Explore the vast range of titles available.

A field study was conducted from 2020 to 2023 at Kansas State University Agricultural Research Center near Hays, KS, to understand the emergence dynamics and periodicity of glyphosate-resistant (GR) Palmer amaranth (Amaranthus palmeri S. Watson) as influenced by cover crop (CC) residue and residual herbicide in grain sorghum [Sorghum bicolor (L.) Moench]. The study site was under a wheat (Triticum aestivum L.)–sorghum–fallow rotation with a natural seedbank of GR A. palmeri. Treatments included (1) fall-planted CC mixture [winter triticale (×Triticosecale Wittm. ex A. Camus [Secale × Triticum])/winter peas (Pisum sativum L.)/ rapeseed (Brassica napus L.)/radish (Raphanus sativus L.)] after wheat harvest and terminated at triticale heading stage (next spring before sorghum planting) with glyphosate alone or (2) glyphosate plus acetochlor/atrazine, (3) chemical fallow (no CC but treated with acetochlor/ atrazine and dicamba before sorghum planting), and (4) nontreated control (no CC and no herbicide). Results indicated that CC terminated with glyphosate plus acetochlor/atrazine had a delayed and reduced cumulative emergence of GR A. palmeri as compared with chemical fallow and CC terminated with glyphosate alone across all 3 yr. Compared with chemical fallow, the CC terminated with glyphosate alone and glyphosate plus acetochlor/atrazine required 66 to 643 and 105 to 1,257 more cumulative growing degree days, respectively, to achieve 90% cumulative emergence of GR A. palmeri across all 3 yr. The combined effect of CC residue with glyphosate plus acetochlor/atrazine reduced the total emergence counts of GR A. palmeri by 42% to 56% and 82% to 94% as compared with chemical fallow and nontreated control, respectively. These results suggest that fall-planted CC combined with a residual herbicide at termination can be utilized for GR A. palmeri suppression in grain sorghum.

The current study aimed to screen glyphosate-alternative weed control methods in three perennial crops in Greece. Field trials were conducted and repeated (2018 to 2019 and 2019 to 2020) in a citrus orchard (Citrus clementina Hort. ex Tan), an olive grove (Olea europaea L.), and a vineyard (Vitis vinifera L.) under the randomized complete block design (nine treatments, four blocks). Glyphosate was applied in the citrus orchard (720 g ae ha–1), the olive grove (720 g ae ha–1), and the vineyard (1,800 g ae ha–1). Pelargonic acid (1,088 g ha–1; two times), barley (Hordeum vulgare L.) residues and white mustard (Sinapis alba L.) residues were evaluated in all sites. Mowing was evaluated in the citrus orchard (one time) and the vineyard (two times). Flazasulfuron (50 g ha–1), oxyfluorfen (144 g ha–1), and flumioxazin (150 g ha–1) were applied in the citrus orchard and the olive grove. Penoxsulam + florasulam (15 + 7.5 g ha–1) was also applied in the olive grove. Cycloxydim (200 g ha–1), quizalofop-p-ethyl (150 g ha–1) and propaquizafop (150 g ha–1) were applied in the vineyard. An untreated control was included in all sites. Flazasulfuron, oxyfluorfen, and flumioxazin resulted in similar normalized difference vegetation index (NDVI) and weed biomass to glyphosate in the citrus orchard in both years and evaluations. Pelargonic acid (two times) and mowing (one time) were effective on broadleaf weeds. Flazasulfuron and penoxsulam + florasulam were the most promising glyphosate-alternative weed control methods against hairy fleabane [Conyza bonariensis (L.) Cronquist] in the olive grove. Cover crop residues showed their suppressive ability as in the citrus orchard. All selective herbicides resulted in similar NDVI and johnsongrass [Sorghum halepense (L.) Pers.] dry weight values in the vineyard in both years. Negative and strong correlations were observed in all sites and years between crop yield and weed dry weight (R2 = 0.543 to 0.924).

