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We evaluate the potential of using a process-based ecosystem model (BEPS) for crop biomass mapping at 20 m resolution over the research site in Manitoba, western Canada driven by spatially explicit leaf area index (LAI) retrieved from Sentinel-2 spectral reflectance throughout the entire growing season. We find that overall, the BEPS-simulated crop gross primary production (GPP), net primary production (NPP), and LAI time-series can explain 82%, 83%, and 85%, respectively, of the variation in the above-ground biomass (AGB) for six selected annual crops, while an application of individual crop LAI explains only 50% of the variation in AGB. The linear relationships between the AGB and these three indicators (GPP, NPP and LAI time-series) are rather high for the six crops, while the slopes of the regression models vary for individual crop type, indicating the need for calibration of key photosynthetic parameters and carbon allocation coefficients. This study demonstrates that accumulated GPP and NPP derived from an ecosystem model, driven by Sentinel-2 LAI data and abiotic data, can be effectively used for crop AGB mapping; the temporal information from LAI is also effective in AGB mapping for some crop types.

This study aimed to realize Sustainable Development Goals (SDGs), i.e., no poverty, zero hunger, and sustainable cities and communities through the implementation of an intelligent cattle-monitoring system to enhance dairy production. Livestock industries in developing countries lack the technology that can directly impact meat and dairy products, where human resources are a major factor. This study proposed a novel, cost-effective, smart dairy-monitoring system by implementing intelligent wireless sensor nodes, the Internet of Things (IoT), and a Node-Micro controller Unit (Node-MCU). The proposed system comprises three modules, including an intelligent environmental parameter regularization system, a cow collar (equipped with a temperature sensor, a GPS module to locate the animal, and a stethoscope to update the heart rate), and an automatic water-filling unit for drinking water. Furthermore, a novel IoT-based front end has been developed to take data from prescribed modules and maintain a separate database for further analysis. The presented Wireless Sensor Nodes (WSNs) can intelligently determine the case of any instability in environmental parameters. Moreover, the cow collar is designed to obtain precise values of the temperature, heart rate, and accurate location of the animal. Additionally, auto-notification to the concerned party is a valuable addition developed in the cow collar design. It employed a plug-and-play design to provide ease in implementation. Moreover, automation reduces human intervention, hence labor costs are decreased when a farm has hundreds of animals. The proposed system also increases the production of dairy and meat products by improving animal health via the regularization of the environment and automated food and watering. The current study represents a comprehensive comparative analysis of the proposed implementation with the existing systems that validate the novelty of this work. This implementation can be further stretched for other applications, i.e., smart monitoring of zoo animals and poultry.

Sufficient production, consistent food supply, and environmental protection in urban +settings are major global concerns for future sustainable cities. Currently, sustainable food supply is under intense pressure due to exponential population growth, expanding urban dwellings, climate change, and limited natural resources. The recent novel coronavirus 2019 (COVID-19) pandemic crisis has impacted sustainable fresh food supply, and has disrupted the food supply chain and prices significantly. Under these circumstances, urban horticulture and crop cultivation have emerged as potential ways to expand to new locations through urban green infrastructure. Therefore, the objective of this study is to review the salient features of contemporary urban horticulture, in addition to illustrating traditional and innovative developments occurring in urban environments. Current urban cropping systems, such as home gardening, community gardens, edible landscape, and indoor planting systems, can be enhanced with new techniques, such as vertical gardening, hydroponics, aeroponics, aquaponics, and rooftop gardening. These modern techniques are ecofriendly, energy- saving, and promise food security through steady supplies of fresh fruits and vegetables to urban neighborhoods. There is a need, in this modern era, to integrate information technology tools in urban horticulture, which could help in maintaining consistent food supply during (and after) a pandemic, as well as make agriculture more sustainable.

AbstractInformation about regional‐level carbon (C) stocks in agroforestry systems (AFS), as well as the annual loss of agroforests and associated C stocks, is scarce, limiting our capacity for increasing C sequestration through establishing, retaining, and enhancing these systems. This study quantified regional‐level C stocks and the associated incremental economic value in the forest land‐use component of three common AFS (hedgerows, shelterbelts, and silvopastures), estimated the annual loss of hedgerow and silvopasture forests and the associated C, and assessed the potential to enhance C storage through the expansion of shelterbelts in central Alberta, Canada, using publicly available satellite imagery, previously collected field data and the Google Earth Engine platform. Results showed that forests in the three AFS stored 699.9 million tons (Mt) C across 9.5 million hectares (Mha) of land in central Alberta and were valued at $102.7 billion based on the 2021 Canadian C tax rate of $40 t−1 CO2‐equivalent. Silvopasture forests in the studied region had the highest C stocks, which were 14.2 and 67.2 times that found in hedgerow and shelterbelt forests, respectively. Between 2001 and 2020, forests in hedgerows and silvopastures declined at rates of 468.1 and 1957.1 ha year−1, respectively, leading to an 8.4 Mt decline in total C storage over the 20 years. However, there is potential to establish new shelterbelts at many road/field margins, which could increase C stocks by 2.3 times the current C stocks in shelterbelt forests. These results highlight the importance of retaining existing and establishing new AFS for increasing C sequestration, emphasizing the impact of agroforest loss on reducing C storage within agroecosystems. The development of policies that assist or reward landowners for providing the ecosystem service of C storage by retaining, establishing, and enhancing agroforests as part of existing agroecosystem management should be encouraged for mitigating climate change.

