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.

The prediction of hydrological phenomena using simpler hydrological models requires less computing power and input data compared to the more complex models. Ordinarily, a more complex, white-box model would be expected to have better predictive capabilities than a simple grey box or black-box model. But complexity may not necessarily translate to better prediction accuracy or might be unfeasible in data scarce areas or when computer power is limited. Therefore, the shift of hydrological science towards the more process-based models needs to be justified. To answer this, the paper compares 2 hydrological models: (a) the simpler tank model; and (b) the more complex TOPMODEL. More precisely, the difference in performance between tank model as a lumped model and the TOPMODEL concept as a semi-distributed model in Atari River catchment, in Eastern Uganda was conducted. The objectives were: (1) To calibrate tank model and TOPMODEL; (2) To validate tank model and TOPMODEL; and (3) To compare the performance of tank model and TOPMODEL. During calibration, both models exhibited equifinality, with many parameter sets equally likely to make acceptable hydrological simulations. In calibration, the tank model and TOPMODEL performances were close in terms of ‘Nash-Sutcliffe efficiency’ and ‘RMSE-observations standard deviation ratio’ indices. However, during the validation period, TOPMODEL performed much better than tank model. Owing to TOPMODEL’s better performance during model validation, it was judged to be better suited for making runoff forecasts in Atari River catchment.

Microgreens are innovative vegetable products whose production and consumption are gaining popularity globally thanks to their recognized nutraceutical properties. To date, the effects of lighting conditions and growing substrate on the performances of Brassica carinata microgreens (indigenous to Africa) remain underexplored. The present study aimed at providing insights into the influence of different lighting treatments provided by LEDs, namely monochromatic blue (B), red (R), cool white (W) and a combination of three color diodes (B + R + W), and substrates (cocopeat, sand and cocopeat–sand mix (v/v) (1:1)) on the growth, yield and bioactive compounds of B. carinata microgreens. Seeds were germinated in dark chambers and cultivated in growth chambers equipped with LED lighting systems for 14 days under a fixed light intensity of 160 ± 2.5 µmol m⁻² s⁻¹ and photoperiod of 12 h d⁻¹. The best performances were associated with the spectrum that combined B + R + W LEDs and with substrate resulting from the cocopeat–sand mix, including the highest yield (19.19 g plant⁻¹), plant height (9.94 cm), leaf area (68.11 mm²) and canopy cover (55.9%). Enhanced carotenoid and flavonoid contents were obtained with B + R + W LEDs, while the B LED increased the total amount of chlorophyll (11,880 mg kg⁻¹). For plants grown under B + R + W LEDs in cocopeat, high nitrate levels were observed. Our results demonstrate that substrate and light environment interact to influence the growth, yield and concentration of bioactive compounds of B. carinata microgreens.

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.

In this study, we propose quantifying the Amu River riverbank erosion with the modelled river discharge in Kaldar District, Balkh Province of Afghanistan from 2004 to 2020. We propose a framework synergizing multi-source information for modelling the erosion area based on three components: (1) river discharge, (2) river width, and (3) erosion area. The total river discharge for the watershed shared by Afghanistan and Tajikistan was modelled using hydrological parameters from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) data through multivariate linear regression with ground station data. The river width was determined manually using the Normalized Difference Water Index (NDWI) derived from Landsat data. The riverbank erosion area was derived from the digital shoreline analysis using the NDWI. The digital shoreline analysis showed that, between 2008 and 2020, the average riverbank erosion area in Kaldar District is about 5.4 km² per year, and, overall, 86.3 km² during 2004–2020 due to flood events. The significantly higher land loss events occurred at 10 km² bank erosion during the years 2008–2009 and 2015–2016, and 19 km² peak erosion occurred during 2011–2012. A linear relation between the erosion area with respect to the discharge intensity and the specific stream power was observed with an R² of 0.84 and RMSE of 1.761 for both.