Improving the management of cereal rye (Secale cereale L.), the most frequently used cover crop in the United States, provides an opportunity to enhance agroecosystem service provisioning. Services such as erosion control, weed suppression, and nitrate leaching mitigation are correlated with cereal rye ground cover and biomass, which decline as sowing dates are delayed. Our objective was to quantify whether increasing cereal rye seeding rates could compensate for lost growing degree days as sowing is delayed across the Northeastern United States, and if early spring ground cover could predict late spring biomass and guide grower decision making. We established a two-factor experiment across 13 site-years to test the effects of sowing date, relative to the historic first frost date of each location, and seeding rate (0, 17, 34, 67, 101, and 135 kg ha-1) on cereal rye productivity and weed suppression. Delaying sowing from 2 weeks before to 2 weeks after the historic first frost date decreased cereal rye ground cover by 57%, biomass by 44%. Increasing seeding rates could not fully compensate for fewer growing degree days. Increasing seeding rates up to 60 kg ha-1 maximized ground cover, biomass, and weed suppression at most site-years. Ground cover in early spring was correlated with biomass at anthesis, independent of sowing date, indicating utility as a decision support tool for growers. Overall, our results suggest that seeding rates up to 60 kg ha-1, which is lower than that existing regional recommendations, balance seed costs with ecosystem service provisioning potential in the Northeastern United States.

This chapter reviews the consumer demand for hemp-based products. It provides an overview of the process that involved the legalisation of hemp products which in turn, led to the need to understand potential consumer demand for these products. The chapter also includes a case study to support the need to meet these demands whilst also ensuring that consumers can trust the products whilst also highlighting the products’ safety and sustainability.

Increasing terrestrial carbon storage can reduce atmospheric carbon dioxide levels as a climate mitigation strategy. Agricultural soil offers potential for persistent soil carbon sequestration. Mineral-associated organic carbon (MAOC) is generally more persistent than particulate organic carbon (POC), but it is unclear how much influence soil health and management can have on MAOC relative to climate and edaphic conditions. Soil samples from 196 agricultural fields on 77 farms throughout Vermont were fractionated by size to understand how these factors affect the amount and proportions of POC and MAOC. Our results reveal a significant effect of crop type, climate, soil texture, and aggregate stability on these carbon fractions. Hay and pasture fields had on average 69

Cover cropping provides ecosystem services to farms and communities. One ecosystem service often overlooked is the material output of forage that is possible from cover crops. Cover crops can provide high yield and quality feed for a variety of livestock types. In many cases farmers may generate economic return from harvesting cover crops but in these cases the cover crop must be treated more like a cash crop. Attention should be paid to species/variety selection, planting dates, and fertility to achieve forage and the expected conservation goals expected from the cover crop. This chapter focuses on cover crop production practices that should be considered if a cover crop is to be harvested as forage. Both stored feed and grazing systems are reviewed, and examples of farm adoption highlighted.

Accurate and high spatiotemporal resolution soil moisture (SM) monitoring in cropland is important for water resource management, drought forecasting, and nutrient transport estimation at the field scale for sustainable crop production. Although recent research has applied machine learning (ML) to downscale coarse-resolution satellite SM products, most of this past work has focused only on surface SM estimation, and the performance of rootzone SM products has not been intensively evaluated in cropland. This study introduces a novel framework that integrates multi-source satellite-based ML models with the Layered Green and Ampt Infiltration with Redistribution (LGAR) model to produce high-resolution (100 m, hourly) SM products for both the surface layer (0-5 cm) and rootzone (0-100 cm) across cropland in the contiguous United States (CONUS). First, six ML models were trained using multiple high-resolution remote sensing datasets (Sentinel-1, Sentinel-2, and Landsat) to predict surface and rootzone SM. These ML predictions were then assimilated into the LGAR model using the ensemble Kalman filter (EnKF). The framework was developed and validated using an eight-fold cross-validation scheme with in-situ data from 431 cropland sites across CONUS, sourced from three networks (SCAN, USCRN, and PSA). The 100-m hourly SM data from this framework surpasses existing products (9-km SMAP L4, SMAPbased 1-km thermal hydraulic disaggregation of SM product) in spatial and temporal resolution and captures rootzone SM that is not available in the SMAP-HydroBlocks SM product. It achieves good performance, with median bias-corrected root mean squared error (ubRMSE) of 0.053 m3/m3 and median Kling-Gupta efficiency (KGE) of 0.379 in the surface layer, and median ubRMSE of 0.027 m3/m3 and median KGE of 0.302 in the rootzone. While the framework demonstrates strong performance, its accuracy varies across climatic regimes, with surface SM performing better in non-humid areas (median KGE = 0.375 versus median KGE = 0.416) and rootzone SM in humid regions (median KGE = 0.313 versus median KGE = 0.127). This high-resolution cropland SM product can potentially benefit multiple agricultural applications, such as irrigation management and nutrient leaching estimation, and provide valuable insights to support farmers and land managers in decision-making processes.

