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.

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.

Tillage, Fungicide, and Cultivar Effects on Frogeye Leaf Spot Severity and Yield in Soybean.

Frogeye leaf spot (FLS) of soybean, caused by Cercospora sojina, has been a problem in the southern United States for many years but has become an increasing problem in the northern United States more recently, causing significant yield losses. This increase in disease severity in the northern United States has been attributed to increased utilization of no-till planting and changes in climate. A field study was conducted at the University of Tennessee, Research and Education Center in Milan, TN from 2007 to 2010 to determine severity in tilled and no-till plots treated with or without fungicide at R3 and R5 growth stages. Three FLS-susceptible cultivars, one each in Maturity Groups III, IV, and V, were treated with pyraclostrobin (Headline) fungicide. Analysis of variance using the area under the disease progress curve (AUDPC) indicated no significant difference (P ≤ 0.05) in disease severity between tilled and no-till plots without fungicide. Fungicide did not significantly reduce disease under no-till, but did under tilled plots. This is the first study showing that no-till plots did not reduce or enhance the severity of FLS when no fungicide was applied. Fungicide application significantly reduced (P ≤ 0.05) disease severity and AUDPC and increased yield in tilled plots. The yield gains in tilled, fungicide-treated plots ranged from 1 to 17%. When fungicide was applied, disease severity was not reduced as significantly in no-till as in treated tilled plots, suggesting that fungicide programs under a no-till system may require further study to minimize the risk of FLS severity.

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.

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 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.

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.

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.

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.

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.

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.

Aminomethylphosphonic acid accumulation in plant species treated with glyphosate.

Aminomethylphosphonic acid (AMPA) is the most frequently detected metabolite of glyphosate in plants. The objective of this study was to determine if there is any correlation of metabolism of glyphosate to AMPA in different plant species and their natural level of resistance to glyphosate. Greenhouse studies were conducted to determine the glyphosate I 50 values (rate required to cause a 50% reduction in plant growth) and to quantify AMPA and shikimate concentrations in selected leguminous and nonleguminous species treated with glyphosate at respective I 50 rates. Coffee senna [ Cassia occidentalis (L.) Link] was the most sensitive ( I 50 = 75 g/ha) and hemp sesbania [ Sesbania herbacea (P.Mill.) McVaugh] was the most resistant ( I 50 = 456 g/ha) to glyphosate. Hemp sesbania was 6-fold and Illinois bundleflower [ Desmanthus illinoensis (Michx.) MacM. ex B.L.Robins. & Fern.] was 4-fold more resistant to glyphosate than coffee senna. Glyphosate was present in all plant species, and its concentration ranged from 0.308 to 38.7 microg/g of tissue. AMPA was present in all leguminous species studied except hemp sesbania. AMPA concentration ranged from 0.119 to 4.77 microg/g of tissue. Shikimate was present in all plant species treated with glyphosate, and levels ranged from 0.053 to 16.5 mg/g of tissue. Non-glyphosate-resistant (non-GR) soybean accumulated much higher shikimate than glyphosate-resistant (GR) soybean. Although some leguminous species were found to be more resistant to glyphosate than others, and there was considerable variation between species in the glyphosate to AMPA levels found, metabolism of glyphosate to AMPA did not appear to be a common factor in explaining natural resistance levels.

Nitrogen metabolism and seed composition as influenced by glyphosate application in glyphosate-resistant soybean.

Previous research has demonstrated that glyphosate can affect nitrogen fixation or nitrogen assimilation in soybean. This 2-year field study investigated the effects of glyphosate application of 1.12 and 3.36 kg of ae ha(-1) on nitrogen metabolism and seed composition in glyphosate-resistant (GR) soybean. There was no effect of glyphosate application on nitrogen fixation as measured by acetylene reduction assay, soybean yield, or seed nitrogen content. However, there were significant effects of glyphosate application on nitrogen assimilation, as measured by in vivo nitrate reductase activity (NRA) in leaves, roots, and nodules, especially at high rate. Transiently lower leaf nitrogen or (15)N natural abundance in high glyphosate application soybean supports the inhibition of NRA. With the higher glyphosate application level protein was significantly higher (10.3%) in treated soybean compared to untreated soybean. Inversely, total oil and linolenic acid were lowest at the high glyphosate application rate, but oleic acid was greatest (22%) in treated soybean. These results suggest that nitrate assimilation in GR soybean was more affected than nitrogen fixation by glyphosate application and that glyphosate application may alter nitrogen and carbon metabolism.

Tillage management to mitigate herbicide loss in runoff under simulated rainfall conditions.

