Examining the Corn Seedling Emergence-Temperature Relationship for Recent Hybrids: Insights from Experimental Studies.

Corn seedling emergence is a critical factor affecting crop yields. Accurately predicting emergence is crucial for precise crop growth and development simulation in process-based crop models. While various experimental studies have investigated the relationship between corn seedling emergence and temperature, there remains a scarcity of studies focused on newer corn hybrids. In the present study, statistical models (linear and quadratic functional relationships) are developed based on the seedling emergence of ten current corn hybrids, considering soil and air temperatures as influencing factors. The data used for model development are obtained from controlled soil plant atmospheric research chamber experiments focused on corn seedling emergence at five different temperatures. Upon evaluating the developed models, the quadratic model relating the air temperature with time to emergence was found more accurate for all corn hybrids (coefficient of determination (R2): 0.97, root mean square error (RMSE): 0.42 day) followed by the quadratic model based on soil temperature (R2: 0.96, RMSE: 1.42 days), linear model based on air (R2: 0.94, RMSE: 0.53 day) and soil temperature (R2: 0.94, RMSE: 0.70 day). A growing degree day (GDD)-based model was also developed for the newer hybrids. When comparing the developed GDD-based model with the existing GDD models (based on old hybrids), it was observed that the GDD required for emergence was 16% higher than the GDD used in the current models. This showed that the existing GDD-based models need to be revisited when adopted for newer hybrids and adapted to corn crop simulation models. The developed seedling emergence model, integrated into a process-based corn crop simulation model, can benefit farmers and researchers in corn crop management. It can aid in optimizing planting schedules, supporting management decisions, and predicting corn crop growth, development, and it yields more accurately.

Climate trends and maize production nexus in Mississippi: empirical evidence from ARDL modelling.

Climate change poses a significant threat to agriculture. However, climatic trends and their impact on Mississippi (MS) maize (Zea mays L.) are unknown. The objectives were to: (i) analyze trends in climatic variables (1970 to 2020) using Mann-Kendall and Sen slope method, (ii) quantify the impact of climate change on maize yield in short and long run using the auto-regressive distributive lag (ARDL) model, and (iii) categorize the critical months for maize-climate link using Pearson's correlation matrix. The climatic variables considered were maximum temperature (Tmax), minimum temperature (Tmin), diurnal temperature range (DTR), precipitation (PT), relative humidity (RH), and carbon emissions (CO2). The pre-analysis, post-analysis, and model robustness statistical tests were verified, and all conditions were met. A significant upward trend in Tmax (0.13 °C/decade), Tmin (0.27 °C/decade), and CO2 (5.1 units/decade), and a downward trend in DTR ( - 0.15 °C/decade) were noted. The PT and RH insignificantly increased by 4.32 mm and 0.11% per decade, respectively. The ARDL model explained 76.6% of the total variations in maize yield. Notably, the maize yield had a negative correlation with Tmax for June, and July, with PT in August, and with DTR for June, July, and August, whereas a positive correlation was noted with Tmin in June, July, and August. Overall, a unit change in Tmax reduced the maize yield by 7.39% and 26.33%, and a unit change in PT reduced it by 0.65% and 2.69% in the short and long run, respectively. However, a unit change in Tmin, and CO2 emissions increased maize yield by 20.68% and 0.63% in the long run with no short run effect. Overall, it is imperative to reassess the agronomic management strategies, developing and testing cultivars adaptable to the revealed climatic trend, with ability to withstand severe weather conditions in ensuring sustainable maize production.

Climate trends and soybean production since 1970 in Mississippi: Empirical evidence from ARDL model.

