Agronomy Journal最新文献

Root-derived carbon (C) and nitrogen (N) release in relation to chemical composition has rarely been quantified in field studies. The objectives of this study were to evaluate C and N release from six summer cover crop roots and to correlate it with their chemical composition. Root decomposition and N release from velvet bean (Mucuna aterrima), pearl millet (Pennisetum americanum), dwarf pigeon pea (Cajanus cajan), sunn hemp (Crotalaria juncea), showy rattlebox (Crotalaria spectabilis), and jack bean (Canavalia ensiformis) were evaluated over 2 years under no-till subtropical conditions. There was no difference in C release rates for either the labile or recalcitrant C compartments in the first (average: k₁ = 0.0577; k₂ = 0.0017) or the second year (average: k₁ = 0.1657; k₂ = 0.0029). Root C remaining after 140 days did not differ in the first year (average 46.4%), but it was higher in the second year for pearl millet (65.3%) compared to the other species (40.2%). N release was the most intense during the first 21 days and decreased drastically afterward. After 140 days, the N remaining in pearl millet and velvet bean roots was higher (77.7%) than in the other species (47.1%) in the first year, while in the second year, pearl millet contained more N (50%) compared to velvet bean (38%) and jack bean (28.1%). The C and N release rates were poorly correlated to the chemical composition of the summer cover crop roots. Our results reinforce the agronomic recommendation to sow main crops immediately after cover crop management to maximize N recovery from roots.

Nitrogen (N) is essential to maximize corn (Zea mays L.) yield; however, over- and underapplication can cause environmental concerns or yield losses. Optimizing N management is critical to balance productivity and sustainability. This study was conducted during 2022–2024 in Florida to determine corn N response. The experiment included six N rates (0–392 kg N ha⁻¹ by 78.5 increments) over 3 years, with an additional rate (471 kg N ha⁻¹) in the third year, using a randomized complete block design with four replications. Results showed that 314–471 kg N ha⁻¹ produced the highest and statistically similar aboveground biomass (21,598–23,166 kg ha⁻¹), grain yield (12,479–13,588 kg ha⁻¹), and N uptake (227–250 kg ha⁻¹). For grain N removal, (144–162 kg ha⁻¹), 392 and 471 kg N ha⁻¹ were statistically similar, while 314 kg N ha⁻¹ was significantly lower than 392 kg N ha⁻¹, indicating a threshold response beyond 314 kg N ha⁻¹. Agronomic N use efficiency and partial factor productivity were highest at 157 (58.8 kg kg⁻¹) and 78.5 (57.9 kg kg⁻¹) kg N ha⁻¹, respectively. Results suggest no agronomic advantage above the 314 kg N ha⁻¹ rate, and yield decreased at 471 kg N ha⁻¹. Response analysis indicated that 23.2 g N was required per kg of corn grain under irrigation system. The nitrogen nutrition index confirmed that moderate applications (235–314 kg N ha⁻¹) sustained crop N status, while higher rates (>392 kg N ha⁻¹) offered little to no benefit. Collectively, these results support refining N recommendations to optimize agronomic production in Florida.

Hops (Humulus lupulus L.) have been cultivated in Brazil, the world's third-largest beer producer, to meet the growing demand of its expanding brewing industry. Despite advances in agricultural practices, research on the sustainability of tropicalizing hop production remains limited. This study evaluates the social and environmental impacts of hop production in 10 reference farms across the Brazilian states of Alagoas, Goiás, and São Paulo using the Ambitec-Agro tool. This tool assesses the impacts generated by technological innovations adopted in rural environments through change coefficients incorporated into multicriteria indicators, with weights assigned based on the spatial scale of impact occurrence within the farms. Impact indices were calculated across seven aspects: technology efficiency, environmental quality, customer respect, employment, income, health, and management and governance. These aspects were integrated into 27 criteria and 148 indicators. The results indicate positive outcomes across most criteria, with occasional temporary negative results related to energy consumption; use of agricultural inputs and raw materials; water consumption; occupational safety and health; and atmospheric emissions. To address these issues, farmers have invested in solar panels to reduce energy consumption and support other crops, implemented fertigation to improve water and input efficiency, promoted the safe use of protective equipment, and implemented green manure to store carbon. Additionally, hop production has contributed to economic growth by generating income, creating jobs, and promoting gender and generational equality, while also fostering the production of a high-added-value product for the Brazilian agroindustry.

