Analysing patterns of forest cover change and related land uses in the Tano-Offin forest reserve in Ghana:Implications for forest policy and land management

Forest cover change is a major contributing factor to global environmental change. Whereas several studies have focused on the general land use and land cover dynamics, we focus on analysing forest cover change patterns in a protected landscape taking into consideration how other land categories are increasing at the expense of the forest. In this study, we analyse forest cover change patterns and associated proximate land use factors between 1987 and 2017 using Landsat images from the Tano-Offin Forest Reserve (TOFR) in Ghana. Using the Random Forest machine learning algorithm, we classified the images into forest, developed land, and agricultural land. The study finds that forest cover losses are 1.9 and 1.4 times the amount of forest cover gains in 1987–2002 and 2002–2017, respectively. We find that even though the forest cover is more likely to recover from the agricultural land, land developers mostly targeted the agricultural land. The focus of Ghana's Forest and Wildlife Policy and the underlying process of forest cover change in the TOFR suggest that a country's forest policy should focus on a combination of diverse and spatially explicit proximate factors that are likely to threaten the integrity of forests

Estimating Groundnut Yield in Smallholder Agriculture Systems Using PlanetScope Data

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

Agroecology, Ecosystem Services and Crop Health and Productivity: A Participatory Geospatial Analysis in Northern Malawi

Agroecology (AE) is a cost-effective alternative to increasing productivity and enhancing ecological integrity. Geospatial techniques are cost-effective for near real-time monitoring of earth systems. In smallholder agricultural systems, however, the application of geospatial techniques is limited and understanding of the impact of agronomic practices on ecosystems and crop productivity is underexplored from a geospatial perspective. Therefore, spatial participatory techniques (PPGIS) were applied to explore the relationship between AE, ecosystems, and biodiversity conservation by comparing AE with non-AE farms. Remote sensing techniques were applied to assess the impact of AE on crop health and to prospectively predict crop health using leaf area (LAIs) and vegetation indices (VIs). Machine learning and statistical methods were applied to estimate groundnut productivity from observed yield and satellite-derived VIs and to identify the optimal growth stage for yield estimation. Finally, to overcome the challenge of dense cloud cover in complex heterogeneous agricultural landscapes, optical and radar remote sensing data were integrated to develop a method for mapping crop types and land cover. The PPGIS activities revealed that the AE farmers understand the linkages between farm-level practices/processes and ecosystem services compared with the non-AE farmers and prioritize ecologically friendly conservation strategies. Farms on which agroecological methods were implemented were healthier, with average seasonal LAIs for maize/pigeon pea (1.28m2/m2) and maize/beans (1.29m2/m2) compared with 0.97m2/m2 and 0.80m2/m2, respectively, for non-AE farms. Random Forest (RF) regressions prospectively predicted crop health for maize/beans (R2=0.90, root mean square error [RMSE] =0.32m2/m2), and maize/pigeon pea (R2=0.88m2/m2, RMSE=0.42m2/m2) on the AE farms, but results for non-AE farms were not statistically significant. The groundnut yield estimation revealed that the RF model (R2=0.96 and RMSE=0.29kg/ha) outperformed other models in estimating groundnut yield and the optimal growth stage for estimation is the R5/development of seeds stage. Finally, integrating Sentinel-1, Sentinel-2 and PlanetScope data produced detailed crop type and land cover maps with an average overall classification accuracy of 85.70%. The findings demonstrate the importance of spatial participatory approaches for examining the impacts of agronomic practices on ecosystems and crop health and the need for integrative approaches to food security planning and conservation in the Global South

Estimating yield of household groundnut fields in rural smallholder farming systems and its implication for food security, Lightning Talk (7 min)

The study used random forest regression analysis to predict groundnut yields based on yield data, in-situ leaf area index and vegetation indices. I used ArcGIS Pro and ArcMap for computing vegetation indices and creating yield prediction maps

Spatial and Temporal Change of Land Cover in Protected Areas in Malawi: Implications for Conservation Management

Protected areas (PAs) transform over time due to natural and anthropogenic processes, resulting in the loss of biodiversity and ecosystem services. As current and projected climatic trends are poised to pressurize the sustainability of PAs, analyses of the existing perturbations are crucial for providing valuable insights that will facilitate conservation management. In this study, land cover change, landscape characteristics, and spatiotemporal patterns of the vegetation intensity in the Kasungu National Park (area = 2445.10 km2) in Malawi were assessed using Landsat data (1997, 2008 and 2018) in a Fuzzy K-Means unsupervised classification. The findings reveal that a 21.12% forest cover loss occurred from 1997 to 2018: an average annual loss of 1.09%. Transition analyses of the land cover changes revealed that forest to shrubs conversion was the main form of land cover transition, while conversions from shrubs (3.51%) and bare land (3.48%) to forest over the two decades were comparatively lower, signifying a very low rate of forest regeneration. The remaining forest cover in the park was aggregated in a small land area with dissimilar landscape characteristics. Vegetation intensity and vigor were lower mainly in the eastern part of the park in 2018. The findings have implications for conservation management in the context of climate change and the growing demand for ecosystem services in forest-dependent localities

Developing and implementing a model of equitable distribution of mentorship in districts with spatial inequities and maldistribution of human resources for maternal and newborn care in Rwanda

Background The shortage of health care providers (HCPs) and inequity in their distribution along with the lack of sufficient and equal professional development opportunities in low-income countries contribute to the high mortality and morbidity of women and newborns. Strengthening skills and building the capacity of all HCPs involved in Maternal and Newborn Health (MNH) is essential to ensuring that mothers and newborns receive the required care in the period around birth. The Training, Support, and Access Model (TSAM) project identified onsite mentorship at primary care Health Centers (HCs) as an approach that could help reduce mortality and morbidity through capacity building of HCPs in Rwanda. This paper presents the results and lessons learnt through the design and implementation of a mentorship model and highlights some implications for future research. Methods The design phase started with an assessment of the status of training in HCs to inform the selection of Hospital-Based Mentors (HBMs). These HBMs took different courses to become mentors. A clear process was established for engaging all stakeholders and to ensure ownership of the model. Then the HBMs conducted monthly visits to all 68 TSAM assigned HCs for 18 months and were extended later in 43 HCs of South. Upon completion of 6 visits, mentees were requested to assist their peers who are not participating in the mentoring programme through a process of peer mentoring to ensure sustainability after the project ends. Results The onsite mentorship in HCs by the HBMs led to equal training of HCPs across all HCs regardless of the location of the HC. Research on this mentorship showed that the training improved the knowledge and self-efficacy of HCPs in managing postpartum haemorrhage (PPH) and newborn resuscitation. The lessons learned include that well trained midwives can conduct successful mentorships at lower levels in the healthcare system. The key challenge was the inconsistency of mentees due to a shortage of HCPs at the HC level. Conclusions The initiation of onsite mentorship in HCs by HBMs with the support of the district health leaders resulted in consistent and equal mentoring at all HCs including those located in remote areas.Medicine, Faculty ofNon UBCPopulation and Public Health (SPPH), School ofReviewedFacultyResearche

Crop Type and Land Cover Mapping in Northern Malawi Using the Integration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data

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