Studying historical response of crops to weather conditions at a finer scale is essential for devising agricultural strategies tailored to expected climate changes. However, determining the relationship between crop and climate in Mississippi (MS) remains elusive. Therefore, this research attempted to i) estimate climate trends between 1970 and 2020 in MS during the soybean growing season (SGS) using the Mann-Kendall and Sen slope method, ii) calculate the impact of climate change on soybean yield using an auto-regressive distributive lag (ARDL) econometric model, and iii) identify the most critical months from a crop-climate perspective by generating a correlation between the detrended yield and the monthly average for each climatic variable. Specific variables considered were maximum temperature (Tmax), minimum temperature (Tmin), diurnal temperature range (DTR), precipitation (PT), carbon dioxide emissions (CO2), and relative humidity (RH). All required diagnostic-tests i.e., pre-analysis, post-analysis, model-sensitivity, and assessing the models' goodness-of-fit were performed and statistical standards were met. A positive trend in Tmin (+0.25 °C/decade), and a negative trend in DTR (-0.18 °C/decade) was found. Although Tmax, PT, and RH showed non-significant trends, numerical changes were noted as +0.11 °C/decade, +3.03 mm/decade, and -0.06 %/decade, respectively. Furthermore, soybean yield was positively correlated with Tmin (in June and September), PT (in July and August), and RH (in July), but negatively correlated with Tmax (in July and August) and DTR (in June, July, and August). Soybean yield was observed to be significantly reduced by 18.11 % over the long-term and by 5.51 % over the short-term for every 1 °C increase in Tmax. With every unit increase in Tmin and CO2 emissions, the yield of soybeans increased significantly by 7.76 % and 3.04 %, respectively. Altogether, soybeans in MS exhibited variable sensitivity to short- and long-terms climatic changes. The results highlight the importance of testing climate-resilient agronomic practices and cultivars that encompass asymmetric sensitivities in response to climatic conditions of MS.

Effect of continuous sugarcane bagasse-derived biochar application on rainfed cotton (Gossypium hirsutum L.) growth, yield and lint quality in the humid Mississippi delta.

Optimizing soil health through soil amendments is a promising strategy for enhancing rainwater efficiency for stabilizing crop production. Biochar, obtained by torrefaction of sugarcane bagasse, a byproduct from sugar mills, has a high potential for its use as a soil amendment, which can boost crop yields, but needs further field trials for its adoption in farming systems. A field study was conducted during 2019-2021 at Stoneville, Mississippi, to assess rainfed cotton (Gossypium hirsutum L.) production under four biochar levels (0, 10, 20, and 40 t ha-1) on Dundee silt loam soil. The effects of biochar on cotton growth and lint yield and quality were examined. Biochar levels had no significant impact on cotton lint and seed yield for the first two years. Still, in the third year, a significant increase in lint yield by 13 and 21.7% was recorded at 20 and 40 t ha-1 biochar levels, respectively. In the third year, lint yields were 1523, 1586, 1721, and 1854 kg ha-1 at 0, 10, 20 and 40 t ha-1 biochar levels, respectively. Similarly, cotton seed yield increased by 10.8% and 13.4% in 20 and 40 t ha-1 biochar plots. This study demonstrated that successive biochar applications at 20 or 40 t ha-1 can enhance cotton lint and seed yields under rainfed conditions. These improved yields with biochar did not produce increased net returns due to the increased production costs. Many lint quality parameters were unaffected except for micronaire, fiber strength and fiber length. However, potential long-term benefits of enhanced cotton production from biochar application beyond the length of the study merit further investigation. Additionally, biochar application is more relevant when accrued carbon credits through carbon sequestration outweigh the increased production costs due to biochar application.

Eddy covariance assessment of alternate wetting and drying floodwater management on rice methane emissions.

Reducing methane emissions and water use is critical for combating climate change and declining aquifers on food production. Reductions in irrigation water use and methane emissions are known benefits of practicing alternate wetting and drying (AWD) over continuous flooding (CF) water management in lowland rice (Oryza sativa L.) production systems. In a two-year (2020 and 2021) study, methane emissions from large farm-scale (∼50 ha) rice fields managed under CF and AWD in soils dominated by Sharkey clay (Sharkey clay, clay over loamy, montmorillonitic non-acid, thermic Vertic halauepet) were monitored using the eddy covariance method (EC). In the EC system, an open-path laser gas analyzer was used to monitor air methane gas density in the constant flux layer of the atmosphere over the rice-crop canopies. Total water pumped into the field for floodwater management was higher in CF compared to AWD by 24 and 14% in 2020 and 2021, respectively. Considerable variations between seasons in the amount of methane emitted from the CF and AWD treatments were observed: CF emitted 29 and 75 kg ha-1 and AWD emitted 14 and 34 kg ha-1 methane in 2020 and 2021, respectively. Notwithstanding, the extent of reduction in methane emissions due to AWD over CF was similar for each crop season (52% in 2020 and 55% in 2021). Rice grain yield harvested differed by only ±2% between AWD and CF. This investigation of large-scale system-level evaluation, using the EC method, confirmed that by practicing AWD floodwater management in rice, water pumped from aquifers could be reduced by about a quarter and methane emissions from rice fields could be cut down by about half without affecting grain yields, thereby promoting sustainable water management and greenhouse gas emission reduction during rice production in the Lower Mississippi Delta.

