The Integrated Agricultural Systems workgroup is examining agricultural systems of the US to determine fundamental principles that underlie successful production systems. Our hypothesis is that principles are applicab...The Integrated Agricultural Systems workgroup is examining agricultural systems of the US to determine fundamental principles that underlie successful production systems. Our hypothesis is that principles are applicable across regions, but key drivers interact to influence producer decisions and create distinct production systems. We interviewed agricultural producers to examine the underlying rationale for producer decisions and discern primary factors influencing production and marketing practices. While drivers are common among regions, interactions between drivers and influences on decision-makers vary substantially to create unique production systems. The internal social driver that values farming lifestyle is the principal factor that leads people to farming. The type of farming is partly a lifestyle choice and is influenced by other factors. Economic drivers and marketing options are primary drivers influencing production systems and management choices, as farmers provide an economic foundation for their families. While all producers employed strategies to manage production and marketing risks, these varied with different marketing channels. Identification of key drivers and principles can be used by producers, scientists and policy makers to direct agricultural production and agricultural research. New management systems can be developed that are flexible enough to respond to changing societal demands, and are environmentally and economically sustainable.展开更多
Background GOSSYM is a mechanistic,process-based cotton model that can simulate cotton crop growth and development,yield,and fiber quality.Its fiber quality module was developed based on controlled experiments explici...Background GOSSYM is a mechanistic,process-based cotton model that can simulate cotton crop growth and development,yield,and fiber quality.Its fiber quality module was developed based on controlled experiments explicitly conducted on the Texas Marker^(-1)(TM1)variety,potentially making its functional equations more aligned with this cultivar.To assess the model’s broader applicability,this study analyzed fiber quality data from 40 upland cotton cultivars,including TM1.The measured fiber quality from all cultivars was then compared with the modelsimulated fiber quality.Results Among the 40 upland cultivars,fiber strength varied from 28.4 cN·tex^(-1) to 34.6 cN·tex^(-1),fiber length ranged from 27.1 mm to 33.3 mm,micronaire value ranged from 2.7 to 4.6,and length uniformity index varied from 82.3%to 85.5%.The model simulated fiber quality closely matched the measured values for TM1,with the absolute percentage error(APE)being less than 0.92%for fiber strength,fiber length,and length uniformity index and 4.7%for micronaire.However,significant differences were observed for the other cultivars.The Pearson correlation coefficient(r)between the measured and simulated values was negative for all fiber quality traits,and Wilmotts’s index of agreement(WIA)was below 0.45,indicating a strong model bias toward TM1 without incorporating cultivar-specific parameters.After incorporating cultivar-specific parameters,the model’s performance improved significantly,with an average r-value of 0.84 and WIA of 0.88.Conclusions The adopted methodology and estimated cultivar-specific parameters improved the model’s simulation accuracy.This approach can be applied to newer cotton cultivars,enhancing the GOSSYM model’s utility and its applicability for agricultural management and policy decisions.展开更多
Big data with its vast volume and complexity is increasingly concerned, developed and used for all professions and trades. Remote sensing, as one of the sources for big data, is generating earth-observation data and a...Big data with its vast volume and complexity is increasingly concerned, developed and used for all professions and trades. Remote sensing, as one of the sources for big data, is generating earth-observation data and analysis results daily from the platforms of satellites, manned/unmanned aircrafts, and ground-based structures. Agricultural remote sensing is one of the backbone technologies for precision agriculture, which considers within-field variability for site-specific management instead of uniform management as in traditional agriculture. The key of agricultural remote sensing is, with global positioning data and geographic information, to produce spatially-varied data for subsequent precision agricultural operations. Agricultural remote sensing data, as general remote sensing data, have all characteristics of big data. The acquisition, processing, storage, analysis and visualization of agricultural remote sensing big data are critical to the success of precision agriculture. This paper overviews available remote sensing data resources, recent development of technologies for remote sensing big data management, and remote sensing data processing and management for precision agriculture. A five-layer-fifteen- level （FLFL） satellite remote sensing data management structure is described and adapted to create a more appropriate four-layer-twelve-level （FLTL） remote sensing data management structure for management and applications of agricultural remote sensing big data for precision agriculture where the sensors are typically on high-resolution satellites, manned aircrafts, unmanned aerial vehicles and ground-based structures. The FLTL structure is the management and application framework of agricultural remote sensing big data for precision agriculture and local farm studies, which outlooks the future coordination of remote sensing big data management and applications at local regional and farm scale.展开更多
This study investigated three different types of multispectral imaging systems for airborne remote sensing to support management in agricultural application and production.The three systems have been used in agricultu...This study investigated three different types of multispectral imaging systems for airborne remote sensing to support management in agricultural application and production.The three systems have been used in agricultural studies.They range from low-cost to relatively high-cost,manually operated to automated,multispectral composite imaging with a single camera and integrated imaging with custom-mounting of separate cameras.Practical issues regarding use of the imaging systems were described and discussed.The low-cost system,due to band saturation,slow imaging speed and poor image quality,is more preferable to slower moving platforms that can fly close to the ground,such as unmanned autonomous helicopters,but not recommended for low or high altitude aerial remote sensing on fixed-wing aircraft.With the restriction on payload unmanned autonomous helicopters are not recommended for high-cost systems because they are typically heavy and difficult to mount.The system with intermediate cost works well for low altitude aerial remote sensing on fixed-wing aircraft with field shapefile-based global positioning triggering.This system also works well for high altitude aerial remote sensing on fixed-wing aircraft with global positioning triggering or manually operated.The custom-built system is recommended for high altitude aerial remote sensing on fixed-wing aircraft with waypoint global positioning triggering or manually operated.展开更多
Precision agriculture accounts for within-field variability for targeted treatment rather than uniform treatment of an entire field.It is built on agricultural mechanization and state-of-the-art technologies of geogra...Precision agriculture accounts for within-field variability for targeted treatment rather than uniform treatment of an entire field.It is built on agricultural mechanization and state-of-the-art technologies of geographical information systems(GIS),global positioning systems(GPS)and remote sensing,and is used to monitor soil,crop growth,weed infestation,insects,diseases,and water status in farm fields to provide data and information to guide agricultural management practices.Precision agriculture began with mapping of crop fields at different scales to support agricultural planning and decision making.With the development of variable-rate technology,precision agriculture focuses more on tactical actions in controlling variable-rate seeding,fertilizer and pesticide application,and irrigation in real-time or within the crop season instead of mapping a field in one crop season to make decisions for the next crop season.With the development of aerial variable-rate systems,low-altitude airborne systems can provide high-resolution data for prescription variable-rate operations.Airborne systems for multispectral imaging using a number of imaging sensors(cameras)were developed.Unmanned aerial vehicles(UAVs)provide a unique platform for remote sensing of crop fields at slow speeds and low-altitudes,and they are efficient and more flexible than manned agricultural airplanes,which often cannot provide images at both low altitude and low speed for capture of high-quality images.UAVs are also more universal in their applicability than agricultural aircraft since the latter are used only in specific regions.This study presents the low-altitude remote sensing systems developed for detection of crop stress caused by multiple factors.UAVs,as a special platform,were discussed for crop sensing based on the researchers'studies.展开更多
