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In sub-Saharan Africa (SSA), diets are largely based on cereal or root staple crops. Together with socio-cultural change, economic and demographic growth could boost the demand for meat, with significant environmental repercussions. We model meat consumption pathways to 2050 for SSA based on several scenarios calibrated on historical demand drivers. To assess the consequent environmental impact, we adopt an environmentally-extended input-output (EEIO) framework and apply it on the EXIOBASE 3.3 hybrid tables. We find that, depending on the interplay of resources efficiency and demand growth, by 2050 the growth in meat consumption in SSA could cause a growth in greenhouse gases emissions of 1.4 [0.9–1.9] Gt CO2e/yr (~175% of current regional agriculture-related emissions), which is an extension of cropping and grazing-related land of 15 [12.5–21] · 106 km2 (one quarter of today’s global agricultural land), the consumption of an additional 36 [29–47] Gm3/yr of blue water (nearly doubling the current regional agricultural consumption), an eutrophication potential growth of 7.6 [4.9–9.5] t PO4e/yr, and the consumption of additional 0.9 [0.5–1.4] EJ/yr of fossil fuels and 49 [32–73] TWh/yr of electricity. These results suggest that—in the absence of significant improvements in the regional sectoral resource efficiency—meat demand growth in SSA is bound to become a major global sustainability challenge. In addition, we show that a partial substitution of the protein intake from the expected growth in meat consumption with plant-based alternatives carries additional significant potential for mitigating environmental impacts. The policies affecting both farming practices and dietary choices will thus have a significant impact on the SSA and global environmental flows.

Each month the JRC issues the MARS Bulletin detailing the agro-meteorological and expert analysis underpinning the assessment of European crops’ status and yield forecasts. In this context a resume is provided to give an overview on the geographical distribution of eventual crop damages. The MARS Bulletin provides such information in a set of synthetic maps (Areas of Concern), produced in each Bulletin, depicting extreme weather events and their impact on crops that have occurred in Europe during the analysis period. The present article describes the mix of quantitative and qualitative datasets and methodologies that drive the delineation of the Areas of Concern (AOC) maps and evaluates their capability to resemble crop production losses. The quantitative analysis is based on the Mars Crop Yield Forecasting System (MCYFS) indicators coming from meteorological models, crop growth models and remote sensing data. Indicators are considered in absolute and relative terms and in their relation with standard statistical metrics. The outcome of the quantitative analysis is a set of potential Areas of Concern. Experts’ judgment is thus necessary to discriminate potential results through a qualitative analysis focused on: past occurred events and climatologic conditions; agro-management practices; regional agricultural systems peculiarity and their historical resilience and resistance to adverse conditions. In this article the experts’ judgment of the team of current MARS analysts, as used in the AOC analysis, is translated into a warning index. Such index condenses the specific contribution to the final production of each development stage and the adverse agrometeorological events occurred, as depicted into the AOC maps. The warning index is computed at country scale for the past five agricultural seasons, from season 2011-2012 to season 2015-2016. Two crops are considered, winter wheat and grain maize as proxy for winter and summer crop groups. The warning indexes calculated are then compared to the national production in a qualitative way. To support the analysis few study cases are presented. The findings of this article highlight that the events depicted in the AOC maps are informative about production losses and specific knowledge is needed to full understand the information carried. Highlights • The comparison of the issued warnings through AOC analysis shows some good agreements with production anomalies. • AOC analysis properly assessed most of the winter wheat production anomalies. • AOC analysis could provide warning relevant at regional scale only. • AOC analysis tends to underestimate unfavorable conditions at harvest. • This paper represents the first attempt to formalize the tacit knowledge within the group of MARS analysts.

The choice of basil (Ocimum basilicum L.) genotypes determines key attributes such as yield, flavor, and adaptability, contributing significantly to the overall success and sustainability of basil cultivation practices. As the primary aim of this study, seven basil accessions were characterized for both their growth performance and biochemical profile of volatile compounds, enabling the differentiation among distinct chemotypes. As secondary objectives, growth performance and production were evaluated under natural solar radiation conditions (SR100) and with a 30% reduction in solar radiation using a net (SR70). Light use efficiency (LUE) determination revealed the plants’ biomass production capability under different solar radiation (SR) conditions. Genotypes A, B, C, and G were characterized by a high levels of linalool, which is typically associated with the “pesto” sauce smell. Lemon basil D exhibited a different chemotype due to the presence of neral and geranial. E and F displayed a different chemotype due to the higher concentration of α-bergamotene. The total fresh harvested biomass was significantly higher in SR70 than SR100 conditions. The second harvest in both SR conditions was the most productive one, while genotype E under SR70 displayed the highest yield. The landraces D and E showed the highest LUE values, indicating their capability in converting the solar radiation into fresh biomass. Plants grown in SR70 conditions registered significantly higher values of plant height, number of branches, and leaf weight. This work aimed to provide valuable insights into the selection of basil genotypes suitable for sustainable agriculture. Conversely, it lays the basis for cultivation aspects pertaining to the crop’s adaptability in peri-urban, marginal lands, which are characterized by limited solar radiation.