Mixed-Species Cover Crop Biomass Estimation Using Planet Imagery.

Cover crop biomass is helpful for weed and pest control, soil erosion control, nutrient recycling, and overall soil health and crop productivity improvement. These benefits may vary based on cover crop species and their biomass. There is growing interest in the agricultural sector of using remotely sensed imagery to estimate cover crop biomass. Four small plot study sites located at the United States Department of Agriculture Agricultural Research Service, Crop Production Systems Research Unit farm, Stoneville, MS with different cereals, legumes, and their mixture as fall-seeded cover crops were selected for this analysis. A randomized complete block design with four replications was used at all four study sites. Cover crop biomass and canopy-level hyperspectral data were collected at the end of April, just before cover crop termination. High-resolution (3 m) PlanetScope imagery (Dove satellite constellation with PS2.SD and PSB.SD sensors) was collected throughout the cover crop season from November to April in the 2021 and 2022 study cycles. Results showed that mixed cover crop increased biomass production up to 24% higher compared to single species rye. Reflectance bands (blue, green, red and near infrared) and vegetation indices derived from imagery collected during March were more strongly correlated with biomass (r = 0-0.74) compared to imagery from November (r = 0.01-0.41) and April (r = 0.03-0.57), suggesting that the timing of imagery acquisition is important for biomass estimation. The highest correlation was observed with the near-infrared band (r = 0.74) during March. The R2 for biomass prediction with the random forest model improved from 0.25 to 0.61 when cover crop species/mix information was added along with Planet imagery bands and vegetation indices as biomass predictors. More study with multiple timepoint biomass, hyperspectral, and imagery collection is needed to choose appropriate bands and estimate the biomass of mix cover crop species.

Resilience of soybean cultivars to drought stress during flowering and early-seed setting stages.

Drought stress during the reproductive stage and declining soybean yield potential raise concerns about yield loss and economic return. In this study, ten cultivars were characterized for 20 traits to identify reproductive stage (R1-R6) drought-tolerant soybean. Drought stress resulted in a marked reduction (17%) in pollen germination. The reduced stomatal conductance coupled with high canopy temperature resulted in reduced seed number (45%) and seed weight (35%). Drought stress followed by rehydration increased the hundred seed weight at the compensation of seed number. Further, soybean oil decreased, protein increased, and cultivars responded differently under drought compared to control. In general, cultivars with high tolerance scores for yield displayed lower tolerance scores for quality content and vice versa. Among ten cultivars, LS5009XS and G4620RX showed maximum stress tolerance scores for seed number and seed weight. The observed variability in leaf reflectance properties and their relationship with physiological or yield components suggested that leaf-level sensing information can be used for differentiating drought-sensitive soybean cultivars from tolerant ones. The study led to the identification of drought-resilient cultivars/promising traits which can be exploited in breeding to develop multi-stress tolerant cultivars.

Impact of recent climate change on corn, rice, and wheat in southeastern USA.

