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The magnitude of ecosystem services provided by winter cover crops is linked to their performance (i.e., biomass and associated nitrogen content, forage quality, and fractional ground cover), although few studies quantify these characteristics across the landscape. Remote sensing can produce landscape-level assessments of cover crop performance. However, commonly employed optical vegetation indices (VI) saturate, limiting their ability to measure high-biomass cover crops. Contemporary VIs that employ red-edge bands have been shown to be more robust to saturation issues. Additionally, synthetic aperture radar (SAR) data have been effective at estimating crop biophysical characteristics, although this has not been demonstrated on winter cover crops. We assessed the integration of optical (Sentinel-2) and SAR (Sentinel-1) imagery to estimate winter cover crops biomass across 27 fields over three winter–spring seasons (2018–2021) in Maryland. We used log-linear models to predict cover crop biomass as a function of 27 VIs and eight SAR metrics. Our results suggest that the integration of the normalized difference red-edge vegetation index (NDVI_RE1; employing Sentinel-2 bands 5 and 8A), combined with SAR interferometric (InSAR) coherence, best estimated the biomass of cereal grass cover crops. However, these results were season- and species-specific (R2 = 0.74, 0.81, and 0.34; RMSE = 1227, 793, and 776 kg ha−1, for wheat (Triticum aestivum L.), triticale (Triticale hexaploide L.), and cereal rye (Secale cereale), respectively, in spring (March–May)). Compared to the optical-only model, InSAR coherence improved biomass estimations by 4% in wheat, 5% in triticale, and by 11% in cereal rye. Both optical-only and optical-SAR biomass prediction models exhibited saturation occurring at ~1900 kg ha−1; thus, more work is needed to enable accurate biomass estimations past the point of saturation. To address this continued concern, future work could consider the use of weather and climate variables, machine learning models, the integration of proximal sensing and satellite observations, and/or the integration of process-based crop-soil simulation models and remote sensing observations.

Growing cover or winter crops and retaining crop residue on agricultural lands are considered beneficial management practices to address soil health and water quality. Remote sensing is a valuable tool to assess and map crop residue cover and cover crops. The objective of this study is to evaluate the performance of linear spectral unmixing for estimating soil cover in the non-growing season (November–May) over the Canadian Lake Erie Basin using seasonal multitemporal satellite imagery. Soil cover ground measurements and multispectral Landsat-8 imagery were acquired for two areas throughout the 2015–2016 non-growing season. Vertical soil cover photos were collected from up to 40 residue and 30 cover crop fields for each area (e.g., Elgin and Essex sites) when harvest, cloud, and snow conditions permitted. Images and data were reviewed and compiled to represent a complete coverage of the basin for three time periods (post-harvest, pre-planting, and post-planting). The correlations between field measured and satellite imagery estimated soil covers (e.g., residue and green) were evaluated by coefficient of determination (R2) and root mean square error (RMSE). Overall, spectral unmixing of satellite imagery is well suited for estimating soil cover in the non-growing season. Spectral unmixing using three-endmembers (i.e., corn residue-soil-green cover; soybean residue-soil-green cover) showed higher correlations with field measured soil cover than spectral unmixing using two- or four-endmembers. For the nine non-growing season images analyzed, the residue and green cover fractions derived from linear spectral unmixing using corn residue-soil-green cover endmembers were highly correlated with the field-measured data (mean R2 of 0.70 and 0.86, respectively). The results of this study support the use of remote sensing and spectral unmixing techniques for monitoring performance metrics for government initiatives, such as the Canada-Ontario Lake Erie Action Plan, and as input for sustainability indicators that both require knowledge about non-growing season land management over a large area.