Cover crops (CCs) are a promising strategy for maintaining and enhancing agroecosystem sustainability, yet CCs’ effects on the subsequent crop yield are highly variable. To quantitatively synthesize the effects of CCs on subsequent crop yield, a meta-analysis of 672 observations collected from 63 recent studies (2015 to 2021) in temperate climates was conducted. Legume CC species increased subsequent crop yield significantly more than grass (by 14%), nonlegume broadleaves (by 7%), and mixtures (by 2%). Incorporation of CC residue into soil increased crop yield by approx. 15% compared to leaving the CC residue on the soil surface. Relative to the no-CC control, the adoption of grass and legume CC species in non-organic vegetable cropping systems enhanced crop yield by 14% and 19%, respectively. Likewise, crop yield with legume CCs in coarse and medium textured soil, and under high precipitation conditions (>700 mm), was significantly greater than the no-CC control by 18%, 4%, and 11%, respectively. Cover crops significantly increased vegetable crop yields and decreased the silage corn yield; however, grain corn, soybean, and winter wheat yield did not decrease with CC. Adoption of CC in no-tillage and plow tillage systems contributed to an increase in crop yield compared to the no-CC control. Our meta-analysis highlights that crop yield response to CC might become more robust when pedo-climatic conditions and agronomic factors are considered.

Asseng, S. et al. Crop models are essential tools for assessing the threat of climate change to local and global food production1. Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature2. Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 °C to 32 °C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each °C of further temperature increase and become more variable over space and time. We thank the Agricultural Model Intercomparison and Improvement Project and its leaders C. Rosenzweig from NASA Goddard Institute for Space Studies and Columbia University (USA), J. Jones from University of Florida (USA), J. Hatfield from United States Department of Agriculture (USA) and J. Antle from Oregon State University (USA) for support. We also thank M. Lopez from CIMMYT (Turkey), M. Usman Bashir from University of Agriculture, Faisalabad (Pakistan), S. Soufizadeh from Shahid Beheshti University (Iran), and J. Lorgeou and J-C. Deswarte from ARVALIS—Institut du Végétal (France) for assistance with selecting key locations and quantifying regional crop cultivars, anthesis and maturity dates and R. Raymundo for assistance with GIS. S.A. and D.C. received financial support from the International Food Policy Research Institute (IFPRI). C.S. was funded through USDA National Institute for Food and Agriculture award 32011-68002-30191. C.M. received financial support from the KULUNDA project (01LL0905L) and the FACCE MACSUR project (031A103B) funded through the German Federal Ministry of Education and Research (BMBF). F.E. received support from the FACCE MACSUR project (031A103B) funded through the German Federal Ministry of Education and Research (2812ERA115) and E.E.R. was funded through the German Science Foundation (project EW 119/5-1). M.J. and J.E.O. were funded through the FACCE MACSUR project by the Danish Strategic Research Council. K.C.K. and C.N. were funded by the FACCE MACSUR project through the German Federal Ministry of Food and Agriculture (BMEL). F.T., T.P. and R.P.R. received financial support from FACCE MACSUR project funded through the Finnish Ministry of Agriculture and Forestry (MMM); F.T. was also funded through National Natural Science Foundation of China (No. 41071030). C.B. was funded through the Helmholtz project ‘REKLIM—Regional Climate Change: Causes and Effects’ Topic 9: ‘Climate Change and Air Quality’. M.P.R. and P.D.A. received funding from the CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS). G.O’L. was funded through the Australian Grains Research and Development Corporation and the Department of Environment and Primary Industries Victoria, Australia. R.C.I. was funded by Texas AgriLife Research, Texas A&M University. E.W. and Z.Z. were funded by CSIRO and the Chinese Academy of Sciences (CAS) through the research project ‘Advancing crop yield while reducing the use of water and nitrogen’ and by the CSIRO-MoE PhD Research Program. Peer reviewed

Although traditional agriculture carried out by ethnic groups is considered for its high biodiversity and important for food security and sovereignty, few studies have investigated the potential of these systems in the interest of promoting a sustainable agricultural development policy according to United Nations Sustainable Development Goals. Using the FAO's Sustainability Assessment of Food and Agriculture (SAFA) methodology, this study analyzed the sustainability of four traditional agricultural systems, three indigenous (Waorani, Shuar, and Kichwa) and one migrant settler populations in the Yasuní Biosphere Reserve (YBR) and identified synergies and trade-offs among the dimensions of sustainability. The results showed different dynamics in all dimensions of sustainability-specifically, trade-offs in the dimensions of good governance with environmental integrity and social well-being, economic resilience, and social well-being. It was identified that the differences in terms of sustainability are narrowing between the indigenous Shuar people's traditional agricultural systems and those of migrant settlers, which provides policymakers with specific information to design sustainable development policies and rescue traditional agricultural systems in the Amazon region.