Erratic precipitation events, including winter droughts and spring rains, are increasing in California, challenging the feasibility of continuous rice (CR) (Oryza sativa L.) mono-cropping and increasing fallows. Exposure of CR soils to extended aerobic periods increases soil nitrogen (N) availability, but region-specific agronomic guidelines have yet to be developed. Yield response to N fertilization and disease severity were evaluated in a 3-year field study for two treatments—CR and fallow rice (FR—rice following a year-long fallow). Maximum observed yields did not differ between treatments, averaging 14.0 Mg ha⁻¹ in 2021, 12.6 Mg ha⁻¹ in 2022, and 9.6 Mg ha⁻¹ in 2023. Based on quadratic regressions of yield response to N, the agronomic optimum nitrogen rate was higher for CR in all years. Where no fertilizer N was applied, FR yielded higher than CR, averaging a difference of 2.9 Mg ha⁻¹. The yield differences at 0 kg N ha⁻¹ can be attributed to soil N availability, where FR averaged 31.6 kg N ha⁻¹ more N uptake than CR at maturity. Fertilizer recovery efficiency (FNRE) did not differ between treatments and averaged 59.8%. Stem rot, caused by Sclerotium oryzae, was more severe in CR than in FR, having averaged severity indexes of 3.7 and 3.1, respectively. Based on differences in soil N uptake and FNRE, the N rate can be reduced by approximately 50 kg N ha⁻¹ for fields following a fallow compared to CR fields, allowing growers to maintain yields with lower inputs.

Soil organic carbon (SOC) is essential for maintaining soil fertility, agronomic productivity, and overall soil health. However, the maximum removal of crop residues, such as corn stover, can deplete SOC and soil essential nutrients, necessitating the need for effective mitigation strategies. In this study, we investigated the impacts of various agricultural management practices (residue management, animal manure application, winter rye cover cropping, inorganic nitrogen fertilizer application, and irrigation) on SOC stocks in no-till continuous corn production systems in south-central Nebraska. Over 10 years, manure increased SOC stocks by 6.00% ± 2.04%, whereas residue removal decreased SOC stocks by 2.61% ± 1.81% at the 0- to 150-cm depth. The cumulative SOC at the 0- to 150-cm depth was significantly higher with manure application compared to no amelioration (126 ± 1.5 vs. 116 ± 2.2 Mg ha⁻¹, p < 0.05). This improvement is attributed to reductions in bulk density, increases in soil pH, and increases in soil total nitrogen. Winter rye cover cropping partially offset SOC losses, though its effectiveness was limited by the short growing period and the resulting low biomass production in our study. Inorganic nitrogen fertilizer had minimal long-term impacts on SOC due to its high bioavailability and rapid loss. Irrigation facilitated deeper SOC accumulation via the movement of dissolved OC through the soil profile. Manure application, cover cropping, and the optimization of fertilizer and irrigation rates offer effective strategies for sustainable soil management, enhancing SOC storage, maintaining soil fertility, and supporting long-term agricultural productivity.

Rye (Secale cereale L.) cover crop (RCC) use offers environmental benefits but is often avoided prior to corn (Zea mays L.) due to yield losses and increased N fertilizer needs. This study evaluated corn response to N fertilizer timings following an RCC terminated prior to planting at three diverse locations in Indiana. Treatments included RCC and no RCC and N fertilizer applied at 44 kg N ha⁻¹ in a 5 cm to the side, 5 cm below the seed (5 × 5) starter at planting plus remaining N fertilizer applied at early vegetative (e.g., V4–V6), late vegetative (e.g., V9–V11), and early + late-vegetative growth stages. Total N fertilizer rates were constant across N application timings (201–235 kg N ha⁻¹ across locations) and a 0 kg N ha⁻¹ control was included. A significant (p < 0.1) RCC × N timing yield interaction was observed in 5 of 6 site-years, indicating optimum N timing differs with RCC presence. Without RCC, a 5 × 5 + late N application decreased yield in 4 of 6 site-years and a 5 × 5 + early + late N offered no yield benefit when compared to a 5 × 5 + early N application. With RCC, a 5 × 5 + late or 5 × 5 + early + late N decreased yield in 6 of 6 and 2 of 6 site-years, respectively, when compared to a 5 × 5 + early N application. Overall, late-vegetative growth stage N applications can cause corn yield reductions and should be avoided when following an RCC.