Climate change and its impact on agriculture productivity vary among crops and regions. The southeastern United States (SE-US) is agro-ecologically diversified, economically dependent on agriculture, and mostly overlooked by agroclimatic researchers. The objective of this study was to compute the effect of climatic variables; daily maximum temperature (Tmax), daily minimum temperature (Tmin), and rainfall on the yield of major cereal crops i.e., corn (Zea mays L.), rice (Oryza sativa L.), and wheat (Triticum aestivum L.) in SE-US. A fixed-effect model (panel data approach) was used by applying the production function on panel data from 1980 to 2020 from 11 SE-US states. An asymmetrical warming pattern was observed, where nocturnal warming was 105.90%, 106.30%, and 32.14%, higher than the diurnal warming during corn, rice, and wheat growing seasons, respectively. Additionally, a shift in rainfall was noticed ranging from 19.2 to 37.2 mm over different growing seasons. Rainfall significantly reduced wheat yield, while, it had no effect on corn and rice yields. The Tmax and Tmin had no significant effect on wheat yield. A 1 °C rise in Tmax significantly decreased corn (- 34%) and rice (- 8.30%) yield which was offset by a 1 °C increase in Tmin increasing corn (47%) and rice (22.40%) yield. Conclusively, overall temperature change of 1 °C in the SE-US significantly improved corn yield by 13%, rice yield by 14.10%, and had no effect on wheat yield.

Photosynthetic Response of Soybean and Cotton to Different Irrigation Regimes and Planting Geometries.

Soybean [Glycine max (L.) Merr.] and cotton (Gossypium hirsutum L.) are the major row crops in the USA, and growers are tending toward the twin-row system and irrigation to increase productivity. In a 2-year study (2018 and 2019), we examined the gas exchange and chlorophyll fluorescence parameters to better understand the regulatory and adaptive mechanisms of the photosynthetic components of cotton and soybean grown under varying levels of irrigations and planting geometries in a split-plot experiment. The main plots were three irrigation regimes: (i) all furrows irrigation (AFI), (ii) alternate or skipped furrow irrigation (SFI), and iii) no irrigation or rainfed (RF), and the subplots were two planting patterns, single-row (SR) and twin-row (TR). The light response curves at vegetative and reproductive phases revealed lower photosynthesis rates in the RF crops than in AFI and SFI. A higher decrease was noticed in RF soybean for light compensation point (LCP) and light saturation point (LSP) than that of RF cotton. The decrease in the maximum assimilation rate (Amax) was higher in soybean than cotton. A decrease of 12 and 17% in Amax was observed in RF soybean while the decrease is limited to 9 and 6% in RF cotton during the 2018 and 2019 seasons, respectively. Both stomatal conductance (gs) and transpiration (E) declined under RF. The moisture deficit stress resulted in enhanced operating quantum efficiency of PSII photochemistry (ΦPSII), which is probably due to increased photorespiration. The non-photochemical quenching (NPQ), a measure of thermal dissipation of absorbed light energy, and quantum efficiency of dissipation by down-regulation (ΦNPQ) increased significantly in both crops up to 50% under RF conditions. The photochemical quenching declined by 28% in soybean and 26% in cotton. It appears soybean preferentially uses non-photochemical energy dissipation while cotton uses elevated electron transport rate (ETR) under RF conditions for light energy utilization. No significant differences among SR and TR systems were observed for LCP, LSP, AQE, Amax, gs, E, ETR, and various chlorophyll fluorescence parameters. This study reveals preferential use of non-photochemical energy dissipation in soybean while cotton uses both photochemical and non-photochemical energy dissipation to protect PSI and PSII centers and ETR, although they fall under C3 species when exposed to moisture limited environments.

Hyperspectral plant sensing for differentiating glyphosate-resistant and glyphosate-susceptible johnsongrass through machine learning algorithms.