Efficient use of nitrogen (N) is essential to protect water quality in high-input organic vegetable production systems, but little is known about the long-term effects of organic management on N mass balances. We measured soil N and tabulated N inputs (organic fertilizers, compost, irrigation water, atmospheric deposition, cover crop seed, vegetable transplant plugs and fixation by legume cover crops) and exports in harvested crops (lettuce, broccoli) over eight years to calculate soil surface and soil system N mass balances for the Salinas Organic Cropping Systems study in Salinas, CA. Our objectives were to 1) quantify the long-term effects of compost, cover crop frequency and cover crop type on soil N, cover crop and vegetable crop N uptake, and yield, and 2) tabulate N balances to assess the effects of these factors on N export in harvested crops, soil N storage and potential N loss. Results show that across all systems only 13 to 23% of N inputs were exported in harvest. Annual compost applications increased soil N stocks but had little effect on vegetable N uptake or yield, increasing the cumulative soil system N balance surplus over eight years by 999 kg ha -1 , relative to the system receiving organic fertilizers alone. Annually planted winter cover crops increased N availability, crop uptake and export; however, biological N fixation by legumes negated the positive effect of increased harvest exports on the balance surplus in the legume-rye cover cropped system. Over eight years, rye cover crops improved system performance and reduced the cumulative N surplus by 384 kg ha -1 relative to the legume-rye mixture by increasing N retention and availability without increasing N inputs. Reduced reliance on external compost inputs and increased use of annually planted non-legume cover crops can improve efficient N use and cropping system yield, consequently improving environmental performance.

Intensified cover-cropping practices are increasingly viewed as a herbicide-resistance management tool but clear distinction between reactive and proactive resistance management performance targets is needed. We evaluated two proactive performance targets for integrating cover-cropping tactics, including (1) facilitation of reduced herbicide inputs and (2) reduced herbicide selection pressure. We conducted corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] field experiments in Pennsylvania and Delaware using synthetic weed seedbanks of horseweed [Conyza canadensis (L.) Cronquist] and smooth pigweed (Amaranthus hybridus L.) to assess winter and summer annual population dynamics, respectively. The effect of alternative cover crops was evaluated across a range of herbicide inputs. Cover crop biomass production ranged from 2,000 to 8,500 kg ha−1 in corn and 3,000 to 5,500 kg ha−1 in soybean. Experimental results demonstrated that herbicide-based tactics were the primary drivers of total weed biomass production, with cover-cropping tactics providing an additive weed-suppression benefit. Substitution of cover crops for PRE or POST herbicide programs did not reduce total weed control levels or cash crop yields but did result in lower net returns due to higher input costs. Cover-cropping tactics significantly reduced C. canadensis populations in three of four cover crop treatments and decreased the number of large rosettes (>7.6-cm diameter) at the time of preplant herbicide exposure. Substitution of cover crops for PRE herbicides resulted in increased selection pressure on POST herbicides, but reduced the number of large individuals (>10 cm) at POST applications. Collectively, our findings suggest that cover crops can reduce the intensity of selection pressure on POST herbicides, but the magnitude of the effect varies based on weed life-history traits. Additional work is needed to describe proactive resistance management concepts and performance targets for integrating cover crops so producers can apply these concepts in site-specific, within-field management practices.

The global increase in food demand in the context of climate change requires a clear understanding of cropland function and of its impact on biogeochemical cycles. However, although gas exchange between croplands and the atmosphere is measurable in the field, it is difficult to quantify at the plot scale over relatively large areas because of the heterogeneous character of landscapes and differences in crop management. However, assessing accurate carbon and water budgets over croplands is essential to promote sustainable agronomic practices and reduce the water demand and the climatic impacts of croplands while maintaining sufficient yields. From this perspective, we developed a crop model, SAFYE-CO2, that assimilates high spatial- and temporal-resolution (HSTR) remote sensing products to estimate daily crop biomass, water and CO2 fluxes, annual yields, and carbon budgets at the parcel level over large areas. This modeling approach was evaluated for sunflower against two in situ datasets. First, the model’s output was compared to data acquired during two cropping seasons at the Auradé integrated carbon observation system (ICOS) instrumented site in southwestern France. The model accurately simulated the daily net CO2 flux (root mean square error (RMSE) = 0.97 gC·m−2·d−1 and determination coefficient (R2) = 0.83) and water flux (RMSE = 0.68 mm·d−1 and R2 = 0.79). The model’s performance was then evaluated against biomass and yield data collected from 80 plots located in southwestern France. The model was able to satisfactorily estimate biomass dynamics and yield (RMSE = 66 and 54 g·m−2, respectively). To investigate the potential application of the proposed approach at a large scale, given that soil properties are important factors affecting the model, a sensitivity analysis of two existing soil products (GlobalSoilMap and SoilGrids) was carried out. Our results show that these products are not sufficiently accurate for inclusion as inputs to the model, which requires more accurate information on soil water retention capacity to assess water fluxes. Additionally, we argue that no water stress should be considered in the crop growth computation since this stress is already present because of remote sensing information in the proposed approach. This study should be considered a first step to fulfill the existing gap in quantifying carbon budgets at the plot scale over large areas and to accurately estimate the effects of management practices, such as the use of cover crops or specific crop rotations on cropland C and water budgets.

