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The basins of the Indus and Ganges rivers cover 2.20 million km2 and are inhabited by more than a billion people. The region is under extreme pressures of population and poverty, unregulated utilization of the resources and low levels of productivity. The needs are: (1) development policies that are regionally differentiated to ensure resource sustainability and high productivity; (2) immediate development and implementation of policies for sound groundwater management and energy use; (3) improvement of the fragile food security and to broaden its base; and (4) policy changes to address land fragmentation and improved infrastructure. Meeting these needs will help to improve productivity, reduce rural poverty and improve overall human development.

AbstractBACKGROUND: Inappropriate utilisation of biosolids may adversely impact agrosystem productivity. Here, we address the response of wheat (Triticum durum) to different doses (0, 40, 100, 200 and 300 t ha−1) of either municipal solid waste (MSW) compost or sewage sludge in a greenhouse pot experiment. Plant growth, heavy metal uptake, and antioxidant activity were considered.RESULTS: Biomass production of treated plants was significantly enhanced at 40 t ha−1 and 100 t ha−1 of MSW compost (+48% and +78% relative to the control, respectively). At the same doses of sewage sludge, the increase was only 18%. Higher doses of both biosolids restricted significantly the plant growth, in concomitance with the significant accumulation of heavy metals (Ni2+, Pb2+, Cu2+ and Zn2+), especially in leaves. Leaf activities of antioxidant enzymes (ascorbate peroxidase, glutathione reductase, catalase and superoxide dismutase) were unchanged at 40 t ha−1 MSW compost or sewage sludge, but were significantly stimulated at higher doses (200–300 t ha−1), together with higher leaf concentration of reduced glutathione.CONCLUSION: This preliminary study suggests that a MSW supply at moderate doses (100 t ha−1) could be highly beneficial for wheat productivity. Copyright © 2010 Society of Chemical Industry

Life Cycle Assessment (the systematic analysis of the environmental impact of products during their entire life cycle), Carbon Footprint and Water Footprint assessments play an important role in decision-making processes. These assessments can help guide land management decisions and will likely play a larger role in the future, especially in natural areas with high biodiversity. Agriculture is a substantial consumer of fresh water, so it is important to identify causes and possible solutions to optimize agricultural water use. Water footprint assessments consider water consumption from several points of view and aid in reaching Sustainable Development Goals. Olive trees are a widespread agricultural crop growing in the Mediterranean Basin and are particularly important in the Umbria region in Italy. This paper estimates the water footprint impact related to the production of 1 kg of olives in a rainfed olive orchard managed using low environmental impact techniques. Eleven years of data collection (meteorological data, olives yield data, processes data) are analyzed for typical rural conditions. The results show that local management techniques have lower water requirements than standard international usages. These results can be used to improve and to further explore agricultural water use.

Abstract An energy access assessment conducted by Practical Action in 2018 as part of the Renewable Energy for Refugees project established that most households and small enterprises in Kigeme, Gihembe and Nyabiheke refugee camps in Rwanda had limited or no access to electricity. It also identified both demand in the camps for modern energy services and a willingness and ability to pay. To address the lack of access to electricity, two solar home system companies operating in Rwanda were supported by the project to access the camps and supply systems to refugees and the host community via market-based delivery models. This paper applies the diffusion of innovations theory as a framework to investigate the sales of solar home systems in the camps. It is the first paper to present data in this area and it assesses both the viability of market-based delivery of solar home systems in refugee camps and the suitability of using diffusion of innovations theory in these contexts. The results indicate that solar home systems can provide an advantage to households compared to existing energy solutions and are, in most cases, compatible with refugees' basic energy needs and expectations. However, the cost of systems remains a barrier and without subsidy, further reductions in costs or adaptations to payment models, solar home systems are unlikely to provide large proportions of households and small enterprises in the camps with access to energy. This seriously impacts the possibility of achieving Sustainable Development Goal 7 and for the United Nations High Commissioner for Refugees to achieve the objectives it set out in its Clean Energy Challenge policy.