To maximize the multiple agroecosystem services that cover crops provide, farmers and their advisers need detailed information. Assorted groups have sought to distill the large amount of information pertaining to cover crop management into decision support tools to provide recommendations. However, such tools have suffered from concerns including a lack of site-specificity, failure to integrate existing data across agricultural systems and scales, and suboptimal user interface design. This chapter reports on tools developed by the Precision Sustainable Agriculture (PSA) network that are site-specific, relevant across the US, and user-friendly. We provide examples of a cover crop species selector and a cover crop nitrogen calculator. Future challenges relate to the amount of data held within tools, the need for regular updates, and the difficulty in modeling interactions between cash and cover crop genetics, the environment, and management to provide the best possible recommendations.

Context Brassica, grass, and legume mixtures can produce high-quality forage but management remains unclear because of the variation among of brassica cultivars.Aim This experiment assessed the nutritive value and herbage accumulation of fall-grown brassica, oat (Avena sativa L.), and pea (Pisum sativum L.) mixtures.Methods A 2-year, four-replicate RCBD (randomized complete block design) field experiment was conducted with six brassica cultivars planted with oats and peas. Mixes with each brassica cultivar were planted at five seeding rates. Forage was harvested twice, approximately 2 and 3 months after planting.Key results Turnips (Brassica rapa L.), colza (Brassica napus L.), radish (Raphanus sativus L.), and interspecies forage hybrid (Brassica rapa & times; napus) remained at vegetative growth stage throughout the experiment, and had small differences in nutritive value. When these brassicas were seeded at 1.7-3.4 kg ha-1 with 50-75 kg ha-1 oats and 34-50 kg ha-1 peas, they produced an average of 2620 kg dry matter ha-1 with crude protein (CP) of 22.6%, total digestible nutrients (TDN) 64.3%, and neutral detergent fiber (NDF) of 36.2%. Conversely, flowering mustard (Brassica juncea (L.) Czern.) saw declines in nutritive value at the later harvest and performed best when seeded at 5.0 kg ha-1 in mixtures (herbage accumulation: 3654 kg dry matter ha-1; CP: 19.7%; TDN: 63.0%; NDF: 39.5%).Conclusions Growth habit of brassicas affects on how cultivars should be managed in forage mixtures.Implications Brassica seeding rates in mixtures should be based on variety growth habit and harvest timing.

Peas (Pisum sp.) are widely used in crop rotations because they increase soil fertility through nitrogen fixation. Previous work indicates that pea genotypes differ in the benefit they provide to subsequently planted corn, with wild accessions displaying greater rotational value due to the recruitment of beneficial soil microbes. We further investigate the source of variation in rotational value, focusing on changes in the soil microbiome and soil chemical properties across 108 genotypes from the USDA Pea Single Plant Plus Mini-Core. Four replicates of each pea genotype were grown in a greenhouse for four weeks, when they were uprooted to determine soil microbial communities and soil chemical properties. Corn was then grown to maturity in the same pots with the same soil to measure the yield of subsequently planted corn, and ultimately the rotational value of each genotype. A genome-wide association study (GWAS) was performed to determine the genetic basis of rotational value. While no variation was observed in rotational value, we identified genotypes that enrich a potential plant growth promoting bacteria in the genus Pseudarthrobacter. The enrichment of this genus was associated with a single SNP. Crops are rarely bred with their impact on the subsequently planted crop in mind, despite this being an important pre-requisite for increasing adoption of cover cropping and crop rotation. We have identified pea accessions and traits contained within the USDA Pea Single Plant Plus Mini-Core that may be useful in future breeding endeavors focused on increasing crop rotational value.

