience included level of education, wealth, agroecological practice, and seed sources. Based on the findings, we advocate for promoting seed security as part of broader localized and place-specific action plans to foster resilience to climate change in agricultural regions. 

Land-use and local field management affect pollinators, pest damage and ultimately crop yields. Agroecology is implemented as a sustainable alternative to conventional agricultural practices, but little is known about its potential for pollination and pest management. Sub-Saharan Africa is underrepresented in studies investigating the relative importance of pests and pollinators for crop productivity and how this might be influenced by surrounding landscapes or agroecological practices. In Malawi, we selected 24 smallholder farms differing in landscape-scale shrubland cover, implementation of manual pest removal as an indicator of an agroecological pest management practice, and the number of agroecological soil practices employed at the household level, such as mulching, intercropping and soil conservation tillage. We established pumpkin plots and assessed the abundance and richness of flower visitors and damage of flowers (florivory) caused by pest herbivores on flowers. Using a full-factorial hand pollination and exclusion experiment on each plot, we investigated the relative contribution of pollination and florivory to pumpkin yield. Increasing shrubland cover decreased honeybee abundance but increased the abundance and richness of non-honeybee visitors. Manual removal of herbivores considered to be pests reduced flower visitors, whereas more agroecological soil management practices increased flower visitors. Neither shrubland cover nor agroecological management affected florivory. Pollinator limitation, but not florivory, constrained pumpkin fruit set, and increasing visitor richness decreased the relative differences between hand- and animal-pollinated flowers. We recommend improved protection of shrubland habitats and increasing agroecological soil practices to promote pollinator richness on smallholder farms. 

In the context of food insecurity in resource-poor settings, agroecology (AE) has emerged as an important approach promoted for improving crop productivity, yet few studies have demonstrated how a combination of agroecological methods can improve crop health and thereby crop productivity. Using a geospatial approach, this study investigated whether agroecological practices can improve crop health in smallholder contexts. WE compared leaf area indexes (LAIs) of crops on AEs and non AE-farms and prospectively predicted the impact of AE using vegetation indexes (VIs). We found that crops on AE farms produced higher average growing season LAIs for maize and pigeonpeas (1.28 m2/m2) and maize and beans (1.29 m2/m2) farms compared to 0.97 m2/m2 and 0.80 m2/m2, respectively, for the same crops on the non-AE farms. The higher LAIs suggest that the combination of farming strategies practiced on the AE farms produced healtheir crops on AE farms. Random forest regression prospective predictions generated statistically significant higher LAIs for maize and beans (R2 = 0.90, root mean square error (RMSE] = 0.32 m2/m2) and maize and pigeonpea (R2 = 0.88 m2/m2, RMSE = 0.42 m2/m2) on the AE farms, but predictions for the non-AE farms were not statistically significant. The findings demonstrate that combining AE strategies can potentially improve crop productivity to enhance household food security and income in smallholder contexts. 

Crop yield is related to household food security and community resilience, especially in smallholder agricultural systems. As such, it is crucial to accurately estimate within-season yield in order to provide critical information for farm management and decision making. Therefore, the primary objective of this paper is to assess the most appropriate method, indices, and growth stage for predicting the groundnut yield in smallholder agricultural systems in northern Malawi. We have estimated the yield of groundnut in two smallholder farms using the observed yield and vegetation indices (VIs), which were derived from multitemporal PlanetScope satellite data. Simple linear, multiple linear (MLR), and random forest (RF) regressions were applied for the prediction. The leave-one-out cross-validation method was used to validate the models. The results showed that (i) of the modelling approaches, the RF model using the five most important variables (RF5) was the best approach for predicting the groundnut yield, with a coefficient of determination (R2) of 0.96 and a root mean square error (RMSE) of 0.29 kg/ha, followed by the MLR model (R2 = 0.84, RMSE = 0.84 kg/ha); in addition, (ii) the best within-season stage to accurately predict groundnut yield is during the R5/beginning seed stage. The RF5 model was used to estimate the yield for four different farms. The estimated yields were compared with the total reported yields from the farms. The results revealed that the RF5 model generally accurately estimated the groundnut yields, with the margins of error ranging between 0.85% and 11%. The errors are within the post-harvest loss margins in Malawi. The results indicate that the observed yield and VIs, which were derived from open-source remote sensing data, can be applied to estimate yield in order to facilitate farming and food security planning. 