BACKGROUND: Johnsongrass (Sorghum halepense) is one of the weeds that evolves resistance to glyphosate [N-(phosphonomethyl)-glycine], the most widely used herbicide, and the weed may cause agronomic troublesome in the southern USA. This paper reports a study on developing a hyperspectral plant sensing approach to explore the spectral features of glyphosate-resistant (GR) and glyphosate-sensitive (GS) plants to evaluate this approach using machine learning algorithms to differentiate between GR and GS plants. RESULTS: On average, GR plants have higher spectral reflectance compared with GS plants. The sensitive spectral bands were optimally selected using the successive projections algorithm respectively wrapped with the machine learning algorithms of k-nearest neighbors (KNN), random forest (RF), and support vector machine (SVM) with Fisher linear discriminant analysis (FLDA) to classify between GS and GS plants. At 3 weeks after transplanting (WAT) KNN and SVM could not acceptably classify the GR and GS plants but they improved significantly with the stages to have their overall accuracies reaching 73% and 77%, respectively, at 5 WAT. RF and FLDA had a better ability to classify the plants at 3 WAT but RF was low in accuracy at 2 WAT while FLDA dropped accuracy to 50% at 4 WAT from 57% at 3 WAT and raised it to 73% at 5 WAT. CONCLUSIONS: Previous studies were conducted developing the hyperspectral imaging approach to differentiate GR Palmer amaranth from GS Palmer amaranth and GR Italian ryegrass from GS Italian ryegrass with classification accuracies of 90% and 80%, respectively. This study demonstrated that the hyperspectral plant sensing approach could be developed to differentiate GR johnsongrass from glyphosate-sensitive GS johnsongrass with the highest classification accuracy of 77%. The comparison with our previous studies indicated that the similar hyperspectral approach could be used and transferred from classification across different GR and GS weed biotypes, such as Palmer amaranth, Italian ryegrass and johnsongrass, so it is highly possible for classification of more other GR and GS weed biotypes as well. On the basis of classic pattern recognition approaches the process of plant classification can be enhanced by modeling using machine learning algorithms. © 2022 Society of Chemical Industry. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Soil Microbial Communities in Diverse Agroecosystems Exposed to the Herbicide Glyphosate.

Despite glyphosate's wide use for weed control in agriculture, questions remain about the herbicide's effect on soil microbial communities. The existing scientific literature contains conflicting results, from no observable effect of glyphosate to the enrichment of agricultural pathogens such as Fusarium spp. We conducted a comprehensive field-based study to compare the microbial communities on the roots of plants that received a foliar application of glyphosate to adjacent plants that did not. The 2-year study was conducted in Beltsville, MD, and Stoneville, MS, with corn and soybean crops grown in a variety of organic and conventional farming systems. By sequencing environmental metabarcode amplicons, the prokaryotic and fungal communities were described, along with chemical and physical properties of the soil. Sections of corn and soybean roots were plated to screen for the presence of plant pathogens. Geography, farming system, and season were significant factors determining the composition of fungal and prokaryotic communities. Plots treated with glyphosate did not differ from untreated plots in overall microbial community composition after controlling for other factors. We did not detect an effect of glyphosate treatment on the relative abundance of organisms such as Fusarium spp.IMPORTANCE Increasing the efficiency of food production systems while reducing negative environmental effects remains a key societal challenge to successfully meet the needs of a growing global population. The herbicide glyphosate has become a nearly ubiquitous component of agricultural production across the globe, enabling an increasing adoption of no-till agriculture. Despite this widespread use, there remains considerable debate on the consequences of glyphosate exposure. In this paper, we examine the effect of glyphosate on soil microbial communities associated with the roots of glyphosate-resistant crops. Using metabarcoding techniques, we evaluated prokaryotic and fungal communities from agricultural soil samples (n = 768). No effects of glyphosate were found on soil microbial communities associated with glyphosate-resistant corn and soybean varieties across diverse farming systems.

Assessing crop damage from dicamba on non-dicamba-tolerant soybean by hyperspectral imaging through machine learning.

BACKGROUND: Dicamba effectively controls several broadleaf weeds. The off-target drift of dicamba spray or vapor drift can cause severe injury to susceptible crops, including non-dicamba-tolerant crops. In a field experiment, advanced hyperspectral imaging (HSI) was used to study the spectral response of soybean plants to different dicamba rates, and appropriate spectral features and models for assessing the crop damage from dicamba were developed. RESULTS: In an experiment with six different dicamba rates, an ordinal spectral variation pattern was observed at both 1 week after treatment (WAT) and 3 WAT. The soybean receiving a dicamba rate ≥0.2X exhibited unrecoverable damage. Two recoverability spectral indices (HDRI and HDNI) were developed based on three optimal wavebands. Based on the Jeffries-Matusita distance metric, Spearman correlation analysis and independent t-test for sensitivity to dicamba spray rates, a number of wavebands and classic spectral features were extracted. The models for quantifying dicamba spray levels were established using the machine learning algorithms of naive Bayes, random forest and support vector machine. CONCLUSIONS: The spectral response of soybean injury caused by dicamba sprays can be clearly captured by HSI. The recoverability spectral indices developed were able to accurately differentiate the recoverable and unrecoverable damage, with an overall accuracy (OA) higher than 90%. The optimal spectral feature sets were identified for characterizing dicamba spray rates under recoverable and unrecoverable situations. The spectral features plus plant height can yield relatively high accuracy under the recoverable situation (OA = 94%). These results can be of practical importance in weed management. © 2019 Society of Chemical Industry.

