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The effect of environmental factors on the proportion of the nitrogen (N) derived from the atmosphere (Ndfa) in soybean [Glycine max (L.) Merril] have been traditionally approached individually given their intrinsic complexity. Alternatively, a more in-depth investigation of such complex interactions can be pursued by delimiting environments where Ndfa is uniform. Thus, the aim of this study was to define environments on 24 sites by extensively characterizing weather, plant, and Ndfa-related traits to find discriminant variables defining three Ndfa classes (low, medium, and high). The Ndfa was determined at the beginning of the seed filling (R5 growth stage) using the 15N natural abundance method. Twenty environmental variables were utilized to categorize Ndfa in low (< 57%), medium (57–66%), and high (≥ 66%) classes via implementation of discriminant multivariate analysis. The Ndfa averaged 60%, lower to previous values reported for Brazil (ranging from 69 to 94%). Mean air temperature, associated to SOM and soil N, was the most important variable related to low Ndfa, while improving soil fertility (soil pH, base saturation, exchangeable Ca and Mg, and available P) was critical for high Ndfa and, consequently, seed yield. The high contribution of those factors highlight the importance of implementing strategies to improve soil fertility, to promote better plant growth, and thus enhancing Ndfa contribution to crop N uptake. [ABSTRACT FROM AUTHOR]

Efficient, more accurate reporting of maize (Zea mays L.) phenology, crop condition, and progress is crucial for agronomists and policy makers. Integration of satellite imagery with machine learning models has shown great potential to improve crop classification and facilitate in-season phenological reports. However, crop phenology classification precision must be substantially improved to transform data into actionable management decisions for farmers and agronomists. An integrated approach utilizing ground truth field data for maize crop phenology (2013–2018 seasons), satellite imagery (Landsat 8), and weather data was explored with the following objectives: (i) model training and validation—identify the best combination of spectral bands, vegetation indices (VIs), weather parameters, geolocation, and ground truth data, resulting in a model with the highest accuracy across years at each season segment (step one) and (ii) model testing—post-selection model performance evaluation for each phenology class with unseen data (hold-out cross-validation) (step two). The best model performance for classifying maize phenology was documented when VIs (NDVI, EVI, GCVI, NDWI, GVMI) and vapor pressure deficit (VPD) were used as input variables. This study supports the integration of field ground truth, satellite imagery, and weather data to classify maize crop phenology, thereby facilitating foundational decision making and agricultural interventions for the different members of the agricultural chain. [ABSTRACT FROM AUTHOR]

The application of phosphorus (P) to soil can increase its availability to plants and alter P fractions in annual and perennial organs of Cordia trichotoma. If a portion of P accumulates in perennial organs in organic fractions it can be used in the next growth season, possibly decreasing plant dependence on P derived from soil fertilization. However, if P is preferentially accumulated in inorganic fractions in annual organs, plants will be more dependent on phosphate fertilization. This study aimed to evaluate the distribution of P fractions in organs of C. trichotoma grown on sandy soil treated with 120 and 360 kg P2O5 ha−1. The control was a zero application. After 24 months following fertilization, C. trichotoma seedlings were cut and separated into leaves, branches, stems and roots, dried, ground and subjected to chemical fractionation of P, which estimates fractions of total soluble P, soluble inorganic and organic P, lipid P, P associated ribonucleic acid and deoxyribonucleic acid, and residual P. P in annual organs, as leaves, accumulated preferentially in the soluble inorganic fraction in both treatments. In perennial organs such as stems and branches, P accumulated preferentially in the soluble organic fraction. The application of 300% of the recommended dosage (360 kg P2O5 ha−1) promoted the accumulation of P in soluble organic fractions which may contribute to annual growth the following season and be a strategy to reduce the dependence of 2-year-old stands on soil-derived P and on fertilizers. [ABSTRACT FROM AUTHOR]