Even though annual rainfall is high in the Delta region of Mississippi, only 30% occurs during the months in which the major crops are produced, making irrigation often necessary to meet crop water needs and to avoid ...Even though annual rainfall is high in the Delta region of Mississippi, only 30% occurs during the months in which the major crops are produced, making irrigation often necessary to meet crop water needs and to avoid risk of yield and profitability loss. Approximately, 65% of the farmland in this region is irrigated. The shallow Mississippi River Valley Alluvial Aquifer is the major source of water for irrigation and for aquaculture in the predominant catfish industry. This groundwater is being heavily used as row-crop irrigation has increased tremendously. Water level in this aquifer has declined significantly over the past twenty five years, with overdraft of approximately 370 million cubic meters of water per year. Moreover, the common irrigation practices in the Delta re-gion of Mississippi do not use water efficiently, further depleting the ground water and making ir-rigation more expensive to producers due to increasing energy prices. Irrigation experts in the re-gion have tested and verified various methods and tools that increase irrigation efficiency. This article presents a review of the current status of the irrigation practices in the Delta region of Mis-sissippi, and the improved methods and tools that are available to increase irrigation efficiency and to reduce energy costs for producers in the region as well as to stop the overdraft of the declining aquifer, ensuring its sustainable use.展开更多
Italian ryegrass is an annual/biennial grass that is typically used as a pasture crop or a cover crop along roadsides, rights-of-way, and industrial areas. Glyphosate-resistant (GR) Italian ryegrass populations have b...Italian ryegrass is an annual/biennial grass that is typically used as a pasture crop or a cover crop along roadsides, rights-of-way, and industrial areas. Glyphosate-resistant (GR) Italian ryegrass populations have been documented around the world, mostly in orchard and vineyard situations. The first evidence of evolved GR Italian ryegrass in row/agronomic crops was reported from Washington County, Mississippi in 2005. GR Italian ryegrass populations can jeopardize preplant burndown options in reduced-tillage crop production systems, thereby, delaying planting operations. The effects of competition of Italian ryegrass on crop growth and yield are poorly understood. A field study was conducted in the 2012 growing season and repeated in the 2013 growing season. GR and susceptible (GS) Italian ryegrass populations were established in the greenhouse and transplanted in prepared corn row beds in the fall of 2011 and 2012 at 0, 1, 2, 3, and 4 plants·meter> of crop row. Italian ryegrass plants overwintered and developed over the following spring-summer. Glyphosate was applied at 1.26 kg·ae/ha (1.5× of labeled rate) in the spring to burndown the Italian ryegrass plants and corn was planted into the ryegrass residue 2 - 3 wk later. Current corn production practices were followed. Corn density (early and late season), height (early season), and yield and Italian ryegrass biomass (early-mid season) measurements were recorded during both years. Corn height was greater in 2012 than that in 2013 at comparable stages of the growing season, due to a cooler and wetter early season in 2013 than that in 2012. Averaged across weed densities, corn density (both early and late season) and yield were higher in the GS than those in the GR population, but Italian ryegrass biomass was similar for both populations. Averaged across Italian ryegrass populations, corn density (both early and late season), and yield were inversely proportional to Italian ryegrass density. In summary, Italian ryegrass significantly reduced corn density and yield and reduction was greater with the GR than that with the GS population. Studies are underway to study inter population competition in Italian ryegrass and investigate allelopathic effects of Italian ryegrass on selected crops.展开更多
The wide distribution of Palmer amaranth (Amaranthus palmeri) in the southern US became a serious weed control problem prior to the extensive use of glyphosate-resistant crops. Currently glyphosate-resistant populatio...The wide distribution of Palmer amaranth (Amaranthus palmeri) in the southern US became a serious weed control problem prior to the extensive use of glyphosate-resistant crops. Currently glyphosate-resistant populations of Palmer amaranth occur in many areas of this geographic region creating an even more serious threat to crop production. Investigations were undertaken using four biotypes (one glyphosate-sensitive, one resistant from Georgia and two of unknown tolerance from Mississippi) of Palmer amaranth to assess bioassay techniques for the rapid detection and level of resistance in populations of this weed. These plants were characterized with respect to chlorophyll, betalain, and protein levels and immunological responses to an antibody of 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) the target site of glyphosate. Only slight differences were found in four biotypes grown under greenhouse conditions regarding extractable soluble protein and chlorophyll content, but one biotype was found to be devoid of the red pigment, betalain. Measurement of early growth (seedling shoot elongation) of seedlings was a useful detection tool to determine glyphosate resistance. A leaf disc bioassay (using visual ratings and/or chlorophyll analysis) and an assay for shikimate accumulation were effective methods for determining herbicide resistance levels. The two unknown biotypes were found to be resistant to this herbicide. Some differences were found in the protein profiles of the biotypes, and western blots demonstrated a weak labeling of antibody in the glyphosate-sensitive biotype, whereas strong labeling occurred in the resistant plants. This latter point supports research by others, that increased copy number of the EPSPS gene (and increased EPSPS protein levels) is the resistance mechanism in this species. Results indicate the utility of certain bioassays for the determination of resistance and provide useful comparative information on the levels of inherent constituents among closely related plants.展开更多
Velvetleaf (Abutilon theophrasti Medic.) infestations negatively impact row crop production throughout the United States and Canada’s eastern provinces. To implement management strategies to control velvetleaf, manag...Velvetleaf (Abutilon theophrasti Medic.) infestations negatively impact row crop production throughout the United States and Canada’s eastern provinces. To implement management strategies to control velvetleaf, managers need tools for differentiating it from crop plants. 5 Band, 7 Band, 8 Band, and 16 Band multispectral datasets simulating LANDSAT 3 plus a blue band, LANDSAT 8, WorldView 2, and WorldView 3 spectral bands, respectively were tested as input into the random forest algorithm for velvetleaf soybean [Glycine max L. (Merr.)] discrimination. During two separate greenhouse experiments in 2014, leaf reflectance measurements were obtained at the vegetative growth stage of velvetleaf plants and two soybean varieties. The reflectance measurements were collected with a plant contact probe attached to a hyperspectral spectroradiometer. Leaf hyperspectral reflectance measurements were convolved to the four multispectral datasets with computer software. Overall, user’s, and producer’s accuracies and kappa coefficient were employed to determine classification accuracies. Using the multispectral datasets as input, the random forest algorithm differentiated velvetleaf from the soybean varieties with accuracies ranging from 86.7% to 100%. 7 Band, 16 Band, 8 Band, and 5 Band datasets ranked or tied for the highest accuracies seventeen, sixteen, twelve, and one time, respectively. Kappa coefficients indicated an almost perfect agreement (i.e., kappa value, 0.81 - 1.0) to substantial agreement (i.e., kappa value, 0.61 - 0.80) between reference data and model predicted classes. This study was the first to demonstrate the application of the random forest machine learner and leaf multispectral reflectance data as tools to distinguish velvetleaf from soybean and to identify multispectral band combinations providing the best accuracies. Findings support further application of the random forest machine learner along with remotely-sensed multispectral data as tools for velvetleaf soybean discrimination with future implications for site-specific management of velvetleaf.展开更多