Quantifying crop residue cover (CRC) on field surfaces is important for monitoring the tillage intensity and promoting sustainable management. Remote-sensing-based techniques have proven practical for determining CRC, however, the methods used are primarily limited to empirical regression based on crop residue indices (CRIs). This study provides a systematic evaluation of empirical regressions and machine learning (ML) algorithms based on their ability to estimate CRC using Sentinel-2 Multispectral Instrument (MSI) data. Unmanned aerial vehicle orthomosaics were used to extracted ground CRC for training Sentinel-2 data-based CRC models. For empirical regression, nine MSI bands, 10 published CRIs, three proposed CRIs, and four mean textural features were evaluated using univariate linear regression. The best performance was obtained by a three-band index calculated using (B2 − B4)/(B2 − B12), with an R2cv of 0.63 and RMSEcv of 6.509%, using a 10-fold cross-validation. The methodologies of partial least squares regression (PLSR), artificial neural network (ANN), Gaussian process regression (GPR), support vector regression (SVR), and random forest (RF) were compared with four groups of predictors, including nine MSI bands, 13 CRIs, a combination of MSI bands and mean textural features, and a combination of CRIs and textural features. In general, ML approaches achieved high accuracy. A PLSR model with 13 CRIs and textural features resulted in an accuracy of R2cv = 0.66 and RMSEcv = 6.427%. An RF model with predictors of MSI bands and textural features estimated CRC with an R2cv = 0.61 and RMSEcv = 6.415%. The estimation was improved by an SVR model with the same input predictors (R2cv = 0.67, RMSEcv = 6.343%), followed by a GPR model based on CRIs and textural features. The performance of GPR models was further improved by optimal input variables. A GPR model with six input variables, three MSI bands and three textural features, performed the best, with R2cv = 0.69 and RMSEcv = 6.149%. This study provides a reference for estimating CRC from Sentinel-2 imagery using ML approaches. The GPR approach is recommended. A combination of spectral information and textural features leads to an improvement in the retrieval of CRC.

Aims The application of cover crop residue is an important means in managing paddy soil. This study was to investigate the influences of minerals on the mineralization of organic matter and the community and necromass of microbes in paddy soils amended with cover crop residue. Methods 13 C-labelled cover crop residue ( Astragalus sinicus L. ) was prepared using a pulse labeling method. The mineralization of cover crop residue and native soil organic matter and the community and necromass of microbes in two flooded paddy soils amended with or without illite, goethite and ferrihydrite was investigated by incubation experiments. The released CO 2 /CH 4 was analyzed by gas chromatography. Amino sugar was used as the biomarker of microbial residue carbon. Soil microbial communities were analyzed by high-throughput sequencing and quantitative polymerase chain reaction. Results Illite, goethite and ferrihydrite significantly decreased the amounts of both CO 2 and CH 4 emissions from native soil organic carbon in both paddy soils. Moreover, the inhibition efficiency followed the same sequence of ferrihydrite > goethite > illite for both CO 2 and CH 4 emissions. However, ferrihydrite significantly stimulated the mineralization of cover crop residue in both paddy soils. The examined minerals, especially ferrihydrite, also tended to decrease bacterial and fungal abundance and diversity in both paddy soils. Moreover, the contents of both bacterial and fungal residue carbon were significantly decreased by the examined minerals in alkaline paddy soil. Conclusion Examined minerals tended to decrease total mineralization of cover crop residue and native organic matter, the microbial abundance, diversity, and necromass in flooded paddy soils.

Soil erosion within agricultural landscapes has significant environmental and economic impacts and is strongly driven by reduced residue cover in agricultural fields. Large-area soil erosion models such as the Daily Erosion Project are important tools for understanding the patterns of soil erosion, but they rely on the accurate estimation of crop residue cover over large regions to infer the tillage practices, an erosion model input. Remote sensing analyses are becoming accepted as a reliable way to estimate crop residue cover, but most use localized training datasets that may not scale well outside small study areas. An alternative source of training data may be commonly conducted tillage surveys that capture information via rapid “windshield” surveys. In this study, we utilized the Google Earth Engine to assess the utility of three crop residue survey types (windshield tillage surveys, windshield binned residue surveys, and photo analysis surveys) and one synthetic survey (retroactively binned photo analysis data) as sources of training data for crop residue cover regressions. We found that neither windshield-based survey method was able to produce reliable regressions but that they can produce reasonable distinctions between low-residue and high-residue fields. On the other hand, both photo analysis and retroactively binned photo analysis survey data were able to produce reliable regressions with r2 values of 0.57 and 0.56, respectively. Overall, this study demonstrates that photo analysis surveys are the most reliable dataset to use when creating crop residue cover models, but we also acknowledge that these surveys are expensive to conduct and suggest some ways these surveys could be made more efficient in the future.

