An integrated approach of field, weather, and satellite data for monitoring maize phenology.

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.

Soil tillage affects soybean growth and promotes heavy metal accumulation in seeds.

When soybean is grown in soils with high heavy metal concentrations, it may introduce those contaminants into the human food chain, posing risks to human health. This study evaluated the effect of tilling the soil with high Cu, Zn, and Mn levels on soybean physiology and metal accumulation in seeds. Disturbed and undisturbed soil samples were collected in two different sites: a vineyard with high heavy metal concentration and a grassland area, containing natural vegetation. Two soybean cultivars were sown and grown in the greenhouse. Photosynthetic parameters and biochemical analysis of oxidative stress were performed. Cu, Zn, and Mn in leaves and seeds, dry mass, and weight of seeds were evaluated. Soil structure had a high influence on plant growth and physiology, while soil site had a high impact on heavy metal accumulation in leaves and seeds. Soybean plants that grown in vineyard soils with high heavy metal concentrations, accumulated 50% more Zn in leaves and seeds, 70% more Cu in leaves, and 90% more Cu in seeds, than those plants grown in grassland soils. Besides, Zn concentration in seeds was higher than the permissible limit. Moreover, the disturbance of both vineyard soil and grassland soil was not good for plant growth and physiology, which have increased TBARS and H2O2 concentration in plants, transpiration rate, metal concentration in leaves and seeds. Soil disturbance may have caused organic matter oxidation and changes in the composition and quantity of soil microorganisms and it affects the availability of other nutrients in the soil.

Effect of hairy vetch cover crop on maize nitrogen supply and productivity at varying yield environments in Southern Brazil.

Cover crops play a critical role on conservation and sustainable agriculture due to their well-documented benefits on both soil and crop productivity. Inclusion of legumes (e.g., hairy vetch, Vicia villosa Roth) in the farming system can reduce the nitrogen (N) fertilizer needs for cereals such as maize (Zea mays L.) crop while maintaining or increasing its productivity. The aims of this research study were to quantify the effect of hairy vetch as a cover crop on: i) successor maize yield under varying yield environments (YEs) and fertilizer N rates, and ii) maize N status [N uptake, N nutritional index (NNI), and N fertilizer replacement value (NFRV)] at flowering time. Two field studies were carried out in Southern Brazil under varying YEs. The factors investigated were: YE (low, medium, and high), hairy vetch cover crop (with and without), and fertilizer N rate (0, 60, 120, 180, and 240 kg N ha-1). Under the combination of low YE and low fertilizer N rates (0-60 kg ha-1) with previous vetch, maize displayed the largest yield response and an improvement in its N status. The NNI determined at maize flowering was an efficient index of the vetch effect, increasing delta maize yield response (yield with- minus without-vetch) as the NNI reduced, with more than 10% delta yield response with NNI below 0.85. The NFRV of the hairy vetch represents potential N savings of 151 kg N ha-1 for the LYE, 95 kg N ha-1 for the MYE and from 59 to 45 kg N ha-1 for the HYE depending on the tested fertilizer N rate. The N coming from the legume cover crop in addition to the N fertilization was critical for supplying N to maize and boosting productivity across all YEs.

Winter Wheat Yield Response to Plant Density as a Function of Yield Environment and Tillering Potential: A Review and Field Studies.

Wheat (Triticum aestivum L.) grain yield response to plant density is inconsistent, and the mechanisms driving this response are unclear. A better understanding of the factors governing this relationship could improve plant density recommendations according to specific environmental and genetics characteristics. Therefore, the aims of this paper were to: i) execute a synthesis-analysis of existing literature related to yield-plant density relationship to provide an indication of the need for different agronomic optimum plant density (AOPD) in different yield environments (YEs), and ii) explore a data set of field research studies conducted in Kansas (USA) on yield response to plant density to determine the AOPD at different YEs, evaluate the effect of tillering potential (TP) on the AOPD, and explain changes in AOPD via variations in wheat yield components. Major findings of this study are: i) the synthesis-analysis portrayed new insights of differences in AOPD at varying YEs, reducing the AOPD as the attainable yield increases (with AOPD moving from 397 pl m-2 for the low YE to 191 pl m-2 for the high YE); ii) the field dataset confirmed the trend observed in the synthesis-analysis but expanded on the physiological mechanisms underpinning the yield response to plant density for wheat, mainly highlighting the following points: a) high TP reduces the AOPD mainly in high and low YEs, b) at canopy-scale, both final number of heads and kernels per square meter were the main factors improving yield response to plant density under high TP, c) under varying YEs, at per-plant-scale, a compensation between heads per plant and kernels per head was the main factor contributing to yield with different TP.

Pre-planting weed detection based on ground field spectral data.

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.

Physiological responses of soybean (Glycine max (L.) Merrill) cultivars to copper excess.

Successive applications of copper fungicides on vines have resulted in increased copper content in vineyard soils over the years. This high copper content has affected the growth of young vines in eradicated vineyards. Thus, the cultivation of annual species for a few years is an alternative to copper phytostabilization, because it would be a good way to decrease copper availability to plants. The aim of this study was to evaluate the physiological responses of different soybean cultivars to copper concentration increase. Four different soybean cultivars were grown under three copper concentrations: 0.5, 20 and 40 µM in nutrient solution. The main outcomes of this study were: i) Cultivar M 6410 IPRO recorded the highest photosynthetic rate when plants were exposed to 40 µM of copper in the nutrient solution; ii) plants in cultivar M 6410 IPRO accumulated large copper concentrations in their roots although did not decrease the root dry mass, possibly due to the higher superoxide dismutase activity; iii) cultivar DM 5958 RSF IPRO recorded drastically reduced photosynthetic rate and dry mass production due to copper excess. We conclude that each cultivar responded differently to the excess of copper, but none of them showed tolerance to it.

Maize Yield and Planting Date Relationship: A Synthesis-Analysis for US High-Yielding Contest-Winner and Field Research Data.

For maize (Zea mays L.), early planting date could be of advantage to high yields but a review of planting date effect on high-yielding data is not yet available. Following this rationale, a synthesis-analysis was conducted from the farmer annual maize contest-winner data (n = 16171 data points; 2011-2016 period); cordially provided by the National Corn Growers Association and a scientific literature dataset collected from research publications since the last three decades. The main objectives of this study were to: (i) identify spatial yield variability within the high-yielding maize dataset; (ii) understand the impacts of planting date on yield variability; (iii) explore the effect of management practices on maize yield-planting date relationship, and (iv) utilize the yield-planting date dataset collected via farmer contest-winner as a benchmarking data to be compared to the compendium of scientific literature available for yield-planting date relationship for the primary US maize producing regions. Major findings of this study are: (i) significant correlation between planting date and latitude, (ii) maize yield was maximized when planting window was 89-106 day of the year (DOY) for the 30-35°N, 107-118 DOY for the 35-40°N, <119 DOY for 40-45°N, and <129 DOY for 45-50°N, and (iii) both, yield contest and literature datasets portrayed that planting date becomes a more relevant factor when planting late, presenting a relatively smaller planting window in high-compared to low-latitudes.