Previously we found that a strain of Myrothecium verrucaria (MV) exhibited bioherbicidal activity against several important weeds, and that some commercial formulations of glyphosate applied with MV resulted in synerg...Previously we found that a strain of Myrothecium verrucaria (MV) exhibited bioherbicidal activity against several important weeds, and that some commercial formulations of glyphosate applied with MV resulted in synergistic interactions that improved weed control efficacy. We also found that MV had bioherbicidal activity against glyphosate-resistant Palmer amaranth. We have also reported that some commercial formulations are inhibitory to MV. Our objectives were to test the effect of unformulated glyphosate (high purity, technical-grade glyphosate) alone and in combination with MV for bioherbicidal activity on glyphosate-susceptible and -resistant Palmer amaranth biotypes under greenhouse conditions and to examine technical-grade glyphosate on the growth of this bioherbicide. High purity glyphosate (without adjuvants/surfactants) was not toxic to MV growth and sporulation at concentrations up to 2.0 mM when grown on agar supplemented with the herbicide. Both biotypes were injured by MV and MV plus glyphosate treatments as early as 19 h after application (3 h after a dew period of 16 h). These injury effects increased and were more evident through the 6-day time course, when after 120 h the MV plus glyphosate treatment had killed all glyphosate-susceptible and -resistant plants. The interaction of glyphosate plus MV was synergistic toward the control of Palmer amaranth. Data strongly suggest that the active ingredient is responsible for the synergy previously found when this bioherbicide was combined with some commercial formulations of glyphosate. Results demonstrated that MV can control both glyphosate-resistant and -susceptible Palmer amaranth seedlings and act synergistically with high-purity glyphosate to provide improved weed control.展开更多
Evapotranspiration is an important component in water-balance and irrigation scheduling models. While the FAO-56 Penman-Monteith method has become the de facto standard for estimating reference evapotranspiration (ETo...Evapotranspiration is an important component in water-balance and irrigation scheduling models. While the FAO-56 Penman-Monteith method has become the de facto standard for estimating reference evapotranspiration (ETo), it is a complex method requiring several weather parameters. Required weather data are oftentimes unavailable, and alternative methods must be used. Three alternative ETo methods, the FAO-56 Reduced Set, Hargreaves, and Turc methods, were evaluated for use in Mississippi, a humid region of the USA, using only measurements of air temperature. The Turc equation, developed for use with measured temperature and solar radiation, was tested with estimated radiation and found to provide better estimates of FAO-56 ETo than the other methods. Mean bias errors of 0.75, 0.28, and -0.19 mm, mean absolute errors of 0.92, 0.68, and 0.62 mm, and percent errors of 22.5%, 8.5%, and -5.7% were found for daily estimates for the FAO-56 Reduced Set, Hargreaves, and Turc methods, respectively.展开更多
The growth of clones of seven Amaranthus palmeri x A. spinosus hybrids was compared to type specimens of A. palmeri and A. spinosus. The hybrids came from the field where they were originally discovered and clones of ...The growth of clones of seven Amaranthus palmeri x A. spinosus hybrids was compared to type specimens of A. palmeri and A. spinosus. The hybrids came from the field where they were originally discovered and clones of the type specimens and hybrids were established under greenhouse conditions and used to compare growth rates. A. palmeri had the highest growth rate and A. spinosus the lowest growth rate based on height, node counts, and dry weight accumulation. A. palmeri also had the greatest number of days to flowering and A. spinosus the fewest. Hybrids had intermediary growth rates and days to flowering, but differed from each other with regard to sex identity. The hybrids were either dioecious like A. palmeri or, if monoecious, had patterns unlike A. spinosus. Spine length and texture also varied in hybrids and some were without spines. Hybrid 16Ci was short compared to all others and had succulent leaves and stems, which easily separated from the plant body. These hybridizations resulted in morphologically distinct types with acquisition of physical traits intermediate to the type specimens which may drive evolution of these species.展开更多
Weed management is a major component of a soybean (Glycine max L.) production system;thus, managers need tools to help them distinguish soybean from weeds. Vegetation indices derived from light reflectance properties ...Weed management is a major component of a soybean (Glycine max L.) production system;thus, managers need tools to help them distinguish soybean from weeds. Vegetation indices derived from light reflectance properties of plants have shown promise as tools to enhance differences among plants. The objective of this study was to evaluate normalized difference vegetation indices derived from multispectral leaf reflectance data as input into random forest machine learner to differentiate soybean and three broad leaf weeds: Palmer amaranth (Amaranthus palmeri L.), redroot pigweed (A. retroflexus L.), and velvetleaf (Abutilon theophrasti Medik). Leaf reflectance measurements were acquired from plants grown in two separate greenhouse experiments conducted in 2014. Twelve normalized difference vegetation indices were derived from the reflectance measurements, including advanced, green, greenred, green-blue, and normalized difference vegetation indices, shortwave infrared water stress indices, normalized difference pigment and red edge indices, and structure insensitive pigment index. Using the twelve vegetation indices as input variables, the conditional inference version of random forest (cforest) readily distinguished soybean and velvetleaf from the two pigweeds (Palmer amaranth and redroot pigweed) and from each other with classification accuracies ranging from 93.3% to 100%. The greatest errors were observed between the two pigweed classes, with classification accuracies ranging from 70% to 93.3%. Results suggest combining them into one class to increase classification accuracy. Vegetation indices results were equivalent to or slightly better than results obtained with sixteen multispectral bands used as input data into cforest. This research further supports using vegetation indices and machine learning algorithms such as cforest as decision support tools for weed identification.展开更多
