A growing world population and increases in food and energy consumption have placed production agriculture in a difficult situation. The rapid growth in food production through specialized operations such as monoculture cropping systems has aligned to satisfy increases in demand for food and fiber. However, its adverse impacts on natural resources pose huge challenges for the sustainability of food production. The situation is direr for developing countries or rural regions of the world due to the limited resources available to farming communities in these regions. To avoid production agriculture being at the proverbial crossroads we suggest an alternate approach. One that involves the sustainable use of natural resources without adverse environmental impacts by relying less on production inputs whether it be agrochemicals or machineries. We examined the extent to which regenerative agriculture, permaculture, and smart technology have evolved in response to sustainable agricultural production, agricultural decision support system, and overall global food security. Collectively, regenerative agriculture and permaculture are semi‐closed holistic systems approach designed to reduce or eliminate dependence on external inputs (e.g., chemicals) which restores and maintains natural systems (e.g., soil quality, biodiversity, and ecosystem services). We suggest that fully embracing modern regenerative agriculture as well as integrated permaculture will improve soil health, ecosystem biodiversity, land and resource conservation, agricultural sustainability, and food security. Identifying and implementing practices towards regenerative agriculture, integrated permaculture, digital agriculture, and sustainable agricultural management utilizing modern agricultural technologies infused with data science (artificial intelligence [AI] or machine learning [ML]) is critical.

Per- and polyfluoroalkyl substances (PFAS) are highly persistent synthetic organic contaminants that can cause serious human health concerns such as obesity, liver damage, kidney cancer, hypertension, immunotoxicity and other human health issues. Integrated crop–livestock systems combine agricultural crop production with milk and/or meat production and processing. Key sources of PFAS in these systems include firefighting foams near military bases, wastewater sludge and industrial discharge. Per- and polyfluoroalkyl substances regularly move from soils to nearby surface water and/or groundwater because of their high mobility and persistence. Irrigating crops or managing livestock for milk and meat production using adjacent waters can be detrimental to human health. The presence of PFAS in both groundwater and milk have been reported in dairy production states (e.g., Wisconsin and New Mexico) across the United States. Although there is a limit of 70 parts per trillion of PFAS in drinking water by the U.S. EPA, there are not yet regional screening guidelines for conducting risk assessments of livestock watering as well as the soil and plant matrix. This systematic review includes (i) the sources, impacts and challenges of PFAS in integrated crop–livestock systems, (ii) safety measures and protocols for sampling soil, water and plants for determining PFAS concentration in exposed integrated crop–livestock systems and (iii) the assessment, measurement and evaluation of human health risks related to PFAS exposure.

Phenols are major compounds produced by plant species as a peripheral stimulus or as a regulatory defense mechanism under different environmental biotic stresses. These secondary metabolites are generated from shikimic and acetic acid metabolic pathways. The aromatic benzene ring compound plays an important role in plant development, especially in the defense forefront. They provide structural integrity and support to the plants. Phenolic phytoalexins released by pathogen/arthropod-attacked or wounded plants nullify or repel organisms for the advantage of the host. The allelopathic potential of phenolic compounds is observed in both natural and managed ecosystems. The global impacts of climatic variabilities such as drought, increased carbon dioxide, or greenhouse gas emissions alter the quantitative response of plant phenols. This review primarily discusses the different aspects of phenolic interactions concerning health, antioxidant properties, and insect-plant interaction as a nexus of soil and plant relations in response to variable climatic conditions.

The excessive consumption of herbicides has gradually led to the herbicide resistance weed phenomenon. Managing herbicide resistance weeds can only be explicated by applying high-tech strategies such as artificial intelligence (AI)-based methods. We review here AI-based methods and tools against herbicide-resistant weeds. There are a few commercially available AI-based tools and technologies for controlling weed, as machine learning makes the classification process significantly easy, namely remote sensing, robotics, and spectral analysis. Although AI-based techniques make outstanding improvements against herbicide resistance weeds, there are still limited applications compared to the real potential of the methods due to the challenges. In this review, we identify the need for AI-based weed management against herbicide resistance, comparative evaluation of chemical vs. non-chemical management, advances in remote sensing, and AI technology for weed identification, mapping, and management. We anticipate the ideas will contribute as a forum for establishing and adopting proven AI-based technologies in controlling more weed species across the world.

California has unsustainable use of agricultural water and energy, as well as problems of severe drought, nitrate pollution and groundwater salinity. As the leading producer and exporter of agricultural produce in the United States, 5.6 percent of California’s energy is currently used for pumping groundwater. These problems and new regulatory policies (e.g., Sustainable Groundwater Management Act, Irrigated Lands Regulatory Program) pressure growers to schedule, account and maintain records of water, energy and nutrients needed for crop and soil management. Growers require varying levels of decision support to integrate different irrigation strategies into farm operations. Decision support can come from the public or private sector, where there are many tradeoffs between cost, underlying science, user friendliness and overall challenges in farm integration. Thus, effective irrigation management requires clear definitions, decision support and guidelines for how to incorporate and evaluate the water–nutrient–energy nexus benefits of different practices and combinations of practices under shifting water governance. The California Energy Commission-sponsored Energy Product Evaluation Hub (Cal-EPE Hub) project has a mission of providing science-based evaluation of energy-saving technologies as a direct result of improved water management for irrigation in agriculture, including current and future irrigation decision support systems in California. This project incorporates end-user perceptions into evaluations of existing decision support tools in partnership with government, agricultural and private stakeholders. In this article, we review the policy context and science underlying the available irrigation decision support systems (IDSS), discuss the benefits/tradeoffs and report on their efficacy and ease of use for the most prevalent cropping systems in California. Finally, we identify research and knowledge-to-action gaps for incorporating irrigation decision support systems into new incentives and requirements for reporting water and energy consumption as well as salinity and nitrogen management in the state of California.

In this study, an inexpensive Nix Pro (Nix Sensor Ltd.) color sensor was used to develop prediction models for soil iron (Fe) content. Thirty‐eight soil samples were collected from five agricultural fields across the Animas watershed to develop and validate soil Fe prediction models. We used color space models to develop three different parameter sets for Fe prediction with Nix Pro. The different color space sets were used to develop three new predictive models for Nix Pro‐based Fe content against the lab‐based inductively coupled plasma analyzed Fe content. The model performances were assessed using the coefficient of determination, root mean square error, and model p‐value. Three models (International Commission on Illumination's lightness, ±a axis (redness to greenness), and ± b axis (yellowness to blueness) [CIEL*a*b]; red, green, blue [RGB]; and cyan, magenta, yellow, key [black] [CMYK]) were significant in predicting the Fe content using colorimetric variables with R² ranging from 0.79 to 0.81. The mean square prediction error (MSPE) and Kling–Gupta efficiency (KGE) Index were calculated to validate models and CMYK was predicted to be a better model (MSPE = 0.13; KGE = 0.601) than CIEL*a*b and RGB models. The results suggest Nix Pro is useful in predicting soil Fe content.