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.

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.

Water contamination is often reported in agriculturally intensive areas such as the Indo-Gangetic Plain (IGP) in south-eastern Asia. We evaluated the impact of the organic and conventional farming of basmati rice on water quality during the rainy season (July to October) of 2011 and 2016 at Kaithal, Haryana, India. The study area comprised seven organic and seven conventional fields where organic farming has been practiced for more than two decades. Water quality parameters used for drinking (nitrate, NO₃; total dissolved solids (TDS); electrical conductivity (EC) pH) and irrigation (sodium adsorption ratio (SAR) and residual sodium carbonate (RSC)) purposes were below permissible limits for all samples collected from organic fields and those from conventional fields over the long-term (~15 and ~20 years). Importantly, the magnitude of water NO₃ contamination in conventional fields was approximately double that of organic fields, which is quite alarming and needs attention in future for farming practices in the IGP in south-eastern Asia.

Optimizing crop N uptake while minimizing NH₃ volatilization from N fertilizer sources is a critical part of agricultural best management practices. Urea is the most widely used N fertilizer but is also one of the most susceptible to losses as NH₃. Fertilizer sources and additives can be used to reduce NH₃ volatilization. Specifically, urease inhibitors [e.g., N‐(n‐butyl) thiophosphoric triamide (NBPT)] can reduce NH₃ volatilization from urea. A novel fused ammonium sulfate nitrate (ASN) product has recently been developed as another potential alternative N fertilizer source but has not been studied widely. A field study was performed to quantify NH₃ volatilization from the newly available ASN fertilizer as compared with various N fertilizers (ammonium sulfate [AS] and urea) including urea treated with the urease inhibitor NBPT in an alkaline calcareous loam soil in Idaho. Further, we assessed NH₃ volatilization from surface applied and incorporated N fertilizers from these various N sources. Untreated surface‐applied urea volatilized the highest amount of NH₃, whereas incorporation of N fertilizers was effective in reducing volatilization as compared with untreated surface‐applied urea. Our study indicated that the N fertilizer sources (AS, ASN, and urea+NBPT) were equally effective in reducing NH₃ volatilization when compared with surface‐applied urea. Our study will help refine N fertilizer management under multiple field situations and traditional management practices of crop producers in the western United States. Further, site‐years and study locations would be needed to provide evidence of the effectiveness of ASN in reducing NH₃ volatilization under a wider range of soil and environmental conditions.

Optimizing barley (hordeum vulgare L.) production in Idaho and other parts of the Pacific Northwest (PNW) should focus on farm resource management. The effect of post-harvest residue management on barley residue decomposition has not been adequately studied. Thus, the objective of this study was to determine the effect of residue placement (surface vs. incorporated), residue size (chopped vs. ground-sieved) and soil type (sand and sandy loam) on barley residue decomposition. A 50-day(d) laboratory incubation experiment was conducted at a temperature of 25°C at the Aberdeen Research and Extension Center, Aberdeen, Idaho, USA. Following the study, a Markov-Chain Monte Carlo (MCMC) modeling approach was applied to investigate the first-order decay kinetics of barley residue. An accelerated initial flush of residue carbon(C)-mineralization was measured for the sieved (Day 1) compared to chopped (Day 3 to 5) residues for both surface incorporated applications. The highest evolution of carbon dioxide (CO₂)-C of 8.3 g kg^-1 dry residue was observed on Day 1 from the incorporated-sieved application for both soils. The highest and lowest amount of cumulative CO₂-C released and percentage residue decomposed over 50-d was observed for surface-chopped (107 g kg^-1 dry residue and 27%, respectively) and incorporated-sieved (69 g kg^-1 dry residue and 18%, respectively) residues, respectively. There were no significant differences in C-mineralization from barley residue based on soil type or its interactions with residue placement and size (p >0.05). The largest decay constant k of 0.0083 d^-1 was calculated for surface-chopped residue where the predicted half-life was 80 d, which did not differ from surface sieved or incorporated chopped. In contrast, incorporated-sieved treatments only resulted in a k of 0.0054 d^-1 and would need an additional 48 d to decompose 50% of the residue. Future residue decomposition studies under field conditions are warranted to verify the residue C-mineralization and its impact on residue management.