Maize is the most widespread and, along with wheat, the most important staple crop in the Republic of Serbia, which is of great significance for ensuring national food security. With the increasing demand for food and forage, intensive agricultural practices have been adopted in the maize production systems. In this direction, considerable research efforts have been made to examine the effects of different types of cover crops as a green manure on maize productivity; however, no consistent conclusions have been reached so far. Therefore, the objective of the present study is to examine the possibility of predicting the effects of winter cover crops (CC) integrated with different management practices on the morphological traits, yield, and yield components of maize. The experiment was carried out on chernozem soil from 2016 to 2020 as a randomized complete block design arranged as a split-split-plot with three replicates. The pea as a sole crop (P) and the mixture of pea and triticale (PT) are sown as winter CC with the following subplots: (i) CC used as green manure, and (ii) CC used as forage and removed before maize sowing. The artificial neural network is used for exploring nonlinear functions of the tested parameters and 13 categorical input variables for modeling according to the following factors: CC, way of using CC, N fertilization, and year. The computed maximums of plant height, number of leaves, number of internodes, plant density, number of ears, grain yield, 1000-grain weight, hectolitre weight, dry matter harvest residue, harvest index, leaves percentage, stems percentage, and ears percentage are as follows: 232.3 cm; 9.7; 10.2; 54,340 plants ha−1; 0.9; 9.8 t ha−1; 272.4 g; 67.0 kg HL−1; 9.2 t ha−1; 0.52; 18.9%; 36.0%, and 45.1%, respectively. The optimal result is obtained with peas used as green manure, with 50 kg N ha−1 and in the climatic conditions of 2018. Consequently, maize production under subsequent sowing periods can be successfully optimized by adapting selected management options for higher yield accomplishment.

Cover crops can offer erosion protection as well as soil and environmental quality benefits. Cereal rye (Secale cereale L.) is the most commonly used winter cover crop in corn–soybean rotations in the upper Midwest of the USA because of its superior winter hardiness and growth at cool temperatures. Cereal rye cover crops, however, can occasionally have negative impacts on the yield of a following corn crop, which discourages broader adoption and introduces substantial risk for corn farmers employing cover crops. We hypothesized that because cereal rye shares some pathogens with corn, it may be causing increased disease in corn seedlings planted soon after cereal rye termination. To test this, we performed a series of experiments in a controlled environment chamber to assess the response of corn seedlings with and without a commercial fungicide seed treatment to the presence of cereal rye or other species of cover crops that were terminated with herbicide prior to corn planting. Our results indicate that under cool and wet conditions, cereal rye reduces corn seedling growth performance and increases incidence of corn seedling root disease. Fungicide seed treatment had limited efficacy in preventing these effects, perhaps because environmental conditions were set to be very conducive for disease development. However, hairy vetch (Vicia villosa Roth) and winter canola (Brassica napus L.) cover crops had fewer negative impacts on corn seedlings compared with cereal rye. Thus, to expand the practice of cover cropping before corn, it should become a research priority to develop alternative management practices to reduce the risk of corn seedling root infection following cereal rye cover crops. Over the longer term, testing, selection and breeding efforts should identify potential cover crop species or genotypes that are able to match the winter hardiness, growth at cool temperatures and the conservation and environmental quality benefits of cereal rye, while avoiding the potential for negative impacts on corn seedlings when environmental conditions are suitable for disease development.

Winter cropping can be used to achieve a double benefit for producers: as soil cover and an additional economic crop cycle. Flax (Linum usitatissimum L.) is a spring crop growing in the northern region of the US and used as a fall cover crop in some southern states. In this study, eight seed-type flax varieties were evaluated for production as a fall/winter crop for the Commonwealth of Virginia, a mid-latitude region. Mixed results were obtained; however, the crop showed winter tolerance and potential productivity, especially when the frequency of sub-zero winter temperatures was low. Planting too early in the fall allows for significant stem development that increases susceptibility to physical damage by snowstorms and winter frost. Seed yield was low compared to spring-planted crops; however, it reached up to 400 kg ha−1 in some varieties. Seed weights were comparable to those found elsewhere for the same or other varieties, and seed protein and crude fat content ranged from 228–270 and 189–234 g kg−1, respectively. Across years and varieties, P, K, Mg, Ca, and S averaged 7.74, 9.88, 3.88, 2.86, and 2.35 g kg−1, respectively. Mineral elements Fe, Zn, Mn, Cu, and B averaged 95, 62, 21, and 10 mg kg−1, respectively. However, early maturity in spring ahead of other grains subjected it to significant losses to wild birds. Fall-planted flax has potential as a cover crop and may be harvested for seed, which in addition to a summer crop, provides a producer with economic returns from two crop cycles per year.