Deforestation drives climate change and reinforces food insecurity in forest dependent communities. What drives deforestation varies by location and is shaped by livelihood systems. But how locals perceive restoration is crucial for developing restoration policies. Evidence suggests that applying sustainable farming strategies can potentially restore forests and sustain livelihoods. Applying a broad-based conceptualization of deforestation and restoration in policymaking, however, results in missed opportunities for addressing deforestation and restoration. Here, we explore the drivers of deforestation, the perceptions of restoration, and the challenges to restoration among smallholder farmers in northern Malawi and examine how agroecology can contribute to restoring degraded agroecosystems. Participants report agricultural land expansion, charcoal production, climate change, burnt brick production, and government subsidies as the major drivers of deforestation. We observed that although perceptions of forest restoration reflect farmers' traditional ecological knowledge (TEK) to include reclamation of degraded farmlands, reconstruction of native tree species, and replacement of felled trees on farmlands, there are challenges including splitting families to gain access to more subsidized fertilizers and food aid, embedded cultural practices, growing demand for charcoal in cities, and weak ecosystem governance structures that hinder the effectiveness of restoration efforts. We, however, do find that agroecological intensification can increase yield from smaller farmlands and allow for larger and longer-lasting fallows of spare lands which regenerate forests. Key overarching implications of these findings include the need to integrate livelihoods more explicitly into restoration plans, accounting for TEK in restoration policies in forest-dependent communities and encouraging the adoption of agroecology. 

Mapping crop types and land cover in smallholder farming systems in sub-Saharan Africa remains a challenge due to data costs, high cloud cover, and poor temporal resolution of satellite data. With improvement in satellite technology and image processing techniques, there is a potential for integrating data from sensors with different spectral characteristics and temporal resolutions to effectively map crop types and land cover. In our Malawi study area, it is common that there are no cloud-free images available for the entire crop growth season. The goal of this experiment is to produce detailed crop type and land cover maps in agricultural landscapes using the Sentinel-1 (S-1) radar data, Sentinel-2 (S-2) optical data, S-2 and PlanetScope data fusion, and S-1 C2 matrix and S-1 H/α polarimetric decomposition. We evaluated the ability to combine these data to map crop types and land cover in two smallholder farming locations. The random forest algorithm, trained with crop and land cover type data collected in the field, complemented with samples digitized from Google Earth Pro and DigitalGlobe, was used for the classification experiments. The results show that the S-2 and PlanetScope fused image + S-1 covariance (C2) matrix + H/α polarimetric decomposition (an entropy-based decomposition method) fusion outperformed all other image combinations, producing higher overall accuracies (OAs) (>85%) and Kappa coefficients (>0.80). These OAs represent a 13.53% and 11.7% improvement on the Sentinel-2-only (OAs < 80%) experiment for Thimalala and Edundu, respectively. The experiment also provided accurate insights into the distribution of crop and land cover types in the area. The findings suggest that in cloud-dense and resource-poor locations, fusing high temporal resolution radar data with available optical data presents an opportunity for operational mapping of crop types and land cover to support food security and environmental management decision-making. 

Amid climate change, biodiversity loss and food insecurity, there is the growing need to draw synergies between micro-scale environmental processes and practices, and macro-level ecosystem dynamics to facilitate conservation decision-making. Adopting this synergistic approach can improve crop yields and profitability more sustainably, enhance livelihoods and mitigate climate change. Using spatially explicit data generated through a public participatory geographic information system methodology (n = 37), complemented by spatial analysis, interviews (n = 68) and focus group discussions (n = 4), we explored the synergies between participatory farmer-to-farmer agroecology knowledge sharing, farm-level decisions and their links with macro-level prioritization of conservation strategies. We mapped farm conditions and ecosystem services (ES) of two village areas with varying knowledge systems about farming. Results of the farm-level analysis revealed variations in spatial perception among farmers, differences in understanding the dynamics of crop growth and varying priorities for extension services based on agroecological knowledge. The ES use pattern analysis revealed hotspots in the mapped ES indicators with similarities in both village areas. Despite the similarities in ES use, priorities for biodiversity conservation align with farmers’ understanding of farm processes and practices. Farmers with training in agroecology prioritized strategies that are ecologically friendly while farmers with no agroecology training prioritized the use of strict regulations. Importantly, the results show that agroecology can potentially contribute to biodiversity conservation and food security, with climate change mitigation co-benefits. The findings generally contribute to debates on land sparing and land sharing conservation strategies and advance social learning theory as it pertains to acquiring agroecological knowledge for improved yield and a sustainable environment.