Mississippi state and county level yield gap in corn production

Dhillon, Jagmandeep; Li, Xiaofei; Bheemanahalli, Raju; Reed, Vaughn

doi:10.1002/ael2.20092

Cited by 10 publications

(13 citation statements)

References 28 publications

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“…Canola requires high amounts of fertilizer N compared to cereal crops excluding corn (Y. Lin et al., 2020; McGeary et al., 2022; Yahbi et al., 2022). Numerous studies identified that N requirements in canola and other crops differ based on geographic location, soil and climate conditions, and management practices including hybrid selection, tillage practices, and timing of fertilizer application (Dhillon et al., 2022; McGeary et al., 2022). However, previous studies stated that the application of a high N rate only increases the seed yield of canola and reduces nitrogen use efficiency (NUE) (Ahmad et al., 2011; Ma et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

Optimizing nitrogen application rates for winter canola in Mississippi

Gajula,

Singh,

Kaur

et al. 2024

Agrosystems Geosci & Env

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The production of canola (Brassica napus L.) in the United States is low, whereas the demand is high. Most US canola research is centered in the Midwest, with minimal to no research in Southern states including Mississippi. Therefore, a study was conducted in Mississippi to assess the feasibility of canola as a double crop with soybean (Glycine max L.) rotation and determine its optimum nitrogen (N) requirement. After canola was harvested, soybean was planted within the same experimental plots. In total, six N treatments (0 [control], 34, 67, 101, 135, and 168 kg N ha−1) were applied to canola and replicated four times across all site‐years in a randomized complete block design. The application of N increased seed yield, aboveground biomass, and N content compared to the control in canola. Across all site‐years, the highest seed yield was 1726 kg ha−1 at 168 kg N ha−1. Moreover, N uptake, plant height, and test weight were highest at 135 kg N ha−1, which were 76%, 21%, and 44% greater than control, respectively. Oil content and seed weight were inversely related to N rates indicating dilution with increased N. Overall, 141 kg N ha−1 was found to be the agronomic optimum nitrogen rate, and no further supplementation was deemed necessary to maximize canola yield in Mississippi. Additionally, the following soybean crop benefitted from the N applied to canola and produced greater yields. This study establishes that canola has the potential for double cropping without adversely affecting subsequent soybean yield, provided optimum N rates are applied.

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Section: Introductionmentioning

confidence: 99%

Optimizing nitrogen application rates for winter canola in Mississippi

Gajula,

Singh,

Kaur

et al. 2024

Agrosystems Geosci & Env

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“…The yield of maize in the United States has increased with the adoption of improved management practices under irrigated conditions, but yield sensitivity to soil moisture has also increased by 55% (Lobell et al, 2020). In addition, the increased yield gap between irrigated and rainfed systems indicates a sensitivity of maize to low rainfall (Dhillon et al, 2022; Leng, 2021). Studies investigated the impact of drought on crop yield, but prior research has not comprehensively assessed the effects of soil moisture gradients on reproductive, yield, and quality components in maize (Tang et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

“…Low rainfall during the critical growth stages often limits crop growth and development in maize‐producing regions, thereby limiting crop yields (Qi et al, 2022; Sah et al, 2020). The likelihood of rainfed maize experiencing suboptimal soil moisture conditions is relatively high, and this can result in a yield loss of up to 78% (Dhillon et al, 2022; Leng, 2021).…”

Section: Introductionmentioning

confidence: 99%

Impact of soil moisture stress during the silk emergence and grain‐filling in maize

Vennam,

Poudel,

Ramamoorthy

et al. 2023

Physiologia Plantarum

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Suboptimal soil moisture during the growing season often limits maize growth and yield. However, the growth stage‐specific responses of maize to soil moisture regimes have not been thoroughly investigated. This study investigated the response of maize to five different soil moisture regimes, that are, 0.25, 0.20, 0.15, 0.10, and 0.05 m3 m−3 volumetric water content (VWC), during flowering and grain‐filling stages. Sub‐optimal soil moisture at the flowering and grain‐filling stages reduced ear leaf stomatal conductance by 73 and 64%, respectively. An increase in stress severity caused significant reductions in ear leaf chlorophyll content and greenness‐associated vegetation indices across growth stages. Fourteen days of soil moisture stress during flowering delayed silk emergence, reduced silk length (19%), and silk fresh weight (34%). Furthermore, sub‐optimal soil moisture caused a significant reduction in both kernel number (53%) and weight (54%). Soil moisture stress at the flowering had a direct impact on kernel number and an indirect effect on kernel weight. During grain‐filling, disruption of ear leaf physiology resulted in a 34% decrease in kernel weight and a 43% decrease in kernel number. Unlike grain‐filling, treatments at the flowering significantly reduced kernel starch (3%) and increased protein by 29%. These findings suggest that developing reproductive stage stress‐tolerant hybrids with improved resilience to soil moisture stress could help reduce the yield gap between irrigated and rainfed maize.

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“…Hand harvesting at the end of the season provides accurate yield data, but it is labor intensive and provides no information for in-season management. Yield data collected at harvest serves only as historical information and has limited utility in future growing season as yield varies temporally and spatially 5,6 .…”

Section: Introductionmentioning

confidence: 99%

A modular artificial neural network technique for early estimation of cotton yield using unmanned aerial system

Shrestha,

Swaminathan,

Thomasson

et al. 2023

Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VIII

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Early crop yield estimation aids growers of cotton and other crops in making in-season management decisions and estimating the crop's market value. However, predicting yield early in the growing season is challenging for various reasons including environmental factors and soil variability. Several techniques have been used to estimate cotton yield, and in the last decade machine learning (especially artificial neural networks or ANNs) have been widely adopted. In a standard ANN model, all the input data are collated without considering the temporal characteristics of the data, such as when the data were collected relative to the growth stage of the plants. A modular network called plus artificial neural network (ANN+) was devised to independently receive crop data that has been collected at different stages of crop growth. This study evaluated the potential of adopting ANN+ for early estimation of cotton yield. For this purpose, a field experiment was conducted in central Texas in e2020 and 2021. The study site consisted of three different treatments: variable nitrogen rate, variable fertilizer and irrigation × variety. An unmanned aerial vehicle equipped (UAV) with a fiveband multispectral sensor was flown at various cotton growth stages to collect remote sensing data multiple times within 100 days after planting. The UAV was flown at 30 m above ground level, producing a spatial resolution of approximately 0.02 m. The multispectral imagery was used to extract crop spectral, textural and structural information. Along with this information, weather information in term of growing degree days, solar insolation and precipitation were collected for yield estimation. The custom ANN+ model achieved an R 2 of 0.90 and mean absolute percentage error of 12.29% for cotton yield estimation. Seasonal temperature data contributed the most information to the model, but crop structural and textural metrics from the image data also contributed strongly to the model, suggesting that autonomous aerial systems can be an important part of providing cotton growers early predictions of yield.

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Mississippi state and county level yield gap in corn production

Cited by 10 publications

References 28 publications

Optimizing nitrogen application rates for winter canola in Mississippi

Optimizing nitrogen application rates for winter canola in Mississippi

Impact of soil moisture stress during the silk emergence and grain‐filling in maize

A modular artificial neural network technique for early estimation of cotton yield using unmanned aerial system

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