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There is an intense argument about the environmental impact of annual vs. perennial forage production systems. In this study, a systematic review was employed to obtain 47 empirical studies from 13 published papers between the years 2017–2023 to help clarify the issue. The objective of this study was to determine how perennial and annual forage (business-as-usual, BAU) production systems affect dry matter yield (DM) and energy of production including specific environmental impact variables. Impact variables were classified into three main groups: human health, ecosystem quality, and resource consumption. Net energy of lactation (NEL) was considered as a functional unit. Overall, perennial forage production systems varied less in DM yield and energy production than annual monocrop systems, indicating stability in perennial production. There was no statistically significant difference in human health and resource consumption variables between perennial and annual forage production systems, except for ozone layer depletion potential. However, perennial forage systems significantly lowered variables within the ecosystem quality category. Ecotoxicity potential decreased by two and 18 times compared with BAU—control (only annual monoculture forages), and BAU—improved (any annual cropping system other than BAU—control), respectively. Perennial forage systems showed a significant effect size of −8.16, which was slightly less than the effect size of the BAU—improved system but two times less than BAU—control in terms of terrestrial acidification potential. While BAU—control showed an insignificant effect size in relation to eutrophication potential (EUP), perennial forage systems reduced EUP by approximately five and two times compared with BAU—control and BAU—improved, respectively. Therefore, this study highlights the importance of promoting perennial forage production system to foster resilience and stability in DM yield and energy production, with improvements in environmental human health (ozone layer depletion potential) and ecosystem quality variables. [ABSTRACT FROM AUTHOR]

No-till (NT) alleviates adverse effects of agricultural production on environmental quality, improves soil health, and reduces greenhouse gas emissions, yet this has not been demonstrated in humid subtropical Mississippi. Research objectives were to evaluate nutrient and C dynamics and soil quality via Soil Management Assessment Framework (SMAF) following 15 years of continuous management under corn (Zea mays L.) (2008–2018) and soybean [Glycine max (L.) Merr.] (2019–2022) on a Dundee silt loam soil. After 15 years, soil organic carbon (SOC) stocks (0- to 30-cm depth) under NT were 25% higher than conventional tillage (CT). Between 2020 and 2022, SOC stocks increased 3 Mg ha−1 under NT and decreased 2 Mg ha−1 under CT. Increased SOC retention under NT could represent an opportunity for increasing the systems’ profitability as C markets expand. Greater SOC and aggregation scores improved SMAF soil quality index (SQI) under NT (71%), compared to CT (66%), underscoring the critical role of organic matter in soil functioning in managed agrosystems. Soybean yields were not linked to SMAF SQI (p > 0.05), suggesting that crop yields are mostly affected by non-edaphic factors (e.g., climate extremes). SMAF illustrated positive impacts of NT on SOC sequestration and soil health, which may foster its adoption as a conservation practice in the Lower Mississippi River basin. As such, NT stands as a climate-smart management practice that allows for sustainable intensification in humid subtropical regions. [ABSTRACT FROM AUTHOR]

Exponential population increases are threatening food security, particularly in mountainous areas. One potential solution is dual-use intercropped agroforestry systems such as olive (Olea europaea)–maize (Zea mays), which may mitigate risk by providing multiple market sources (oil and grain) for smallholder producers. Several studies have conducted integrated agroforestry land suitability analyses; however, few studies have used machine learning (ML) algorithms to evaluate multiple variables (i.e., soil physicochemical properties and climatic and topographic data) for the selection of suitable rainfed sites in mountainous terrain systems. The goal of this study is therefore to identify suitable land classes for an integrated olive–maize agroforestry system based on the Food and Agriculture Organization (FAO) land suitability assessment framework for 1757 km2 in Khyber Pakhtunkhwa province, Pakistan. Information on soil physical and chemical properties was obtained from 701 soil samples, along with climatic and topographic data. After determination of land suitability classes for an integrated olive–maize-crop agroforestry system, the region was then mapped through ML algorithms using random forest (RF) and support vector machine (SVM), as well as using traditional techniques of weighted overlay (WOL). Land suitability classes predicted by ML techniques varied greatly. For example, the S1 area (highly suitable) classified through RF was 9%↑ than that of SVM, and 8%↓ than that through WOL. The area of S2 (moderately suitable) classified through RF was 18%↑ than that of SWM and was 17%↓ than the area classified through WOL; similarly, the S3 (marginally suitable) class area via RF was 27%↓ than that of SVM, and 45%↓ than the area classified through WOL. Conversely, the area of N2 (permanently not suitable class) classified through RF and SVM was 6%↑ than the area classified through WOL. Model performance was assessed through overall accuracy and Kappa Index and indicated that RF performed better than SVM and WOL. Crop suitability limitations of the study area included high elevation, slope, pH, and large gravel content. Results can be used for sustainable intensification in mountainous rainfed regions by expanding intercrop agroforestry systems in developing nations to close yield gaps. [ABSTRACT FROM AUTHOR]