Soil microbial community and enzyme activity regulate carbon and nutrient cycling in soils. No‐till has elevated levels of carbon at the soil surface compared to intensively tilled soils. The introduction of crop diversification in agricultural systems increases soil carbon protection and aggregate stability. Thus, the association of no‐till with crop diversification may change the soil microbial distribution and enhance activity in comparison with tilled soils with no crop diversification. We tested this hypothesis assessing the soil microbial community and enzyme activity in a long‐term (32 yr) experiment with contrasting soil management (no‐tillage, (NT) and conventional tillage (CT)) and cropping systems in southern Brazil. Long‐term NT increased microbial biomass at 0–5 cm. In contrast, soil microbial biomass was higher in deeper soil layers (10–30 cm) of CT. No‐tillage and crop diversification increased the activity of β‐glucosidase, acid phosphatase and N‐acetyl‐glucosaminidase. The benefits of an intensified/diversified crop rotation were offset by soil tillage. The higher abundance of PLFA groups at 0–5 cm and 10–30 cm of respective NT and CT soils were correlated with increased C and nutrient levels (N, Ca2+, Mg2+) and decreased Al3+. Highlights: Crop diversification increased the soil microbial biomass and individual PLFA groups.No‐till without crop diversification did not change microbial biomass.No‐till with crop diversification increased enzyme activity.Subsoil fertility restricted microbial responses in a no‐till Oxisol. [ABSTRACT FROM AUTHOR]

BACKGROUND: Site‐specific weed management (SSWM) demands higher resolution data for mapping weeds in fields, but the success of this tool relies on the efficiency of optical sensors to discriminate weeds relative to other targets (soils and residues) before cash crop establishment. The objectives of this study were to (i) evaluate the accuracy of spectral bands to differentiate weeds (target) and other non‐targets, (ii) access vegetation indices (VIs) to assist in the discrimination process, and (iii) evaluate the accuracy of the thresholds to distinguish weeds relative to non‐targets for each VI using training and validation data sets. RESULTS: The main outcomes of this study for effectively distinguishing weeds from other non‐targets are (i) training and validation data exhibited similar spectral curves, (ii) red and near‐infrared spectral bands presented greater accuracy relative to the other bands, and (iii) the tested VIs increased the discrimination accuracy related to single bands, with an overall accuracy above 95% and a kappa above 0.93. CONCLUSION: This study provided a novel approach to distinguish weeds from other non‐targets utilizing a ground‐level sensor before cash crop planting based on field spectral data. However, the limitations of this study are related to the spatial resolution to distinguish weeds that might be closer to the one this study presented, and also related to the soil and crop residues conditions at the time of collecting the readings. Overall the results presented contribute to an improved understanding of spectral signatures from different targets (weeds, soils, and residues) before planting time supporting SSWM. © 2019 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

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Uniform within-row plant spacing is a key crop management strategy to achieve high corn (Zea mays L.) yield. A new precision planting concept is emerging based on the use of modern devices in planters. The objective of this study was to investigate the impact of optimizing within-row plant spacing to enhance plant nutritional status and corn yield. The treatments investigated were (a) traditional planter with mechanical horizontal plate metering system (TP), (b) precision planter with vSet (Precision Planting, Tremont, IL) vacuum meter system (PP) and (c) PP pulled by a tractor equipped with an real time kinematic (RTK)-based auto-steering system (APP). The experimental design was a randomized block with three replications, and the plant evaluations were determined based on a main plot and a plant-to-plant study. Two types of vegetation indexes (VI and normalized diff erence, NDVI) were used for assessing plant nutritional status. A high standard of uniform plant spacing (CV < 10%) was required to achieve the highest corn yield. Optimizing the within-row plant spacing resulted in higher VI and NDVI. Based on the average of two experiments, PP improved the uniformity of plant spacing (CV = 22.5%) compared to TP (CV = 38.7%). This optimization of within-row plant spacing increased the corn yield by 10.7%. Only in the year with higher corn yield potential did APP result in a yield increase of 6.9% in relation to PP. Although the precision planters investigated had decreased the error in plant distribution, no one reached the plant spacing uniformity required to achieve higher corn yields. [ABSTRACT FROM AUTHOR]