The use of microbes and microbial products as bioherbicides has been studied for several decades, and combinations of bioherbicides and herbicides have been examined to discover possible synergistic interactions to im...The use of microbes and microbial products as bioherbicides has been studied for several decades, and combinations of bioherbicides and herbicides have been examined to discover possible synergistic interactions to improve weed control efficacy. Bioassays were conducted to assess possible interactions of the herbicide glufosinate [2-amino-4-(hydroxymethylphosphinyl) butanoic acid] and Colletotrichum truncatum (CT), a fungal bioherbicide to control hemp sesbania (Sesbania exaltata)]. Glufosinate acts as a glutamine synthetase (GS) inhibitor that causes elevated ammonia levels, but the mode of action of CT is unknown. GS has also been implicated in plant defense in certain plant-pathogen interactions. The effects of spray applications of glufosinate (1.0 mM) orbioherbicide (8.0 × 104 conidia ml-1), applied alone or in combination were monitored (88 h time-course) on seedling growth, GS activity and ammonia levels in hypocotyl tissues under controlled environmental conditions. Growth (elongation and fresh weight) and extractable GS activity were inhibited in tissues by glufosinate and glufosinate plus CT treatments as early as 16 h, but CT treatment did not cause substantial growth reduction or GS inhibition until after ~40 h. Generally, ammonia levels in hemp sesbania tissues under these various treatments were inversely correlated with GS activity. Localization of hemp sesbania GS activity on electrophoretic gels indicated a lack of activity after 30 h in glufosinate and glufosinate plus CT-treated tissue. Untreated control tissues contained much lower ammonia levels at 24, 64, and 88 h after treatment than treatments with CT, glufosinate or their combination. CT alone caused elevated ammonia levels only after 64 - 88 h. Glufosinate incorporated in agar at 0.25 mM to 2.0 mM, caused a 10% - 45% reduction of CT colony radial growth, compared to fungal growth on agar without glufosinate, and the herbicide also inhibited sporulation of CT. Although no synergistic interactions were found in the combinations of CT and glufosinate at the concentrations used, further insight on the biochemical action of CT and its interactions with this herbicide on hemp sesbania was achieved.展开更多
There is a growing interest in using miniature multi-sensor technology to monitor plant, soil, and environmental conditions in greenhouses and in field settings. The objectives of this study were to build a small mult...There is a growing interest in using miniature multi-sensor technology to monitor plant, soil, and environmental conditions in greenhouses and in field settings. The objectives of this study were to build a small multi-channel sensing system with ability to measure visible and near infrared light reflectance, relative humidity, and temperature, to test the light reflectance sensors for measuring spectral characteristics of plant leaves and soilless media, and to compare results of the relative humidity and temperature sensors to identical measurement obtained from a greenhouse sensor. The sensing system was built with off-the-shelf miniature multispectral spectrometers and relative humidity and temperature sensors. The spectrometers were sensitive to visible, red-edge, and near infrared light. The system was placed in a greenhouse setting and used to obtain relative reflectance measurements of plant leaves and soilless media and to record temperature and relative humidity conditions in the greenhouse. The spectrometer data obtained from plant leaf and soilless media were compatible with baseline spectral data collected with a hyperspectral spectroradiometer. The greenhouse was equipped with a relative humidity and temperature sensor. The relative humidity and temperature sensor measurements from our sensor system were strongly correlated with the relative humidity and temperature results obtained with the greenhouse sensors...展开更多
Information about the effects of phenotype traits on cottonseed protein, oil, and nutrients is scarce. The objective of this research was to investigate the effects of leaf color trait on seed nutrition in near-isogen...Information about the effects of phenotype traits on cottonseed protein, oil, and nutrients is scarce. The objective of this research was to investigate the effects of leaf color trait on seed nutrition in near-isogenic Gossypium hirsutum cotton expressing green (G) and yellow (Y) leaf color phenotypes. Our hypothesis was that leaf color can influence the accumulation of nutrients in seeds. Sets of isogenic lines were: DES 119 (G) and DES 119 (Y);DP 5690 (G) and DP 5690 (Y);MD 51ne (G) and MD 51ne (Y);SG 747 (G) and SG 747 (Y). Each NIL set is 98.44 % identical. Parent line SA 30 (P) was used as the control. The experiment was repeated for two years (2014 and 2015). The results showed that, in 2014, seed oil in DES 119 (G) and SG 747 (G) were significantly higher than their equivalent yellow lines. Green lines showed higher content of phosphorus compared with yellow lines. Higher levels of Cu, Fe, Mn, Ni, and Zn were recorded in DES 119 (G) and MD 51ne (G). In 2015, seed protein, oil, C, N, P, B, Cu, and Fe were higher in green lines than in yellow lines. There was a significant correlation between protein and nutrients, and between oil and nutrients in 2015, but not in 2014 as the temperature was warmer in 2015 than in 2014. This research demonstrated that leaf color can alter seed composition and mineral nutrition under certain environmental growing conditions such as temperature.展开更多
Palmer amaranth (Amaranthus palmeri S. Wats.) invasion negatively impacts cotton (Gossypium hirsutum L.) production systems throughout the United States. The objective of this study was to evaluate canopy hyperspectra...Palmer amaranth (Amaranthus palmeri S. Wats.) invasion negatively impacts cotton (Gossypium hirsutum L.) production systems throughout the United States. The objective of this study was to evaluate canopy hyperspectral narrowband data as input into the random forest machine learning algorithm to distinguish Palmer amaranth from cotton. The study focused on differentiating the Palmer amaranth from cotton near-isogenic lines with bronze, green, and yellow leaves. A spectroradiometer was used to acquire hyperspectral reflectance measurements of Palmer amaranth and cotton canopies for two separate dates, December 12, 2016, and May 14, 2017. Data were collected from plants that were grown in a greenhouse. The spectral data were aggregated to twenty-four hyperspectral narrowbands proposed for study of vegetation and agriculture crops. Those bands were tested by the conditional inference version of random forest (cforest) to differentiate the Palmer amaranth from cotton. Classifications were binary: Palmer amaranth and cotton bronze, Palmer amaranth and cotton green, and Palmer amaranth and cotton yellow. Classification accuracies were verified with overall, user’s, and producer’s accuracy. For the two dates combined, overall accuracy ranged from 77.8% to 88.9%. The highest overall accuracies were observed for the Palmer amaranth versus the cotton yellow classification (88.9%, December 12, 2016;83.3%, May 14, 2017). Producer’s and user’s accuracies range was 66.7% to 94.4%. Errors were predominately attributed to cotton being misclassified as Palmer amaranth. The overall results indicated that cforest has moderate to strong potential for differentiating Palmer amaranth from cotton when it used hyperspectral narrowbands known to be useful for vegetation and agricultural surveys as input variables. This research further supports using hyperspectral narrowband data and cforest as decision support tools in cotton production systems.展开更多
Widespread distribution of glyphosate-resistant weeds in soybean-growing areas across Mississippi has economically affected soybean planting and follow-up crop management operations. New multiple herbicide-resistant c...Widespread distribution of glyphosate-resistant weeds in soybean-growing areas across Mississippi has economically affected soybean planting and follow-up crop management operations. New multiple herbicide-resistant crop (including soybean) technologies with associated formulations will soon be commercialized. The objectives of this research were to determine the efficacy of new 2,4-D + glyphosate and dicamba formulations on herbicide resistant weeds, and to determine the impact of the new 2,4-D + glyphosate formulation on microbial communities in the soybean rhizosphere involved in nutrient cycling. New 2,4-D + glyphosate and dicamba formulations registered for use on 2,4-D and dicamba-resistant soybean, respectively, adequately controlled glyphosate resistant and susceptible pigweeds (Palmer amaranth and tall waterhemp) and common ragweed. The 2,4-D + glyphosate formulation did not significantly impact soil microbial activities linked to nutrient cycling in the soybean rhizosphere. These results indicate these new 2,4-D + glyphosate and dicamba formulations can be effective in controlling glyphosate resistant and other herbicide resistant weeds while not having adverse effects on the activities of beneficial soil microorganisms.展开更多