Soil nutrient content varies spatially across agricultural fields in hard-to-predict ways, particularly in floodplains with complex fluvial depositional history. Satellite reflectance data from the Sentinel-2 (S2) mission provides spatially continuous land reflectance data that can aid model development when used with point observations of nutrients. Reflectance from vegetation is assumed to obstruct land reflectance of bare soil, such that researchers have masked vegetation in models. We developed a routine for masking vegetation within Google Earth Engine (GEE) using Random Forest classification for iterative application to libraries of S2-images. Using gradient boosting, we then developed soil nutrient models for surface soils at a 250-ha agricultural site using S2 images. Soils were sampled at 2145 point locations to a 23-cm depth and analyzed for Ca, K, Mg, P, pH, S, and Zn. Results showed that masking vegetation improved model performance for models from subsets of the data (80% of samples used for model development, 20% validation), but full data sets did not require masking to achieve accuracy. Models of Ca, K, Mg, and S were successful (validation R2 > 0.60 to 0.96), but models for pH, P, and Zn failed. Bare soil composite images from S2 data are helpful in predicting soil fertility in low-relief floodplains. [ABSTRACT FROM AUTHOR]

Insect manure or "frass" has emerged as an alternative nutrient source for alleviating the dependence on fossil fuel-based fertilizers, reducing food waste, and promoting food security. Yet, research on insect frass chemical composition is in its infancy. Here, we assessed the chemical properties of yellow mealworm (Tenebrio molitor L.) frass compared with poultry litter (PL). Insect frass was obtained from the National Biological Control Laboratory (NBCL; IF-L) and an insect-rearing company (IF-C). PL was collected from facilities in Arkansas (PL-AR) and North Carolina (PL-NC). Samples were analyzed for pH, electrical conductivity, macro- and micronutrients, heavy metals, pathogens, and indicator microorganisms. On average, insect frass had 43% and 47% higher C and N than PL, respectively (P  < 0.05). Considering a 5 mg/ha application rate, IF-C can supply 159 kg N/ha, twice the N supply of PL-AR (78 kg/ha). IF-L had a 53% higher P supply than PL-NC. Mean K, Ca, S, and micronutrient contents were higher in PL than in frass (P  < 0.05), whereas As, Cd, Cr, and Pb were nearly absent in frass. Chemical composition and pathogens in fertilizer sources were largely affected by insect-rearing substrate and supplements used in poultry and insect production. Insect frass utilized in this study had optimum C and N rates relative to PL, suggesting a promising soil amendment for improving soil health and C sequestration, thus contributing to sustainable agricultural intensification and reuse of food waste in circular economies. [ABSTRACT FROM AUTHOR]

Accurate and economically sound soil fertility recommendations are critical for ensuring profitable food production for smallholder farmers. However, such recommendations are lacking in many areas due to insufficient soil and crop response data. This study was conducted between 2016 and 2022 using boundary line analysis to evaluate crop response to soil fertility on 644 farms in Guatemala. We identify optimal soil property conditions for maize (Zea mays L.), common bean (Phaseolus vulgaris L.), and coffee (Coffea arabica L.) production in Guatemala and use crop price information to develop economically sound fertilization recommendations. We also analyzed the drivers of yield outcomes and assessed their relative importance with machine learning (ML) models. Results demonstrate that a majority of country‐level data currently have sub‐optimal soil nutrient levels and by optimizing nutrients, yields and profits could be improved in 64%, 51%, and 69% of maize, common bean, and coffee crops, respectively. In addition, ML underscored the central role of climate in shaping yield outcomes. Variable importance rankings from random forest indicated climate and water balance variables increased predictive accuracy more than soil parameters, highlighting the critical need for climate‐smart adaptations in the region. The pairing of boundary line with ML analysis provided unique and complementary information about factors that drive yield of major crops in Guatemala. This is an effective approach for developing fertility recommendations in Guatemala and could be replicated in other countries where critical nutrient recommendations are currently lacking for closing yield gaps and identifying yield response to climatic stochasticity. Core Ideas: Boundary lines identified critical N, P, K values and pH for Guatemalan maize, common bean, and coffee crops.Sub‐optimal nutrient levels were observed in 64% of maize, 51% of common bean, and 69% of coffee plots.Underperforming farms would see revenue gains through yield maximizing N, P, and K usage.Machine learning revealed maize yields increased with higher temperatures relative to common beans and coffee.Random forest underscored the role of climate in driving yield and the importance of climate‐smart agriculture. [ABSTRACT FROM AUTHOR]