Understanding and accurately predicting soil organic carbon (SOC) stocks in agricultural lands play a vital role in mitigating climate and sustainable land management. However, existing studies often lack high-resolution SOC stock maps at regional scales, limiting their applicability for site-specific land management. This research provides a novel and comprehensive framework for generating high-resolution (10-m) SOC stock maps of agricultural lands in Vermont, USA-one of the first efforts of its kind in a temperate, data-scarce region-using digital soil mapping (DSM). We compiled 361 topsoil samples (0 to 30 cm depth) and then applied Cubist, kNN (k-Nearest Neighbors), and RF (Random Forest) machine learning (ML) algorithms to predict SOC stocks (t ha-1) using environmental variables including climate, terrain, remote sensing, and soil characteristics. Prior to modeling, we used the Boruta algorithm to identify significant variables for SOC stock. Model performance was assessed using cross-validation (70% training and 30% validation). Validation parameters indicated that the RF ML algorithm outperformed others in predictive accuracy. Dynamic variables, including climate and biota, were the most influential variables in defining SOC distribution, while static variables, like terrain attributes and soil properties, were less influential. Spatial prediction maps revealed high SOC stocks in the northeastern part of Vermont, where there is high precipitation and elevation. The study also includes novel spatial uncertainty quantification across different land use types, offering practical insights into prediction confidence and C incentive targeting. Uncertainty in SOC stock prediction accuracy ranged from approximately 3.35 % to 3.63 %, with mean SOC stock values of 94.3 +/- 3.42 (t ha-1) for crops, 100.0 +/- 3.35 (t ha-1) for hay, and 96.1 +/- 3.30 (t ha-1) for pasture. Our findings provide a foundational SOC stock map for Vermont's agricultural lands, revealing key spatial distribution and drivers. The strong influence of climate variables suggests that adaptation strategies should account for regional climatic conditions, and incentives for soil C sequestration should be location specific. This research enhances the capacity to make soil carbon-informed agricultural management decisions aimed at mitigating the impacts of climate change, while also highlighting the need for further research to overcome the limitations of current SOC mapping approaches in Vermont and similar temperate agricultural regions worldwide.

Industrial hemp (Cannabis sativa L.) is an ancient crop used throughout history for fiber, oilseed, and therapeutic compounds. Hemp varieties were cultivated across diverse environments in the United States, but knowledge of those agronomic practices along with genetic resources was lost during a period in which cultivation of cannabis was prohibited. Therefore, regional performance evaluations of hemp varieties for crop performance coupled with scientific communication of outcomes to the public are crucial for hemp's development as an agricultural commodity. Objectives for this research were to evaluate relative yields of industrial hemp varieties grown across the United States and link their suitability for commercial production across locations. A national collaboration established variety trials containing seven industrial hemp varieties planted across 14 locations (36 degrees-48 degrees N latitude and 72 degrees-110 degrees W longitude) over a 3-year period. Crop dry straw yield and seed yield increased from the averages of 1600 and 700 kg ha-1 in Year 1 to 2400 and 1150 kg ha-1 in Year 2, and 3050 and 815 kg ha-1 in Year 3, respectively. The varieties Anka and X-59 performed best in Vermont and Virginia, where seed yields consistently exceeded 1100 kg ha-1; however, no single variety performed above average across all sites. Overall, this assessment identified two industrial hemp varieties suitable for commercial production in specific sites and highlighted the importance for hemp breeders to investigate variety x location x year interactions when developing improved varieties to best capture site-specific productivity.

The industrialization and commodification of grain production has had major environmental, health, and economic implications. Pushing back against this commodity system, grain value chains are emerging in the form of collaborations between farmers, millers, bakers, maltsters, and brewers. These partnerships are part of a broader movement toward the development of values-based supply chains in the food system, in which business partners establish long-term, strategic partnerships based on shared values like fairness, commitment to community, and environmental sustainability. In these arrangements, farmers capture a larger share of the food dollar than in commodity supply chains and are treated as valued partners rather than interchangeable suppliers. Despite the presence of localized grain value chains throughout the U.S., little research exists on their development or functioning. This study examines the nature of partnerships in grain value chains in the Northeast, where food-grade grain production is particularly challenging but nonetheless present. We present a multiple-case study of three established grain value chains-Maine Grains, Farmer Ground Flour, and Valley Malt-that examines the nature of their partnerships and the strategies they employ to navigate challenges in their values-based supply chains. The findings from this study, which are drawn from 41 in-depth interviews with grain growers, processors, end-users, and other key informants, demonstrate that developing committed, trusting, and interdependent partnerships that value one another's success is key to the functioning of these grain value chains.