Teff [Eragrostis tef (Zuccagni.) Trotter], an Ethiopian grain recognized for its nutritional value, is gaining prominence as a super millet in India. To optimize its production, a field experiment was conducted at ICAR-Krishi Vigyan Kendra, Haveri, Karnataka, on Alfisols using a factorial randomized complete block design during the Kharif seasons (July–October) of 2019, 2021, and 2022. Factor I included two planting methods: M1—line sowing and M2—transplanting, while Factor II covered five nutrient levels: N1—control, N2—100% organics, N3—20:10:10, N4—30:15:15, and N5—40:20:20 kg N:P₂O₅:K₂O ha⁻¹. Results showed that transplanting significantly improved grain yield (260 kg ha⁻¹), straw yield (447 kg ha⁻¹), and nutrient uptake (N-8.04, P-1.93, K-5.27 kg ha⁻¹) compared to line sowing. However, soil-available nutrients were higher under line sowing. Among nutrient treatments, N5 (40:20:20) led to the highest straw yield (474 kg ha⁻¹), lodging (81.60%), nutrient uptake, soil nutrient levels, and nutritional quality (protein—13.51%, moisture—12.35%, fat—2.84%, ash—3.19%), but N3 (20:10:10) recorded the highest grain yield (297 kg ha⁻¹). Interaction analysis revealed line sowing with N5 outperformed others in all parameters except grain yield. The findings of this study offer significant implications for enhancing millet-based food security strategies in India. By standardizing cultivation practices for teff, this research paves the way for expanding its production in marginal soils and water-limited regions. This contributes not only to nutritional security due to teff's high-value grain composition but also to climate-resilient agriculture through its adaptability and low input requirements.

Optimizing nitrogen (N) management is fundamental for enhancing crop productivity and mitigating environmental impacts in potato (Solanum tuberosum L.) cultivation. Traditional approaches for quantifying plant N uptake and biomass are labor-intensive and destructive, necessitating innovative remote sensing techniques. This study integrates hyperspectral sensing with machine learning (ML) and deep learning algorithms to estimate plant N uptake, biomass accumulation, and predict tuber yield. The hyperspectral data (400–2500 nm) was collected at multiple potato growth stages from an N management study conducted over two growing seasons (2023–2024) at two locations. The study compared three spectral preprocessing methods to optimize model performance: raw spectra, Savitzky–Golay filtering, and first derivative (FD) transformation. Six predictive models were evaluated, including support vector regression, partial least squares regression, random forest regression, ridge regression (RR), least absolute shrinkage and selection operator regression, and a one-dimensional convolutional neural network (1D-CNN). FD preprocessing enhanced estimation accuracy, with the 1D-CNN model achieving the highest performance for N uptake (R² = 0.82) and biomass estimation (R² = 0.84), outperforming traditional ML models. However, for tuber yield prediction, RR provided the best performance (R² = 0.67). SHapley Additive exPlanations analysis identified key spectral regions in the spectrum that contributed to model predictions. The study demonstrates that hyperspectral data, coupled with AI-driven predictive modeling, has the potential to improve N-use efficiency and optimize fertilizer applications, thereby enhancing sustainability in potato production.

Patel, F. Y., Kumar, S., Manuja, S., Meenakshi, & Shah, N. J. (2025). Efficacy evaluation of novel actives Carrabiitol on growth and yield of soybean in different agro-ecological zones. Agronomy Journal, 117, e70136. https://doi.org/10.1002/agj2.70136

The spelling of co-author's name was incorrectly listed as Minakshi. The correct spelling is Meenakshi. This has now been updated in the author byline, Author Contributions, and How to cite this article section.

We apologize for this error.

Processing yield monitor data remains a challenging task in on-farm research, particularly when evaluating yield response to inputs, such as seed and nitrogen (N). This study evaluated the comparative performance of two yield monitor processing algorithms. The first is simple and is based upon empirical thresholds, and the second is RITAS (Rectangle creation, Intersection assignment, Tessellation, Apportioning, and Smoothing) and is a constructive and computationally expensive algorithm. The effects of the different processing algorithms on the model estimates were compared in a simulation study, and two case studies with experimental data were used to demonstrate the applicability of these algorithms in an on-farm setting. In the simulation study, the simple algorithm produced less precise and accurate estimates when compared to RITAS. For instance, the standard deviations of the agronomic optimum nitrogen rate and economic optimum nitrogen rate (EONR) were 83% and 51% greater when using simple, compared to RITAS. Furthermore, when estimating the EONR, the simple algorithm presented a bias of -8 $��\mathrm{kg}��N��{\mathrm{ha}}^{�-�1�}�$. The experimental data results from a strip trial and a checkerboard trial showed differences of 24 $��\mathrm{kg}��N��{\mathrm{ha}}^{�-�1�}�$ and 26 $��\times ��{10}^3��\mathrm{seeds}��{\mathrm{ha}}^{�}$