Crop residue left in the field after harvest helps to protect against water and wind erosion, increase soil organic matter, and improve soil quality, so a proper estimate of the quantity of crop residue is crucial to optimize tillage and for research into environmental effects. Although remote-sensing-based techniques to estimate crop residue cover (CRC) have proven to be good tools for determining CRC, their application is limited by variations in the moisture of crop residue and soil. In this study, we propose a crop residue angle index (CRAI) to estimate the CRC for four distinct soils with varying soil moisture (SM) content and crop residue moisture (CRM). The current study uses laboratory-based tests ((i) a dry dataset (air-dried soils and crop residues, n = 392); (ii) a wet dataset (wet soils and crop residues, n = 822); (iii) a saturated dataset (saturated soils and crop residues, n = 402); and (iv) all datasets (n = 1616)), which allows us to analysis the soil and crop residue hyperspectral response to varying SM/CRM. The CRAI combines two features that reflect the moisture content in soil and crop residue. The first is the different reflectance of soil and crop residue as a function of moisture in the near-infrared band (833 nm) and short-wave near-infrared band (1670 nm), and the second is different reflectance of soils and crop residues to lignin, cellulose, and moisture in the bands at 2101, 2031, and 2201 nm. The effects of moisture and soil type on the proposed CRAI and selected traditional spectral indices ((i) hyperspectral cellulose absorption index; (ii) hyperspectral shortwave infrared normalized difference residue index; and (iii) selected broad-band spectral indices) were compared by using a laboratory-based dataset. The results show that the SM/CRM significantly affects the broad-band spectral indices and all other spectral indices investigated are less correlated with CRC when using all datasets than when using only the dry, wet, or saturated dataset. Laboratory study suggests that the CRAI is promising for estimating CRC with the four soils and with varying SM/CRM. However, because the CRAI was only validated by a laboratory-based dataset, additional field testing is thus required to verify the use of satellite hyperspectral remote-sensing images for different crops and ecological areas.

Crop Residue Cover (CRC) is crucial for enhancing soil quality and mitigating erosion in agricultural fields. Accurately estimating CRC in near real-time presents challenges due to the limitations of traditional and remote sensing methods. This study addresses the challenge of accurately estimating CRC using unsupervised algorithms on high-resolution Unmanned Aerial System (UAS) imagery. We employ two methods to perform CRC estimation: (1) K-means unsupervised algorithm and (2) Principal Component Analysis (PCA) along with the Otsu thresholding technique. The advantages of these methods lie in their independence from human intervention for any supervised training stage. Additionally, these methods are rapid and suitable for near real-time estimation of CRC as a decision-making support in agricultural management. Our analysis reveals that the K-means method, with an R2=0.79, achieves superior accuracy in CRC estimation over the PCA-Otsu method with an R2=0.46. The accuracy of CRC estimation for both corn and soybean crops is significantly higher in winter than in spring, attributable to the more weathered state of crop residue. Furthermore, CRC estimations in corn fields exhibit a stronger correlation, likely due to the larger size of corn residue which enhances detectability in images. Nevertheless, the variance in CRC estimation accuracy between corn and soybean fields is minimal. Furthermore, CRC estimation achieves the highest correlation in no-till fields, while the lowest correlation is observed in conventionally tilled fields, a difference likely due to the soil disturbance during plowing in conventional tillage practices.

