Ground control point (GCP) is an important calibration factor when correcting position information during unmanned aerial vehicle (UAV) remote sensing. Studies of the optimal number and distribution shape of GCPs have been conducted worldwide in recent years. However, when conducting surveys at houses, construction sites, farming lands, forests, and some other locations, it is both difficult and destructive to install GCP inside the subject area. In many cases, it is only possible to install GCP at the outer edge around the area. Therefore, this study aims to suggest the optimal GCP distribution pattern, which can provide the highest accuracy, when only the outer edge of a particular area is available. In this research, 88 GCP patterns have been validated and compared at an 18 ha farm. Results show that the patterns with GCPs distributed evenly around the field provided the best calibration (RMSE = 0.15 m). If this kind of pattern is not achievable because of obstructions, patterns with GCPs distributed evenly around half of the field or forming an evenly distributed triangle can provide moderate accuracy (RMSE = 0.18 m and 0.43 m, respectively). Patterns with GCPs forming a straight line yielded the worst accuracy (RMSE = 2.10 m). This shows that GCP distributions of a two-dimensional shape, even if the surrounding area is small, are better calibrated than a long, straight line. These results strongly suggest that appropriate GCP distribution patterns in the study areas will provide satisfactory accuracy for constructing integrated monitoring systems of diverse resources.

Agricultural productivity impacts the environment and natural resources in various ways. The severity of these impacts has triggered the emergence of natural resource management and the related, highly criticized science of agroecology. Vegetable production has known environmental impacts. However, the extent of its participation in sustainable production has not been adequately explored. This review sought to explore the spaciotemporal position of vegetables in a suite of existing sustainable agricultural practices, explore regional variations and discover lessons that can guide the future of vegetable production. There are regional differences regarding sustainable production practices and the associated barriers to their adoption. Generally, sustainable agricultural practices with a societal history in a region tend to be successful, unlike when they are “new” innovations. The major barriers to sustainable agricultural practices in vegetable production are economy-related (total investment cost) and crop-related and are also related to the technology transmission approaches. Unfulfilled expectations and a lack of community participation in technology development are noted challenges, which have led to dis-adoption. A farmer-centered approach to technology promotion could help. Comparatively, southern Africa has the most challenges in the adoption of sustainable agricultural practices. From the lessons learned from other regions, agroecology in vegetable cultivation is not unachievable in Africa. The projected challenges mean that sustainable vegetable production is inevitable.

Identification and monitoring of diverse resources or wastes on the ground is important for integrated resource management. The unmanned aerial vehicle (UAV), with its high resolution and facility, is the optimal tool for monitoring ground objects accurately and efficiently. However, previous studies have focused on applying classification methodology on land use and agronomy, and few studies have compared different classification methods using UAV imagery. It is necessary to fully utilize the high resolution of UAV by applying the classification methodology to ground object identification. This study compared three classification methods: A. NDVI threshold, B. RGB image-based machine learning, and C. object-based image analysis (OBIA). Method A was the least time-consuming and could identify vegetation and soil with high accuracy (user’s accuracy > 0.80), but had poor performance at classifying dead vegetation, plastic, and metal (user’s accuracy < 0.50). Both Methods B and C were time- and labor-consuming, but had very high accuracy in separating vegetation, soil, plastic, and metal (user’s accuracy ≥ 0.70 for all classes). Method B showed a good performance in identifying objects with bright colors, whereas Method C showed a high ability in separating objects with similar visual appearances. Scientifically, this study has verified the possibility of using the existing classification methods on identifying small ground objects with a size of less than 1 m, and has discussed the reasons for the different accuracy of the three methods. Practically, these results help users from different fields to choose an appropriate method that suits their target, so that different wastes or multiple resources can be monitored at the same time by combining different methods, which contributes to an improved integrated resource management system.

Climate change and variability in Malawi have had a wide impact on rain-fed agricultural production. The recent climatic events suggest new trends in agro-climatic parameters such as dry spell length, total annual rainfall, and planting dates. However, there is dearth of understand the emerging trends on the agro-climatic parameters in Malawi. Both parametric and non-parametric trend tests were used to quantify the strength of trends on a 49-year series of historical rainfall data from eight meteorological stations across Malawi. The results show that, while there is no change in dry spell length across the country, there is an increase in the number of dry spells and a decrease in the number of rainy days per growing season. Thus, there is a slight decrease in total annual rainfall with high variability. Suitable planting dates have also shifted towards later days in the season, resulting in a shorter growing season accompanied by high- intensity rainfall. This study indicates significant changes in the rainfall pattern in Malawi in specific areas, especially in the north. Therefore, it is imperative to consider early maturing varieties, in-field water harvesting structures, efficient irrigation systems, and extension services to farmers to adapt change of agricultural calendar.