To implement strategies to control Palmer amaranth (Amaranthus palmeri S. Wats.) and redroot pigweed (Amaranthus retroflexus L.) infestations in cotton (Gossypium hirsutum L.) production systems, managers need effecti...To implement strategies to control Palmer amaranth (Amaranthus palmeri S. Wats.) and redroot pigweed (Amaranthus retroflexus L.) infestations in cotton (Gossypium hirsutum L.) production systems, managers need effective techniques to identify the weeds. Leaf light reflectance measurements have shown promise as a tool to distinguish crops from weeds. Studies have targeted plants with green leaves. This study focused on using leaf hyperspectral reflectance data to develop spectral profiles of Palmer amaranth, redroot pigweed, and cotton and to determine regions of the light spectrum most sensitive for pigweed and cotton discrimination. The study focused on cotton near-isogenic lines created to have bronze, green, or yellow colored leaves. Reflectance measurements within the 400 to 2500 nm spectral range were obtained from cotton and weed plants grown in a greenhouse in 2015 and 2016. Two scenarios were evaluated for the comparison: (1) Palmer amaranth versus cotton lines and (2) redroot pigweed versus cotton lines. Statistical significance (p ≤ 0.05) was determined with analysis of variance (ANOVA) and Dunnett’s test. Sensitivity measurements were tabulated to determine the optimal region of the light spectrum for weed and cotton line discrimination. Optimal bands for weed and cotton separation were 600 to 700 nm (both weeds versus cotton bronze and cotton yellow), 710 nm (Palmer amaranth versus cotton green), and 1460 nm (redroot pigweed versus cotton green). Spectral bands were identified for separating Palmer amaranth and redroot pigweed from cotton lines with bronze, green, and yellow leaves. Ground-based and airborne sensors can be tuned into the regions of spectrum identified, facilitating using remote sensing technology for Palmer amaranth and redroot pigweed identification in cotton production systems.展开更多
文摘The Integrated Agricultural Systems workgroup is examining agricultural systems of the US to determine fundamental principles that underlie successful production systems. Our hypothesis is that principles are applicable across regions, but key drivers interact to influence producer decisions and create distinct production systems. We interviewed agricultural producers to examine the underlying rationale for producer decisions and discern primary factors influencing production and marketing practices. While drivers are common among regions, interactions between drivers and influences on decision-makers vary substantially to create unique production systems. The internal social driver that values farming lifestyle is the principal factor that leads people to farming. The type of farming is partly a lifestyle choice and is influenced by other factors. Economic drivers and marketing options are primary drivers influencing production systems and management choices, as farmers provide an economic foundation for their families. While all producers employed strategies to manage production and marketing risks, these varied with different marketing channels. Identification of key drivers and principles can be used by producers, scientists and policy makers to direct agricultural production and agricultural research. New management systems can be developed that are flexible enough to respond to changing societal demands, and are environmentally and economically sustainable.
基金supported by United States Department of Agriculture,Agricultural Research Service(No.58-8042-9-072)United States Department of Agriculture-National Institute of Food and Agriculture(No.2019-34263-30552)+1 种基金Management Information System(No.043050)United States Department of Agriculture-Agricultural Research Service-Non-Assistance Cooperative Agreement(No.58-6066-2-030).
文摘Background GOSSYM is a mechanistic,process-based cotton model that can simulate cotton crop growth and development,yield,and fiber quality.Its fiber quality module was developed based on controlled experiments explicitly conducted on the Texas Marker^(-1)(TM1)variety,potentially making its functional equations more aligned with this cultivar.To assess the model’s broader applicability,this study analyzed fiber quality data from 40 upland cotton cultivars,including TM1.The measured fiber quality from all cultivars was then compared with the modelsimulated fiber quality.Results Among the 40 upland cultivars,fiber strength varied from 28.4 cN·tex^(-1) to 34.6 cN·tex^(-1),fiber length ranged from 27.1 mm to 33.3 mm,micronaire value ranged from 2.7 to 4.6,and length uniformity index varied from 82.3%to 85.5%.The model simulated fiber quality closely matched the measured values for TM1,with the absolute percentage error(APE)being less than 0.92%for fiber strength,fiber length,and length uniformity index and 4.7%for micronaire.However,significant differences were observed for the other cultivars.The Pearson correlation coefficient(r)between the measured and simulated values was negative for all fiber quality traits,and Wilmotts’s index of agreement(WIA)was below 0.45,indicating a strong model bias toward TM1 without incorporating cultivar-specific parameters.After incorporating cultivar-specific parameters,the model’s performance improved significantly,with an average r-value of 0.84 and WIA of 0.88.Conclusions The adopted methodology and estimated cultivar-specific parameters improved the model’s simulation accuracy.This approach can be applied to newer cotton cultivars,enhancing the GOSSYM model’s utility and its applicability for agricultural management and policy decisions.
基金financially supported by the funding appropriated from USDA-ARS National Program 305 Crop Productionthe 948 Program of Ministry of Agriculture of China (2016-X38)
文摘Big data with its vast volume and complexity is increasingly concerned, developed and used for all professions and trades. Remote sensing, as one of the sources for big data, is generating earth-observation data and analysis results daily from the platforms of satellites, manned/unmanned aircrafts, and ground-based structures. Agricultural remote sensing is one of the backbone technologies for precision agriculture, which considers within-field variability for site-specific management instead of uniform management as in traditional agriculture. The key of agricultural remote sensing is, with global positioning data and geographic information, to produce spatially-varied data for subsequent precision agricultural operations. Agricultural remote sensing data, as general remote sensing data, have all characteristics of big data. The acquisition, processing, storage, analysis and visualization of agricultural remote sensing big data are critical to the success of precision agriculture. This paper overviews available remote sensing data resources, recent development of technologies for remote sensing big data management, and remote sensing data processing and management for precision agriculture. A five-layer-fifteen- level （FLFL） satellite remote sensing data management structure is described and adapted to create a more appropriate four-layer-twelve-level （FLTL） remote sensing data management structure for management and applications of agricultural remote sensing big data for precision agriculture where the sensors are typically on high-resolution satellites, manned aircrafts, unmanned aerial vehicles and ground-based structures. The FLTL structure is the management and application framework of agricultural remote sensing big data for precision agriculture and local farm studies, which outlooks the future coordination of remote sensing big data management and applications at local regional and farm scale.
文摘This study investigated three different types of multispectral imaging systems for airborne remote sensing to support management in agricultural application and production.The three systems have been used in agricultural studies.They range from low-cost to relatively high-cost,manually operated to automated,multispectral composite imaging with a single camera and integrated imaging with custom-mounting of separate cameras.Practical issues regarding use of the imaging systems were described and discussed.The low-cost system,due to band saturation,slow imaging speed and poor image quality,is more preferable to slower moving platforms that can fly close to the ground,such as unmanned autonomous helicopters,but not recommended for low or high altitude aerial remote sensing on fixed-wing aircraft.With the restriction on payload unmanned autonomous helicopters are not recommended for high-cost systems because they are typically heavy and difficult to mount.The system with intermediate cost works well for low altitude aerial remote sensing on fixed-wing aircraft with field shapefile-based global positioning triggering.This system also works well for high altitude aerial remote sensing on fixed-wing aircraft with global positioning triggering or manually operated.The custom-built system is recommended for high altitude aerial remote sensing on fixed-wing aircraft with waypoint global positioning triggering or manually operated.