ABSTRACTThe aquifer system of Doñana (SW Spain) represents the most important freshwater source in the Doñana Natural Area. Its spatiotemporal dynamics favours the hydrological connection between surface and subsurface ecosystems, and promotes matter fluxes among the different terrestrial and aquatic systems present here. This aquifer has been intensively studied from a hydrogeological point of view but little is known from an ecological perspective. In order to understand the ecological roles played by microbial communities in this system, we conducted a long‐term seasonal study of bacterial abundance, cell biomass, bacterial biomass and functional activities over a 2‐year period. Bacterial abundance ranged between 2.11 ± 1.79 × 105 and 8.58 ± 6.99 × 107 bacteria mL−1 groundwater, average cell biomass was estimated to be 77.01 ± 31.56 fgC and bacterial biomass varied between 8.99 ± 4.10 × 10−2 and 5.65 ± 0.70 µgC mL−1. Iron‐related bacteria showed the highest activities among the functional groups studied. Moreover, among the variables that usually control spatial distributions of microbial communities in aquifer systems, depth did not have a relevant effect on this aquifer, at least in the range of depths studied, but grain size, probably due to its direct effects on hydrogeological parameters, such as permeability or porosity, appeared to exert moderate control, principally in terms of bacterial abundance. Finally, significant seasonal differences in the means of these microbiological variables were also observed; temperature seems to be the main factor controlling the temporal distribution of microbial communities in this aquifer system.

Understanding the relationship between climate and crop productivity is a key component of projections of future food production, and hence assessments of food security. Climate models and crop yield datasets have errors, but the effects of these errors on regional scale crop models is not well categorized and understood. In this study we compare the effect of synthetic errors in temperature and precipitation observations on the hindcast skill of a process-based crop model and a statistical crop model. We find that errors in temperature data have a significantly stronger influence on both models than errors in precipitation. We also identify key differences in the responses of these models to different types of input data error. Statistical and process-based model responses differ depending on whether synthetic errors are overestimates or underestimates. We also investigate the impact of crop yield calibration data on model skill for both models, using datasets of yield at three different spatial scales. Whilst important for both models, the statistical model is more strongly influenced by crop yield scale than the process-based crop model. However, our results question the value of high resolution yield data for improving the skill of crop models; we find a focus on accuracy to be more likely to be valuable. For both crop models, and for all three spatial scales of yield calibration data, we found that model skill is greatest where growing area is above 10-15 %. Thus information on area harvested would appear to be a priority for data collection efforts. These results are important for three reasons. First, understanding how different crop models rely on different characteristics of temperature, precipitation and crop yield data allows us to match the model type to the available data. Second, we can prioritize where improvements in climate and crop yield data should be directed. Third, as better climate and crop yield data becomes available, we can predict how crop model skill should improve.

Implementing a circular economy aimed at reusing resources is becoming increasingly important for industry. Microalgae fit within a circular economy by being able to bioremediate nutrient waste and as a source of biomass for several commercial applications. Here, we report a novel validation of a circular economy concept using microalgae at a relevant industrial scale with a new two-phase process. During the first phase biomass was grown autotrophically, biomass was then concentrated using membrane technology for the second phase where mixotrophic conditions were applied to boost growth further. Microalgae cultures were able to grow (13.8 g/L), uptake and bioremediate nutrients (Nitrogen > 134 mg/L/day) from an anaerobic digestion side-stream (digestate), obtaining high quality microalgae biomass (>45% protein content) suitable for use as animal feed, closing the circular economy loop for industrial applications.

Resource-use efficiency and crop yield are significant factors in the management of agricultural greenhouse. Appropriate modeling methods effectively improve the control performance and efficiency of the greenhouse system and are conducive to the design of water and energy-saving strategies. Meanwhile, the extreme environment could be forecasted in advance, which reduces pests and diseases as well as provides high-quality food. Accordingly, the interest of the scientific community in greenhouse modeling and optimizing has grown considerably. The objective of this work is to provide guidance and insight into the topic by reviewing 73 representative articles and to further support cleaner and sustainable crop production. Compared to the existing literature review, this work details the approaches to improve the greenhouse model in the aspects of parameter identification, structure and process optimization, and multi-model integration to better model complex greenhouse system. Furthermore, a statistical study has been carried out to summarize popular technology and future trends. It was found that dynamic and neural network techniques are most commonly used to establish the greenhouse model and the heuristic algorithm is popular to improve the accuracy and generalization ability of the model. Notably, deep learning, the combination of “knowledge” and “data”, and coupling between the greenhouse system elements have been considered as future valuable development.