Quantifying water and CO2 fluxes and water use efficiencies across irrigated C3 and C4 crops in a humid climate.

Underground aquifers that took millions of years to fill are being depleted due to unsustainable water withdrawals for crop irrigation. Concurrently, atmospheric warming due to anthropogenic greenhouse gases is enhancing demands for water inputs in agriculture. Accurate information on crop-ecosystem water use efficiencies [EWUE, amount of CO2 removed from the soil-crop-air system per unit of water used in evapotranspiration (ET)] is essential for developing environmentally and economically sustainable water management practices that also help account for CO2, the most abundant of the greenhouse gases, exchange rates from cropping systems. We quantified EWUE of corn (a C4 crop) and soybean and cotton (C3 crops) in a predominantly clay soil under humid climate in the Lower Mississippi (MS) Delta, USA. Crop-ecosystem level exchanges of CO2 and water from these three cropping systems were measured in 2017 using the eddy covariance method. Ancillary micrometeorological data were also collected. On a seasonal basis, all three crops were net sinks for CO2 in the atmosphere: corn, soybean, and cotton fixed -31,331, -23,563, and -8856 kg ha-1 of CO2 in exchange for 483, 552, and 367 mm of ET, respectively (negative values show that CO2 is fixed in the plant or removed from the air). The seasonal NEE estimated for cotton was 72% less than corn and 62% less than soybean. Half-hourly averaged maximum net ecosystem exchange (NEE) from these cropping systems were -33.6, -27.2, and -14.2 kg CO2 ha-1, respectively. Average daily NEE were -258, -169, and -65 kg CO2 ha-1, respectively. The EWUE in these three cropping systems were 53, 43, and 24 kg CO2 ha-1 mm-1 of water. Results of this investigation can help in adopting crop mixtures that are environmentally and economically sustainable, conserving limited water resources in the region.

Glyphosate Resistance Technology Has Minimal or No Effect on Maize Mineral Content and Yield.

Controversy continues to exist regarding whether the transgene for glyphosate resistance (GR) and/or glyphosate applied to GR crops adversely affect plant mineral content. Field studies were conducted in 2013 and 2014 in Stoneville, MS and Urbana, IL to examine this issue in maize. At each location, the experiment was conducted in fields with no history of glyphosate application and fields with several years of glyphosate use preceding the study. Neither glyphosate nor the GR transgene affected yield or mineral content of leaves or seed, except for occasional (<5%) significant effects that were inconsistent across minerals, treatments, and environments. Glyphosate and AMPA (aminomethylphosphonic acid), a main degradation product of glyphosate, were found in leaves from treated plants, but little or no glyphosate and no AMPA was found in maize seeds. These results show that the GR transgene and glyphosate application, whether used for a single year or several years, have no deleterious effect on mineral nutrition or yield of GR maize.

Lack of transgene and glyphosate effects on yield, and mineral and amino acid content of glyphosate-resistant soybean.