Harvesting annual winter cereal rye (Secale cereale L.) (WCR) as an emergency forage could offset cover crop establishment costs, facilitate WCR adoption, and still provide multiple ecosystem benefits. A five site‐year trial was conducted in Colorado (CO) and Illinois (IL) to evaluate the effect of harvest date on WCR forage yield, quality, and its economic performance. From March to April, WCR dry matter (DM) yield increased exponentially in CO and linearly in IL. The DM yield at Julian day (DOY) 112–116 in CO was 6.9, 5.0, and 5.2 Mg ha–1 in 2018, 2019, and 2020, respectively, compared with 4.7 and 2.7 Mg ha–1 in IL in 2019 and 2020, respectively. Delayed harvesting increased acid detergent fiber and neutral detergent fiber concentrations and decreased crude, total digestible nutrients, and relative feed quality. Yield–quality trade‐off showed that forage yield increased rapidly but forage quality declined after DOY 105–108. Economic analysis, including cost of nutrient removal and 10% cash crop (corn [Zea mays L.]) yield penalty following WCR production revealed harvesting WCR biomass as forage was economically feasible in four out of five site‐years at hay price over$132 Mg–1. Eliminating corn yield penalty indicated profitability in four site‐years at hay price of ≥$ 110 Mg–1 and removing nutrient removal costs made all site‐years profitable at hay price of ≥$110 Mg–1. It was concluded that harvesting WCR biomass can be a profitable and effective strategy for sustainable intensification that can offer environmental stewardship and economic benefit. Core Ideas Rye cover crop biomass can be harvested to improve farm profit and increase its adoption. Dry matter yield at Julian day 112–116 ranged from 5.0 to 6.9 Mg ha−1 in CO and 2.7 to 4.7 Mg ha−1 in IL. Forage quality declined rapidly after Julian day 105–108. The economic analysis showed that harvesting rye cover crop biomass is economically feasible. Dual‐use of rye cover crop can be an effective strategy for sustainable intensification.

The widely used SAIL (Scattering by Arbitrarily Inclined Leaves) radiative transfer model (RTM) is designed for canopies that can be considered as homogeneous turbid media and thus should be inadequate for row canopies. However, numerous studies have employed the SAIL model for row crops (e.g., wheat and maize) to simulate canopy reflectance or retrieve vegetation properties with satisfactory accuracy. One crucial reason may be that under certain conditions, a row crop canopy can be considered as a turbid medium, fulfilling the assumption of the SAIL model. Yet, a comprehensive analysis about the performance of SAIL in row canopies under various conditions is currently absent. In this study, we employed field datasets of wheat canopies and synthetic datasets of wheat and maize canopies to explore the impacts of the vegetation cover fraction (fCover), solar angle and soil background on the performance of SAIL in row crops. In the numerical experiments, the LESS 3D RTM was used as a reference to evaluate the performance of SAIL for various scenarios. The results show that the fCover is the most significant factor, and the row canopy with a high fCover has a low soil background influence. For a non-black soil background, both the field measurement and simulation datasets showed that the SAIL model accuracy initially decreased, and then increased with an increasing fCover, with the most significant errors occurring when the fCover was between about 0.4 and 0.7. As for the solar angles, the accuracy of synthetic wheat canopy will be higher with a larger SZA (solar zenith angle), but that of a synthetic maize canopy is little affected by the SZA. The accuracy of the SAA (solar azimuth angle) in an across-row direction is always higher than that in an along-row direction. Additionally, when the SZA ranges from 65° to 75° and the fCover of wheat canopies are greater than 0.6, SAIL can simulate the canopy reflectance with satisfactory accuracy (rRMSE < 10%); the same accuracy can be achieved in maize canopies as long as the fCover is greater than 0.8. These findings provide insight into the applicability of SAIL in row crops and support the use of SAIL in row canopies under certain conditions (with rRMSE < 10%).