Conservation tillage mitigates soil loss in cropland because plant residues help protect the soil, but effects on pesticide movement in surface runoff are not as straightforward. Effects of soil disturbance on surface runoff loss of chlorimuron and alachlor were evaluated utilizing runoff trays. Soil in the trays was either disturbed (tilled) and kept bare or was not tilled, and existing decomposed plant residue was left on the surface. Rainfall (25mm, 20min) was simulated 1d after alachlor (2.8kg ha(-1)) or chlorimuron (54g ha(-1)) application, and runoff was collected. Runoff fractions were analyzed for herbicide and sediment. Total alachlor loss from bare plots was greater than that in no-tillage plots (4.5% vs. 2.3%, respectively). More than one-third of total alachlor lost from bare plots occurred in the first l of runoff, while no-tillage plots had less runoff volume with a more even distribution of alachlor concentration in the runoff during the rainfall simulation and subsequent runoff period. In contrast, more chlorimuron was lost from no-tillage plots than bare plots (12% vs. 1.5%) even though total runoff volume was lower in the no-tillage plots (10.6mm vs. 13.6mm). This was attributed to dense coverage with partially decomposed plant residue in no-tillage plots (1652kg ha(-1)) that intercepted chlorimuron. It was likely that chlorimuron, a polar compound, was more easily washed off surface plant residues and transported in runoff.

Integrating soil conservation practices and glyphosate-resistant crops: impacts on soil.

BACKGROUND: Conservation practices often associated with glyphosate-resistant crops, e.g. limited tillage and crop cover, improve soil conditions, but only limited research has evaluated their effects on soil in combination with glyphosate-resistant crops. It is assumed that conservation practices have similar benefits to soil whether or not glyphosate-resistant crops are used. This paper reviews the impact on soil of conservation practices and glyphosate-resistant crops, and presents data from a Mississippi field trial comparing glyphosate-resistant and non-glyphosate-resistant maize (Zea mays L.) and cotton (Gossypium hirsutum L.) under limited tillage management. RESULTS: Results from the reduced-tillage study indicate differences in soil biological and chemical properties owing to glyphosate-resistant crops. Under continuous glyphosate-resistant maize, soils maintained greater soil organic carbon and nitrogen as compared with continuous non-glyphosate-resistant maize, but no differences were measured in continuous cotton or in cotton rotated with maize. Soil microbial community structure based on total fatty acid methyl ester analysis indicated a significant effect of glyphosate-resistant crop following 5 years of continuous glyphosate-resistant crop as compared with the non-glyphosate-resistant crop system. Results from this study, as well as the literature review, indicate differences attributable to the interaction of conservation practices and glyphosate-resistant crop, but many are transient and benign for the soil ecosystem. CONCLUSIONS: Glyphosate use may result in minor effects on soil biological/chemical properties. However, enhanced organic carbon and plant residues in surface soils under conservation practices may buffer potential effects of glyphosate. Long-term field research established under various cropping systems and ecological regions is needed for critical assessment of glyphosate-resistant crop and conservation practice interactions.

Evidence for cross-adaptation between s-triazine herbicides resulting in reduced efficacy under field conditions.

BACKGROUND: Enhanced atrazine degradation has been observed in agricultural soils from around the globe. Soils exhibiting enhanced atrazine degradation may be cross-adapted with other s-triazine herbicides, thereby reducing their control of sensitive weed species. The aims of this study were (1) to determine the field persistence of simazine in atrazine-adapted and non-adapted soils, (2) to compare mineralization of ring-labeled (14)C-simazine and (14)C-atrazine between atrazine-adapted and non-adapted soils and (3) to evaluate prickly sida control with simazine in atrazine-adapted and non-adapted soils. RESULTS: Pooled over two pre-emergent (PRE) application dates, simazine field persistence was 1.4-fold lower in atrazine-adapted than in non-adapted soils. For both simazine and atrazine, the mineralization lag phase was 4.3-fold shorter and the mineralization rate constant was 3.5-fold higher in atrazine-adapted than in non-adapted soils. Collectively, the persistence and mineralization data confirm cross-adaptation between these s-triazine herbicides. In non-adapted soils, simazine PRE at the 15 March and 17 April planting dates reduced prickly sida density at least 5.4-fold compared with the no simazine PRE treatment. Conversely, in atrazine-adapted soils, prickly sida densities were not statistically different between simazine PRE and no simazine PRE at either planting date, thereby indicating reduced simazine efficacy in atrazine-adapted soils. CONCLUSIONS: Results demonstrate the potential for cross-adaptation among s-triazine herbicides and the subsequent reduction in the control of otherwise sensitive weed species.