文摘Precision agriculture accounts for within-field variability for targeted treatment rather than uniform treatment of an entire field.It is built on agricultural mechanization and state-of-the-art technologies of geographical information systems(GIS),global positioning systems(GPS)and remote sensing,and is used to monitor soil,crop growth,weed infestation,insects,diseases,and water status in farm fields to provide data and information to guide agricultural management practices.Precision agriculture began with mapping of crop fields at different scales to support agricultural planning and decision making.With the development of variable-rate technology,precision agriculture focuses more on tactical actions in controlling variable-rate seeding,fertilizer and pesticide application,and irrigation in real-time or within the crop season instead of mapping a field in one crop season to make decisions for the next crop season.With the development of aerial variable-rate systems,low-altitude airborne systems can provide high-resolution data for prescription variable-rate operations.Airborne systems for multispectral imaging using a number of imaging sensors(cameras)were developed.Unmanned aerial vehicles(UAVs)provide a unique platform for remote sensing of crop fields at slow speeds and low-altitudes,and they are efficient and more flexible than manned agricultural airplanes,which often cannot provide images at both low altitude and low speed for capture of high-quality images.UAVs are also more universal in their applicability than agricultural aircraft since the latter are used only in specific regions.This study presents the low-altitude remote sensing systems developed for detection of crop stress caused by multiple factors.UAVs,as a special platform,were discussed for crop sensing based on the researchers'studies.
文摘Even though annual rainfall is high in the Delta region of Mississippi, only 30% occurs during the months in which the major crops are produced, making irrigation often necessary to meet crop water needs and to avoid risk of yield and profitability loss. Approximately, 65% of the farmland in this region is irrigated. The shallow Mississippi River Valley Alluvial Aquifer is the major source of water for irrigation and for aquaculture in the predominant catfish industry. This groundwater is being heavily used as row-crop irrigation has increased tremendously. Water level in this aquifer has declined significantly over the past twenty five years, with overdraft of approximately 370 million cubic meters of water per year. Moreover, the common irrigation practices in the Delta re-gion of Mississippi do not use water efficiently, further depleting the ground water and making ir-rigation more expensive to producers due to increasing energy prices. Irrigation experts in the re-gion have tested and verified various methods and tools that increase irrigation efficiency. This article presents a review of the current status of the irrigation practices in the Delta region of Mis-sissippi, and the improved methods and tools that are available to increase irrigation efficiency and to reduce energy costs for producers in the region as well as to stop the overdraft of the declining aquifer, ensuring its sustainable use.
文摘Italian ryegrass is an annual/biennial grass that is typically used as a pasture crop or a cover crop along roadsides, rights-of-way, and industrial areas. Glyphosate-resistant (GR) Italian ryegrass populations have been documented around the world, mostly in orchard and vineyard situations. The first evidence of evolved GR Italian ryegrass in row/agronomic crops was reported from Washington County, Mississippi in 2005. GR Italian ryegrass populations can jeopardize preplant burndown options in reduced-tillage crop production systems, thereby, delaying planting operations. The effects of competition of Italian ryegrass on crop growth and yield are poorly understood. A field study was conducted in the 2012 growing season and repeated in the 2013 growing season. GR and susceptible (GS) Italian ryegrass populations were established in the greenhouse and transplanted in prepared corn row beds in the fall of 2011 and 2012 at 0, 1, 2, 3, and 4 plants·meter> of crop row. Italian ryegrass plants overwintered and developed over the following spring-summer. Glyphosate was applied at 1.26 kg·ae/ha (1.5× of labeled rate) in the spring to burndown the Italian ryegrass plants and corn was planted into the ryegrass residue 2 - 3 wk later. Current corn production practices were followed. Corn density (early and late season), height (early season), and yield and Italian ryegrass biomass (early-mid season) measurements were recorded during both years. Corn height was greater in 2012 than that in 2013 at comparable stages of the growing season, due to a cooler and wetter early season in 2013 than that in 2012. Averaged across weed densities, corn density (both early and late season) and yield were higher in the GS than those in the GR population, but Italian ryegrass biomass was similar for both populations. Averaged across Italian ryegrass populations, corn density (both early and late season), and yield were inversely proportional to Italian ryegrass density. In summary, Italian ryegrass significantly reduced corn density and yield and reduction was greater with the GR than that with the GS population. Studies are underway to study inter population competition in Italian ryegrass and investigate allelopathic effects of Italian ryegrass on selected crops.
文摘The wide distribution of Palmer amaranth (Amaranthus palmeri) in the southern US became a serious weed control problem prior to the extensive use of glyphosate-resistant crops. Currently glyphosate-resistant populations of Palmer amaranth occur in many areas of this geographic region creating an even more serious threat to crop production. Investigations were undertaken using four biotypes (one glyphosate-sensitive, one resistant from Georgia and two of unknown tolerance from Mississippi) of Palmer amaranth to assess bioassay techniques for the rapid detection and level of resistance in populations of this weed. These plants were characterized with respect to chlorophyll, betalain, and protein levels and immunological responses to an antibody of 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) the target site of glyphosate. Only slight differences were found in four biotypes grown under greenhouse conditions regarding extractable soluble protein and chlorophyll content, but one biotype was found to be devoid of the red pigment, betalain. Measurement of early growth (seedling shoot elongation) of seedlings was a useful detection tool to determine glyphosate resistance. A leaf disc bioassay (using visual ratings and/or chlorophyll analysis) and an assay for shikimate accumulation were effective methods for determining herbicide resistance levels. The two unknown biotypes were found to be resistant to this herbicide. Some differences were found in the protein profiles of the biotypes, and western blots demonstrated a weak labeling of antibody in the glyphosate-sensitive biotype, whereas strong labeling occurred in the resistant plants. This latter point supports research by others, that increased copy number of the EPSPS gene (and increased EPSPS protein levels) is the resistance mechanism in this species. Results indicate the utility of certain bioassays for the determination of resistance and provide useful comparative information on the levels of inherent constituents among closely related plants.
文摘Velvetleaf (Abutilon theophrasti Medic.) infestations negatively impact row crop production throughout the United States and Canada’s eastern provinces. To implement management strategies to control velvetleaf, managers need tools for differentiating it from crop plants. 5 Band, 7 Band, 8 Band, and 16 Band multispectral datasets simulating LANDSAT 3 plus a blue band, LANDSAT 8, WorldView 2, and WorldView 3 spectral bands, respectively were tested as input into the random forest algorithm for velvetleaf soybean [Glycine max L. (Merr.)] discrimination. During two separate greenhouse experiments in 2014, leaf reflectance measurements were obtained at the vegetative growth stage of velvetleaf plants and two soybean varieties. The reflectance measurements were collected with a plant contact probe attached to a hyperspectral spectroradiometer. Leaf hyperspectral reflectance measurements were convolved to the four multispectral datasets with computer software. Overall, user’s, and producer’s accuracies and kappa coefficient were employed to determine classification accuracies. Using the multispectral datasets as input, the random forest algorithm differentiated velvetleaf from the soybean varieties with accuracies ranging from 86.7% to 100%. 7 Band, 16 Band, 8 Band, and 5 Band datasets ranked or tied for the highest accuracies seventeen, sixteen, twelve, and one time, respectively. Kappa coefficients indicated an almost perfect agreement (i.e., kappa value, 0.81 - 1.0) to substantial agreement (i.e., kappa value, 0.61 - 0.80) between reference data and model predicted classes. This study was the first to demonstrate the application of the random forest machine learner and leaf multispectral reflectance data as tools to distinguish velvetleaf from soybean and to identify multispectral band combinations providing the best accuracies. Findings support further application of the random forest machine learner along with remotely-sensed multispectral data as tools for velvetleaf soybean discrimination with future implications for site-specific management of velvetleaf.