The combination of soil residual herbicides and cover crops is an integral part of best management practices for herbicide-resistant weeds. However, the interception of soil residual herbicides by cover crop biomass interferes with herbicides reaching the soil, which can lead to lower weed control efficacy and increased selection pressure for herbicide resistance. Once intercepted, these herbicides can only move to the soil with water from rainfall or irrigation. Field trials were conducted in 2022 and 2023 to investigate the effect of cover crop termination strategies (fallow, standing, and roller crimped) and simulated rainfall volumes (0, 4.2, and 8.3 mm simulated over 20 min; equivalent to 0, 12.5, and 25 mm h -1 ) on atrazine wash off from cereal rye ( Secale cereale L.) biomass onto the soil. The use of roller crimper resulted in an average of 10% greater ground cover relative to the standing cereal rye. Atrazine interception that was bound to rye biomass reached 29 and 94% in 2022 and 2023, respectively. In 2022, the concentration of atrazine in the soil under roller crimped cereal rye was 9% greater than that understanding cereal rye, after 4.2 mm of rainfall. In 2023, when cereal rye biomass more than doubled, only 6% of the applied atrazine was found under roller crimped cereal rye, after 8.3 mm of rainfall. Cereal rye biomass accumulation negatively impacted the amount of atrazine reaching the soil at the time of application. Although the roller crimped cereal rye reduced the amount of herbicide reaching the soil relative to the standing cereal rye, it also reduced atrazine leaching below the 0–5 cm of soil. In cover cropping systems with high levels of cereal rye biomass (e.g., > 7,000 kg ha -1 ), more than 8.3 mm of rain are required to wash most of the atrazine off of the biomass.

The USDA Environmental Quality Incentives Program (EQIP) provides financial assistance to encourage producers to adopt conservation practices. Historically, one of the most common practices is conservation tillage, primarily the use of no-till planting. The objectives of this research were to determine crop residue using remote sensing, an indicator of tillage intensity, without using training data and examine its performance at the field level. The Landsat Thematic Mapper Series platforms can provide global temporal and spatial coverage beginning in the mid-1980s. In this study, we used the Normalized Difference Tillage Index (NDTI), which has proved to be robust and accurate in studies built upon training datasets. We completed 10 years of residue maps for the 150,000 km2 study area in South Dakota, North Dakota, and Minnesota and validated the results against field-level survey data. The overall accuracy was between 64% and 78% with additional improvement when survey points with suspect geolocation and satellite tillage estimates with fewer than four dates of Landsat images were excluded. This study demonstrates that, with Landsat Archive available at no cost, researchers can implement retrospective, untrained estimates of conservation tillage with sufficient accuracy for some applications.

Conservation tillage is a well known best management practice that improves soil quality, reduces runoff and erosion, and increases infiltration. However, a rapid assessment strategy for quantifying the rate and spatial distribution of conservation tillage practices is lacking. This study was designed to evaluate the sensitivity of a remotely derived crop residue cover index for depicting conventional tillage (CT), strip tillage (ST), and no-tillage (NT) systems in a cotton-corn-peanut rotation in the southeastern Coastal Plain. Treatments consisted of CT (rip and bed operation), NT, NT with subsoiling, and ST. Remotely sensed data were acquired three times prior to canopy closure, using a handheld multispectral radiometer (485 to 1,650 nm) and thermal imager (7,000 to 14,000 nm). Using a combination of visible and near-infrared spectra, a crop residue cover index was calculated and evaluated. Results showed that crop residue cover is greatest in years planted with peanut or cotton—likely due to the later winter cover crop termination date compared to years when corn is planted. The crop residue cover index outperformed the thermal infrared, accurately separating conventional from conservation tillage treatments in four out of six data acquisitions in 2004 and 2006. Differentiation among conservation tillage treatments was inconsistent. Regression analyses showed that a strong linear relationship existed between the crop residue cover index and measured crop residue cover ( r 2 = 0.51 to 0.86, alpha = 0.10). These data suggest that remotely sensed data may be used as a rapid, field-scale indicator of conservation tillage adoption. Rapid assessment methodologies are necessary to quantify the impact of conservation practice adoption on water quality/quantity, assess adoption rates, and improve the placement of conservation tillage practices at local, watershed and regional scales.