BACKGROUND: There has been controversy as to whether the glyphosate resistance gene and/or glyphosate applied to glyphosate-resistant (GR) soybean affect the content of cationic minerals (especially Mg, Mn and Fe), yield and amino acid content of GR soybean. A two-year field study (2013 and 2014) examined these questions at sites in Mississippi, USA. RESULTS: There were no effects of glyphosate, the GR transgene or field crop history (for a field with both no history of glyphosate use versus one with a long history of glyphosate use) on grain yield. Furthermore, these factors had no consistent effects on measured mineral (Al, As, Ba, Cd, Ca, Co, Cr, Cs, Cu, Fe, Ga, K, Li, Mg, Mn, Ni, Pb, Rb, Se, Sr, Tl, U, V, Zn) content of leaves or harvested seed. Effects on minerals were small and inconsistent between years, treatments and mineral, and appeared to be random false positives. No notable effects on free or protein amino acids of the seed were measured, although glyphosate and its degradation product, aminomethylphosphonic acid (AMPA), were found in the seed in concentrations consistent with previous studies. CONCLUSIONS: Neither glyphosate nor the GR transgene affect the content of the minerals measured in leaves and seed, harvested seed amino acid composition, or yield of GR soybean. Furthermore, soils with a legacy of GR crops have no effects on these parameters in soybean. © 2017 Society of Chemical Industry.

Growing season variability in carbon dioxide exchange of irrigated and rainfed soybean in the southern United States.

Measurement of carbon dynamics of soybean (Glycine max L.) ecosystems outside Corn Belt of the United States (U.S.) is lacking. This study examines the seasonal variability of net ecosystem CO2 exchange (NEE) and its components (gross primary production, GPP and ecosystem respiration, ER), and relevant controlling environmental factors between rainfed (El Reno, Oklahoma) and irrigated (Stoneville, Mississippi) soybean fields in the southern U.S. during the 2016 growing season. Grain yield was about 1.6tha-1 for rainfed soybean and 4.9tha-1 for irrigated soybean. The magnitudes of diurnal NEE (~2-weeks average) reached seasonal peak values of -23.18 and -34.78µmolm-2s-1 in rainfed and irrigated soybean, respectively, approximately two months after planting (i.e., during peak growth). Similar thresholds of air temperature (Ta, slightly over 30°C) and vapor pressure deficit (VPD, ~2.5kPa) for NEE were observed at both sites. Daily (7-day average) NEE, GPP, and ER reached seasonal peak values of -4.55, 13.54, and 9.95gCm-2d-1 in rainfed soybean and -7.48, 18.13, and 14.93gCm-2d-1 in irrigated soybean, respectively. The growing season (DOY 132-243) NEE, GPP, and ER totals were -54, 783, and 729gCm-2, respectively, in rainfed soybean. Similarly, cumulative NEE, GPP, and ER totals for DOY 163-256 (flux measurement was initiated on DOY 163, missing first 45days after planting) were -291, 1239, and 948gCm-2, respectively, in irrigated soybean. Rainfed soybean was a net carbon sink for only two months, while irrigated soybean appeared to be a net carbon sink for about three months. However, grain yield and the magnitudes and seasonal sums of CO2 fluxes for irrigated soybean in this study were comparable to those for soybean in the U.S. Corn Belt, but they were lower for rainfed soybean.

Impact of glyphosate-resistant corn, glyphosate applications and tillage on soil nutrient ratios, exoenzyme activities and nutrient acquisition ratios.

BACKGROUND: We report results of the last two years of a 7 year field experiment designed to test the null hypothesis: applications of glyphosate on glyphosate-resistant (GR) and non-resistant (non-GR) corn (Zea mays L.) under conventional tillage and no-till would have no effect on soil exoenzymes and microbial activity. RESULTS: Bulk soil (BS) and rhizosphere soil (RS) macronutrient ratios were not affected by either GR or non-GR corn, or glyphosate applications. Differences observed between exoenzyme activities were associated with tillage rather than glyphosate applications. In 2013, nutrient acquisition ratios for bulk and rhizosphere soils indicated P limitations, but sufficient assimilable N. In 2014, P limitations were observed for bulk and rhizosphere soils, in contrast to balanced C and N acquisition ratios in rhizosphere soils. Stoichiometric relationships indicated few differences between glyphosate and non-glyphosate treatments. Negative correlations between C:P and N:P nutrient ratios and nutrient acquisition ratios underscored the inverse relation between soil nutrient status and microbial community exoenzyme activities. CONCLUSIONS: Inconsistent relationships between microbial community metabolic activity and exoenzyme activity indicated an ephemeral effect of glyphosate on BS exoenzyme activity. Except for ephemeral effects, glyphosate applications appeared not to affect the function of the BS and RS exoenzymes under conventional tillage or no-till. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Effects of Row-Type, Row-Spacing, Seeding Rate, Soil-Type, and Cultivar Differences on Soybean Seed Nutrition under US Mississippi Delta Conditions.