文摘Previously we found that a strain of Myrothecium verrucaria (MV) exhibited bioherbicidal activity against several important weeds, and that some commercial formulations of glyphosate applied with MV resulted in synergistic interactions that improved weed control efficacy. We also found that MV had bioherbicidal activity against glyphosate-resistant Palmer amaranth. We have also reported that some commercial formulations are inhibitory to MV. Our objectives were to test the effect of unformulated glyphosate (high purity, technical-grade glyphosate) alone and in combination with MV for bioherbicidal activity on glyphosate-susceptible and -resistant Palmer amaranth biotypes under greenhouse conditions and to examine technical-grade glyphosate on the growth of this bioherbicide. High purity glyphosate (without adjuvants/surfactants) was not toxic to MV growth and sporulation at concentrations up to 2.0 mM when grown on agar supplemented with the herbicide. Both biotypes were injured by MV and MV plus glyphosate treatments as early as 19 h after application (3 h after a dew period of 16 h). These injury effects increased and were more evident through the 6-day time course, when after 120 h the MV plus glyphosate treatment had killed all glyphosate-susceptible and -resistant plants. The interaction of glyphosate plus MV was synergistic toward the control of Palmer amaranth. Data strongly suggest that the active ingredient is responsible for the synergy previously found when this bioherbicide was combined with some commercial formulations of glyphosate. Results demonstrated that MV can control both glyphosate-resistant and -susceptible Palmer amaranth seedlings and act synergistically with high-purity glyphosate to provide improved weed control.
文摘Evapotranspiration is an important component in water-balance and irrigation scheduling models. While the FAO-56 Penman-Monteith method has become the de facto standard for estimating reference evapotranspiration (ETo), it is a complex method requiring several weather parameters. Required weather data are oftentimes unavailable, and alternative methods must be used. Three alternative ETo methods, the FAO-56 Reduced Set, Hargreaves, and Turc methods, were evaluated for use in Mississippi, a humid region of the USA, using only measurements of air temperature. The Turc equation, developed for use with measured temperature and solar radiation, was tested with estimated radiation and found to provide better estimates of FAO-56 ETo than the other methods. Mean bias errors of 0.75, 0.28, and -0.19 mm, mean absolute errors of 0.92, 0.68, and 0.62 mm, and percent errors of 22.5%, 8.5%, and -5.7% were found for daily estimates for the FAO-56 Reduced Set, Hargreaves, and Turc methods, respectively.
文摘The growth of clones of seven Amaranthus palmeri x A. spinosus hybrids was compared to type specimens of A. palmeri and A. spinosus. The hybrids came from the field where they were originally discovered and clones of the type specimens and hybrids were established under greenhouse conditions and used to compare growth rates. A. palmeri had the highest growth rate and A. spinosus the lowest growth rate based on height, node counts, and dry weight accumulation. A. palmeri also had the greatest number of days to flowering and A. spinosus the fewest. Hybrids had intermediary growth rates and days to flowering, but differed from each other with regard to sex identity. The hybrids were either dioecious like A. palmeri or, if monoecious, had patterns unlike A. spinosus. Spine length and texture also varied in hybrids and some were without spines. Hybrid 16Ci was short compared to all others and had succulent leaves and stems, which easily separated from the plant body. These hybridizations resulted in morphologically distinct types with acquisition of physical traits intermediate to the type specimens which may drive evolution of these species.
文摘Weed management is a major component of a soybean (Glycine max L.) production system;thus, managers need tools to help them distinguish soybean from weeds. Vegetation indices derived from light reflectance properties of plants have shown promise as tools to enhance differences among plants. The objective of this study was to evaluate normalized difference vegetation indices derived from multispectral leaf reflectance data as input into random forest machine learner to differentiate soybean and three broad leaf weeds: Palmer amaranth (Amaranthus palmeri L.), redroot pigweed (A. retroflexus L.), and velvetleaf (Abutilon theophrasti Medik). Leaf reflectance measurements were acquired from plants grown in two separate greenhouse experiments conducted in 2014. Twelve normalized difference vegetation indices were derived from the reflectance measurements, including advanced, green, greenred, green-blue, and normalized difference vegetation indices, shortwave infrared water stress indices, normalized difference pigment and red edge indices, and structure insensitive pigment index. Using the twelve vegetation indices as input variables, the conditional inference version of random forest (cforest) readily distinguished soybean and velvetleaf from the two pigweeds (Palmer amaranth and redroot pigweed) and from each other with classification accuracies ranging from 93.3% to 100%. The greatest errors were observed between the two pigweed classes, with classification accuracies ranging from 70% to 93.3%. Results suggest combining them into one class to increase classification accuracy. Vegetation indices results were equivalent to or slightly better than results obtained with sixteen multispectral bands used as input data into cforest. This research further supports using vegetation indices and machine learning algorithms such as cforest as decision support tools for weed identification.
文摘The use of microbes and microbial products as bioherbicides has been studied for several decades, and combinations of bioherbicides and herbicides have been examined to discover possible synergistic interactions to improve weed control efficacy. Bioassays were conducted to assess possible interactions of the herbicide glufosinate [2-amino-4-(hydroxymethylphosphinyl) butanoic acid] and Colletotrichum truncatum (CT), a fungal bioherbicide to control hemp sesbania (Sesbania exaltata)]. Glufosinate acts as a glutamine synthetase (GS) inhibitor that causes elevated ammonia levels, but the mode of action of CT is unknown. GS has also been implicated in plant defense in certain plant-pathogen interactions. The effects of spray applications of glufosinate (1.0 mM) orbioherbicide (8.0 × 104 conidia ml-1), applied alone or in combination were monitored (88 h time-course) on seedling growth, GS activity and ammonia levels in hypocotyl tissues under controlled environmental conditions. Growth (elongation and fresh weight) and extractable GS activity were inhibited in tissues by glufosinate and glufosinate plus CT treatments as early as 16 h, but CT treatment did not cause substantial growth reduction or GS inhibition until after ~40 h. Generally, ammonia levels in hemp sesbania tissues under these various treatments were inversely correlated with GS activity. Localization of hemp sesbania GS activity on electrophoretic gels indicated a lack of activity after 30 h in glufosinate and glufosinate plus CT-treated tissue. Untreated control tissues contained much lower ammonia levels at 24, 64, and 88 h after treatment than treatments with CT, glufosinate or their combination. CT alone caused elevated ammonia levels only after 64 - 88 h. Glufosinate incorporated in agar at 0.25 mM to 2.0 mM, caused a 10% - 45% reduction of CT colony radial growth, compared to fungal growth on agar without glufosinate, and the herbicide also inhibited sporulation of CT. Although no synergistic interactions were found in the combinations of CT and glufosinate at the concentrations used, further insight on the biochemical action of CT and its interactions with this herbicide on hemp sesbania was achieved.
