Multifunctionality of urban agriculture can support the resolve of many urban challenges. Therefore, it is vital to understand the contribution of academic studies on urban agriculture practices as scientific information. The present study followed a systematic literature review based on the PRISMA method. Finally, 54 identified articles were analyzed. The review study mainly examined the contribution of academic literature on urban agriculture under four dimensions: the socio-economic context of the country, type of agriculture model, opportunities, and challenges. The results revealed the focus of academic literature on urban agriculture to show favoritism toward developed countries’ community gardens. Moreover, the leading academic focus on this discipline identifies multifunctionality. People’s motivations in developed countries tend to favor social, health-related, and educational benefits of urban agriculture; however, in developing countries, urban agriculture is more related to economic and ecological needs. Challenges for urban agriculture are also different among developed and developing countries. Nevertheless, existing academic studies have given comparatively less attention to identifying challenges, benefit groups of urban agriculture, and government support. Since urban agriculture is highly reliant on local factors, studying more about opportunities and challenges for urban agriculture under different socio-economic contexts and different agriculture models could be more beneficial to connect farming practices in cities with urban planning. Therefore, to make an adequate academic contribution to urban sustainability, future urban agriculture studies need to be more holistic.

For countries in Southeast Asia that mainly rely on surface water as their water resource, changes in weather patterns and hydrological systems due to climate change will cause severely decreased water resource availability. Warm weather triggers more water use and exacerbates the extraction of water resources, which will change the operation patterns of water usage and increase demand, resulting in water scarcity. The occurrence of prolonged drought upsets the balance between water supply and demand, significantly increasing the vulnerability of regions to damaging impacts. The objectives of this study are to identify trends and determine the impacts of extreme drought events on water levels for the major important water dams in the northern part of Borneo, and to assess the risk of water insecurity for the dams. In this context, remote sensing images are used to determine the degree of risk of water insecurity in the regions. Statistical methods are used in the analysis of daily water levels and rainfall data. The findings show that water levels in dams on the North and Northeast Coasts of Borneo are greatly affected by the extreme drought climate caused by the Northeast Monsoon, with mild to the high risk recorded in terms of water insecurity, with only two of the water dams being water-secure. This study shows how climate change has affected water availability throughout the regions.

Cropping intensity is one of the most important decisions made independently by farmers in Vietnam. It is a crucial variable of various economic and process-based models. Rice is grown under irrigated triple- and double-rice cropping systems and a rainfed single-rice cropping system in the Vietnamese Mekong Delta (VMD). These rice cropping systems are adopted according to the geographical location and water infrastructure. However, little work has been done to map triple-cropping of rice using Sentinel-1 along with the effects of water infrastructure on the rice cropping intensity decision. This study is focused on monitoring rice cropping patterns in the An Giang province of the VMD from March 2017 to March 2018. The fieldwork was carried out on the dates close to the Sentinel-1A acquisition. The results of dual-polarized (VV and VH) Sentinel-1A data show a strong correlation with the spatial patterns of various rice growth stages and their association with the water infrastructure. The VH backscatter (σ°) is strongly correlated with the three rice growth stages, especially the reproductive stage when the backscatter is less affected by soil moisture and water in the rice fields. In all three cropping patterns, σ°VV and σ°VH show the highest value in the maturity stage, often appearing 10 to 12 days before the harvesting of the rice. A rice cropping pattern map was generated using the Support Vector Machine (SVM) classification of Sentinel-1A data. The overall accuracy of the classification was 80.7% with a 0.78 Kappa coefficient. Therefore, Sentinel-1A can be used to understand rice phenological changes as well as rice cropping systems using radar backscattering.

Wastewater-based epidemiology (WBE) is an approach that can be used to estimate COVID-19 prevalence in the population by detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in wastewater. As the WBE approach uses pooled samples from the study population, it is an inexpensive and non-invasive mass surveillance method compared to individual testing. Thus, it offers a good complement in low- and middle-income countries (LMICs) facing high costs of testing or social stigmatization, and it has a huge potential to monitor SARS-CoV-2 and its variants to curb the global COVID-19 pandemic. The aim of this review is to systematize the current evidence about the application of the WBE approach in mass surveillance of COVID-19 infection in LMICs, as well as its future potential. Among other parameters, population size contributing the fecal input to wastewater is an important parameter for COVID-19 prevalence estimation. It is easier to back-calculate COVID-19 prevalence in the community with centralized wastewater systems, because there can be more accurate estimates about the size of contributing population in the catchment. However, centralized wastewater management systems are often of low quality (or even non-existent) in LMICs, which raises a major concern about the ability to implement the WBE approach. However, it is possible to mobilize the WBE approach, if large areas are divided into sub-areas, corresponding to the existing wastewater management systems. In addition, a strong coordination between stakeholders is required for estimating population size respective to wastewater management systems. Nevertheless, further international efforts should be leveraged to strengthen the sanitation infrastructures in LMICs, using the lessons gathered from the current COVID-19 pandemic to be prepared for future pandemics.

The demand for organic foods is increasing worldwide due to health and environmental benefits. However, there are several unanswered questions, such as: Do organic farmers generate higher profits? Will the cost of cultivation reduce to compensate for low yields? Can farmers practice as per the organic agriculture protocols and obtain certification? The literature on organic agriculture varies widely in terms of profitability, yields and costs of organic products. A few studies have researched site-specific organic agriculture, but none have compared organic with conventional agriculture at larger scale in India. The Indian government has promoted organic agriculture since 2015 through its pan-India scheme—Paramparagat Krishi Vikas Yojana (PKVY). Under this program, there were 13.9 million certified organic farmers in 29,859 organic clusters, covering 0.59 million hectares (about 0.4% of the cropped area in India). This study assessed the implementation process of PKVY and the impact at the farmer level using the Difference-in-Difference approach. An economic surplus model was employed to observe the macro scale using data from an all-India representative sample from 576 clusters for the crop year 2017. The results identified that organic farmers experienced 14–19 percent less costs and 12–18 percent lower yields than conventional farmers. The net result is a marginal increase in profitability compared to traditional agriculture. The economy-wide economic surplus model indicates that there will be a reduction in producer and consumer surplus due to reduced crop yields. However, if the shift from conventional to organic is confined to rainfed, hilly and tribal areas, there will be an increase in both consumer and producer surplus.

The soiling of solar panels from dry deposition affects the overall efficiency of power output from solar power plants. This study focuses on the detection and monitoring of sand deposition (wind-blown dust) on photovoltaic (PV) solar panels in arid regions using multitemporal remote sensing data. The study area is located in Bhadla solar park of Rajasthan, India which receives numerous sandstorms every year, carried by westerly and north-westerly winds. This study aims to use Google Earth Engine (GEE) in monitoring the soiling phenomenon on PV panels. Optical imageries archived in the GEE platform were processed for the generation of various sand indices such as the normalized differential sand index (NDSI), the ratio normalized differential soil index (RNDSI), and the dry bare soil index (DBSI). Land surface temperature (LST) derived from Landsat 8 thermal bands were also used to correlate with sand indices and to observe the pattern of sand accumulation in the target region. Additionally, high-resolution PlanetScope images were used to quantitatively validate the sand indices. Our study suggests that the use of freely available satellite data with semiautomated processing on GEE can be a useful alternative to manual methods. The developed method can provide near real-time monitoring of soiling on PV panels cost-effectively. This study concludes that the DBSI method has a comparatively higher potential (89.6% Accuracy, 0.77 Kappa) in the detection of sand deposition on PV panels as compared to other indices. The findings of this study can be useful to solar energy companies in the development of an operational plan for the cleaning of PV panels regularly.