Estimating crop evapotranspiration (ETc) is crucial for ensuring sustainable and efficient agricultural water management. Although this subject has garnered significant attention from the global scientific community, a comprehensive study encompassing the diversity, trends, and dynamics of research themes is currently lacking. To address this knowledge gap, this review employed a combined bibliometric and thematic approach to analyze bibliographic data from 1872 documents retrieved from the Web of Science™ core collection, spanning the period 1987–2022. The main findings of this review are as follows: (1) the scientific landscape is predominantly shaped by institutions from the USA and China; (2) the journal Agricultural Water Management emerged as the most prolific, with the highest number of publications and total citations; (3) a broad range of topics within ETc research were identified, with a notable emphasis on remote sensing-related subjects; (4) strategic coordination mapping revealed that ETc and reference evapotranspiration (ETo) remains an underdeveloped area of study; (5) climate change and machine learning emerged as key topics of significant scientific concern. The results suggest a need for enhanced institutional collaborations and expanded research investigations, particularly in regions grappling with agricultural water scarcity. Furthermore, research investigations should focus on ETc and ETo to fill existing knowledge gaps and advance both theoretical understanding and practical applications. Future studies should aim to contribute to the understanding of the impacts of climate change on ETc by leveraging machine learning techniques and enhancing our understanding of crop water requirements and their application in irrigation management, while also ensuring continuous updates to the existing body of knowledge to meet future challenges.

Chlorophyll distribution in Haifa Bay was estimated in a period of very high chlorophyll concentrations (up to 70 mg/m3) using remotely sensed data. Radiometric measurements with spectral resolution of 2 nm in the range from 400 to 850 nm, were taken simultaneously with samples for chlorophyll concentration, turbidity and Secchi disk transparency. Prominent features of the reflectance spectra were a wide minimum from 420 to 500 nm, a maximum at 550-570 nm, a minimum at 676 nm and a maximum reflectance showed near 700 nm. High spectral resolution data were used for the selection of the most suitable spectral bands for remote estimating of chlorophyll concentration. The magnitude and position of the peak near 700 nm were highly correlated with chlorophyll concentration. The reflectance height at 690 nm above the baseline from 670 to 850 nm and area above the baseline were used as sensitive indicators of chlorophyll concentration. The best model enabled estimation of chlorophyll concentration with an error of less than 4.3 mg/m3. For the purpose of chlorophyll mapping in Haifa Bay, the use of three relatively narrow spectral bands was sufficient. Radiometric data were also used to simulate radiances in the channels of TM Landsat and to find the algorithm for chlorophyll assessment. The ratios TM3/TM1 and (TM2-TM3)/TM1 were used and enable chlorophyll estimation with an error of less than 8 mg/m3.

Abstract ArduHydro is a low-cost device for water level measurement and monitoring designed for a short and long-term employment in controlled and outdoor environments. It measures water level through an ultrasonic sensor and elaborates the signals through an Arduino micro controller. The small size of this device, the low energy required for its operation, its robustness and accuracy make ArduHydro properly versatile for different applications in the field for water control and management. This article describes the design, the components, the costs, and the performance of ArduHydro. Performance was assessed with a laboratory test inside a hydraulic circuit constituted by an open channel flume and comparing ArduHydro measurements with those obtained with a traditional ultrasonic sensor. An example of ArduHydro application for detecting the wavefront evolution during a surface irrigation is presented as well. The results revealed that ArduHydro measurements were on average very consistent with those obtained by the traditional ultrasonic sensor in all different flow conditions (i.e. different flowrate and water depth) demonstrating its reliability and accuracy in the measuring water level. The application of ArduHydro during a surface watering of an agricultural field allowed to obtain important spatio-temporal information about the water depth along the longitudinal direction of the field, paying the way for a real comprehension of the dynamics of wavefront evolution in a real-world case study.

AbstractLand cover change (LCC) is a complex and dynamic process influenced by social, economic, and biophysical factors that can cause significant impacts on ecological processes and biodiversity conservation. The assessment of LCC is particularly relevant in a country like Mozambique where livelihood strongly depends on natural resources. In this study, LCC was assessed using a point-based sampling approach through Open Foris Collect Earth (CE), a free and open-source software for land assessment developed by the Food and Agriculture Organization of the United Nations. This study aimed to conduct an LCC assessment using CE for the entire Mozambique, and according to three different land classifications: administrative boundaries (provinces), ecoregions, and protected vs unprotected areas. A set of 23,938 randomly selected plots, with an area of 0.5 hectares, placed on a 4 × 4 km regular grid over the entire country, was assessed using CE. The analysis showed that Mozambique has gone through significant loss of forest (− 1.3 Mha) mainly to the conversion to cropland. Deforestation is not occurring evenly throughout the country with some provinces, such as Nampula and Zambezia, characterized by higher rates than others, such as Gaza and Niassa. This result can be explained considering a combination of ecological and socio-economic factors, as well as the conservative role played by the protected areas. Our study confirmed that LCC is a complex phenomenon, and the augmented visual interpretation methodology can effectively complement and integrate the LCC analyses conducted using the traditional wall-to-wall mapping to support national land assessment and forest inventories and provide training data for environmental modeling.