文摘There is a growing interest in using miniature multi-sensor technology to monitor plant, soil, and environmental conditions in greenhouses and in field settings. The objectives of this study were to build a small multi-channel sensing system with ability to measure visible and near infrared light reflectance, relative humidity, and temperature, to test the light reflectance sensors for measuring spectral characteristics of plant leaves and soilless media, and to compare results of the relative humidity and temperature sensors to identical measurement obtained from a greenhouse sensor. The sensing system was built with off-the-shelf miniature multispectral spectrometers and relative humidity and temperature sensors. The spectrometers were sensitive to visible, red-edge, and near infrared light. The system was placed in a greenhouse setting and used to obtain relative reflectance measurements of plant leaves and soilless media and to record temperature and relative humidity conditions in the greenhouse. The spectrometer data obtained from plant leaf and soilless media were compatible with baseline spectral data collected with a hyperspectral spectroradiometer. The greenhouse was equipped with a relative humidity and temperature sensor. The relative humidity and temperature sensor measurements from our sensor system were strongly correlated with the relative humidity and temperature results obtained with the greenhouse sensors...
文摘Information about the effects of phenotype traits on cottonseed protein, oil, and nutrients is scarce. The objective of this research was to investigate the effects of leaf color trait on seed nutrition in near-isogenic Gossypium hirsutum cotton expressing green (G) and yellow (Y) leaf color phenotypes. Our hypothesis was that leaf color can influence the accumulation of nutrients in seeds. Sets of isogenic lines were: DES 119 (G) and DES 119 (Y);DP 5690 (G) and DP 5690 (Y);MD 51ne (G) and MD 51ne (Y);SG 747 (G) and SG 747 (Y). Each NIL set is 98.44 % identical. Parent line SA 30 (P) was used as the control. The experiment was repeated for two years (2014 and 2015). The results showed that, in 2014, seed oil in DES 119 (G) and SG 747 (G) were significantly higher than their equivalent yellow lines. Green lines showed higher content of phosphorus compared with yellow lines. Higher levels of Cu, Fe, Mn, Ni, and Zn were recorded in DES 119 (G) and MD 51ne (G). In 2015, seed protein, oil, C, N, P, B, Cu, and Fe were higher in green lines than in yellow lines. There was a significant correlation between protein and nutrients, and between oil and nutrients in 2015, but not in 2014 as the temperature was warmer in 2015 than in 2014. This research demonstrated that leaf color can alter seed composition and mineral nutrition under certain environmental growing conditions such as temperature.
文摘Palmer amaranth (Amaranthus palmeri S. Wats.) invasion negatively impacts cotton (Gossypium hirsutum L.) production systems throughout the United States. The objective of this study was to evaluate canopy hyperspectral narrowband data as input into the random forest machine learning algorithm to distinguish Palmer amaranth from cotton. The study focused on differentiating the Palmer amaranth from cotton near-isogenic lines with bronze, green, and yellow leaves. A spectroradiometer was used to acquire hyperspectral reflectance measurements of Palmer amaranth and cotton canopies for two separate dates, December 12, 2016, and May 14, 2017. Data were collected from plants that were grown in a greenhouse. The spectral data were aggregated to twenty-four hyperspectral narrowbands proposed for study of vegetation and agriculture crops. Those bands were tested by the conditional inference version of random forest (cforest) to differentiate the Palmer amaranth from cotton. Classifications were binary: Palmer amaranth and cotton bronze, Palmer amaranth and cotton green, and Palmer amaranth and cotton yellow. Classification accuracies were verified with overall, user’s, and producer’s accuracy. For the two dates combined, overall accuracy ranged from 77.8% to 88.9%. The highest overall accuracies were observed for the Palmer amaranth versus the cotton yellow classification (88.9%, December 12, 2016;83.3%, May 14, 2017). Producer’s and user’s accuracies range was 66.7% to 94.4%. Errors were predominately attributed to cotton being misclassified as Palmer amaranth. The overall results indicated that cforest has moderate to strong potential for differentiating Palmer amaranth from cotton when it used hyperspectral narrowbands known to be useful for vegetation and agricultural surveys as input variables. This research further supports using hyperspectral narrowband data and cforest as decision support tools in cotton production systems.
文摘Widespread distribution of glyphosate-resistant weeds in soybean-growing areas across Mississippi has economically affected soybean planting and follow-up crop management operations. New multiple herbicide-resistant crop (including soybean) technologies with associated formulations will soon be commercialized. The objectives of this research were to determine the efficacy of new 2,4-D + glyphosate and dicamba formulations on herbicide resistant weeds, and to determine the impact of the new 2,4-D + glyphosate formulation on microbial communities in the soybean rhizosphere involved in nutrient cycling. New 2,4-D + glyphosate and dicamba formulations registered for use on 2,4-D and dicamba-resistant soybean, respectively, adequately controlled glyphosate resistant and susceptible pigweeds (Palmer amaranth and tall waterhemp) and common ragweed. The 2,4-D + glyphosate formulation did not significantly impact soil microbial activities linked to nutrient cycling in the soybean rhizosphere. These results indicate these new 2,4-D + glyphosate and dicamba formulations can be effective in controlling glyphosate resistant and other herbicide resistant weeds while not having adverse effects on the activities of beneficial soil microorganisms.
文摘To implement strategies to control Palmer amaranth (Amaranthus palmeri S. Wats.) and redroot pigweed (Amaranthus retroflexus L.) infestations in cotton (Gossypium hirsutum L.) production systems, managers need effective techniques to identify the weeds. Leaf light reflectance measurements have shown promise as a tool to distinguish crops from weeds. Studies have targeted plants with green leaves. This study focused on using leaf hyperspectral reflectance data to develop spectral profiles of Palmer amaranth, redroot pigweed, and cotton and to determine regions of the light spectrum most sensitive for pigweed and cotton discrimination. The study focused on cotton near-isogenic lines created to have bronze, green, or yellow colored leaves. Reflectance measurements within the 400 to 2500 nm spectral range were obtained from cotton and weed plants grown in a greenhouse in 2015 and 2016. Two scenarios were evaluated for the comparison: (1) Palmer amaranth versus cotton lines and (2) redroot pigweed versus cotton lines. Statistical significance (p ≤ 0.05) was determined with analysis of variance (ANOVA) and Dunnett’s test. Sensitivity measurements were tabulated to determine the optimal region of the light spectrum for weed and cotton line discrimination. Optimal bands for weed and cotton separation were 600 to 700 nm (both weeds versus cotton bronze and cotton yellow), 710 nm (Palmer amaranth versus cotton green), and 1460 nm (redroot pigweed versus cotton green). Spectral bands were identified for separating Palmer amaranth and redroot pigweed from cotton lines with bronze, green, and yellow leaves. Ground-based and airborne sensors can be tuned into the regions of spectrum identified, facilitating using remote sensing technology for Palmer amaranth and redroot pigweed identification in cotton production systems.