The new Early Soybean Production System (ESPS), developed in the Midsouth USA, including the Mississippi delta, resulted in higher yield under irrigated and non-irrigated conditions. However, information on the effects of the agricultural practices such as row-type (RT: twin- vs. single-row), row-spacing, (RS), seeding rate (SR), soil-type (ST) on seed nutrition under the ESPS environment in the Mississippi delta is very limited. Our previous research in the Mississippi delta showed these agricultural practices altered seed nutrients in one cultivar only. However, whether these effects on seed nutrients will be exhibited by other soybean cultivars with earlier and later maturities across multiple years are not yet known. Therefore, the objective of this research was to evaluate the effects of agricultural practices and cultivar (Cv) differences on seed nutrition in clay and sandy soils under ESPS environment of high heat and drought. Two field experiments were conducted; one experiment was conducted in 2009 and 2010, and the other in 2008, 2009, and 2010 under irrigated conditions. Soybean were grown on 102 cm single-rows and on 25 cm twin-rows with 102 cm centers at seeding rates of 20, 30, 40, and 50 seeds m(-2). Two soybean cultivars (94M80 with earlier maturity; and GP 533 with later maturity) were used. Results showed that increasing seeding rate resulted in increases of protein, sucrose, glucose, raffinose, B, and P concentrations on both single- and twin-rows. However, this increase became either constant or declined at the higher rates (40 and 50 seeds m(-2)). Protein and linolenic acid concentrations were higher in GP 533 than in 94M80 on both row-types, but oil and oleic acid concentrations were in 94M80 than GP 533. Generally, cultivar GP 533 accumulated more seed constituents in seeds than 94M80. In 2010, there were no clear responses of seed nutrients to SR increase in both cultivars, perhaps due to drier year and high heat in 2010. It is concluded that RT and SR can alter seed nutrition under clay and sandy soils, especially under high heat and drought conditions as in the Mississippi delta.

Agricultural practices altered soybean seed protein, oil, fatty acids, sugars, and minerals in the Midsouth USA.

Information on the effects of management practices on soybean seed composition is scarce. Therefore, the objective of this research was to investigate the effects of planting date (PD) and seeding rate (SR) on seed composition (protein, oil, fatty acids, and sugars) and seed minerals (B, P, and Fe) in soybean grown in two row-types (RTs) on the Mississippi Delta region of the Midsouth USA. Two field experiments were conducted in 2009 and 2010 on Sharkey clay and Beulah fine sandy loam soil at Stoneville, MS, USA, under irrigated conditions. Soybean were grown in 102 cm single-rows and 25 cm twin-rows in 102 cm centers at SRs of 20, 30, 40, and 50 seeds m(-2). The results showed that in May and June planting, protein, glucose, P, and B concentrations increased with increased SR, but at the highest SRs (40 and 50 seeds m(-2)), the concentrations remained constant or declined. Palmitic, stearic, and linoleic acid concentrations were the least responsive to SR increases. Early planting resulted in higher oil, oleic acid, sucrose, B, and P on both single and twin-rows. Late planting resulted in higher protein and linolenic acid, but lower oleic acid and oil concentrations. The changes in seed constituents could be due to changes in environmental factors (drought and temperature), and nutrient accumulation in seeds and leaves. The increase of stachyose sugar in 2010 may be due to a drier year and high temperature in 2010 compared to 2009; suggesting the possible role of stachyose as an environmental stress compound. Our research demonstrated that PD, SR, and RT altered some seed constituents, but the level of alteration in each year dependent on environmental factors such as drought and temperature. This information benefits growers and breeders for considering agronomic practices to select for soybean seed nutritional qualities under drought and high heat conditions.