Food system sustainability, and ways of measuring it, are widely explored and discussed in academic literature.Measurement efforts are challenging because food systems are inherently complex and multifaceted, spanning diverse components, indus-tries, sectors, and scales.Several systems of indica-tors and metrics have been proposed to measure sustainability;however,most existing research focuses either on narrow scales (e.g.,farm level or within a single supply chain), expansive scales that can gloss over complexity (e.g.,national or global assessments), or limited scopes (e.g.,only consider-ing environmental factors).A gap in the literature is a holistic local or regional approach to food sys-tem sustainability that integrates components across the system at a regional scale.In this reflec-tive essay,we describe our development of a framework to measure and track sustainability in such systems.We use a tiered framework that includes five sustainability dimensions and a system of indices, indicators, and metrics that allows for the measurement of important food system charac-teristics in a feasible and reproducible way.We employ a collaborative, transdisciplinary, facilitated team science process to first propose, and then refine,a sustainability assessment framework, using the U.S. state of Vermont as a case study.This paper details our processand progress, as well as reflections on challenges and recommendations for other team scientists.We further propose a plan to implement the framework, collect data, and engage with community members.The experiences and findings described here serve as a foundation for our own team's continued work, as well as a springboard for other similar research efforts.

While there is very limited information on the cost of production (COP) for the emerging 100% grass-fed organic dairy sector, this study (1) estimates the COP using primary data collected from on-farm surveys, (2) assesses the correlation between COP and key production and management factors, (3) examines how land, feed and labor efficiency, and production scale affect the COP, and (4) derives recommendations for enhancing the economic efficiency of grass-fed organic dairy farms. Data collected via annual surveys in the Northeastern United States from 2019 to 2022 were analyzed through descriptive statistics, correlation analysis, hypothesis tests, and regression analysis. At an average cost of USD 45.91 per hundredweight equivalent of milk, the marginal impacts of the cows managed per full time equivalent labor and milk sold per cow on the COP were −USD 0.166 and −USD 0.003, respectively. Conversely, the COP increased by USD 1.44 when the crop acres per cow increased by one unit, and the COP of small farms with less than 45 cows was USD 6.20 higher than other farms. As farms are significantly different in resource endowment and other factors, the strategies for reducing the COP and improving the economic returns should be identified for individual farms. However, our analyses highlight the importance of enhancing labor efficiency in forage production, land management, milking and feeding, improving herd management and optimizing nutrition and dry matter intake to support high milk productivity. This study may help existing grass-fed dairy farms improve their farm management and reduce COP and help prospective farms assess their suitability for transitioning to grass-fed operation.

The concept of soil health has potential to catalyze agricultural transformation, though the breadth of the concept may stifle action. The impact of the soil health concept on practice depends on how well the concept is understood by diverse agricultural practitioners, including farmers, extension, and researchers. We use two surveys of soil health practitioners, or those that manage or influence soil, to examine soil health preferences and beliefs. Both surveys are from Vermont, USA, a region consisting mostly of small-to-medium scale farms: survey one queried Vermont soil health practitioners in the fall of 2020 (n = 62) and survey two queried just Vermont farmers in the spring of 2022 (n = 179). Analysis included qualitative coding and statistical analyses, including t-tests, ANOVA and information theory-informed regression analysis. In study one, Vermont practitioners' definitions include the holistic dimensions of soil health as a living ecosystem, the underlying conditions for life to thrive, the production of ecosystem services, and enhancing resilience. Additionally, practitioners rate biological, chemical, and physical indicators as very useful and important, and these ratings do not, in general, vary between decision contexts. In study two, Vermont farmers perceive the benefits of soil health. The importance of soil health is best predicted by beliefs in climate change. Together these studies suggest that in Vermont, the concept of soil health is aligned with systems-oriented thinking about resilient agricultural systems. We conclude that systems thinking is an important factor for improving soil health and practice adoption.

Cover crops play a significant role in improving and maintaining good soil quality. However, there are often some agronomic and cost challenges associated with successfully establishing cover crops. In Northeastern regions of the United States, abiotic stressors such as cold affect and high costs limit uptake of the practice. Using two field trials, a high tunnel study, and laboratory methods, we investigated the possibility of growing improved winter peas (Pisum sativum L.) as a cash cover crop in Northeastern regions of the United States. Results of the field trial showed no significant variance in winter survival between the winter pea genotypes tested. The genotypes tested include cold-hardy cultivars traditionally cultivated for forage and improved winter pea breeding lines selected for edible traits. In two field trial seasons of 2021/2022 and 2022/2023, all genotypes reached their reproductive stage in the first week of June when seeded the previous year around the end of September in Vermont. Our results show that although peas are a viable overwinter crop allowing potential double cropping. However, the mid-June maturity date for dry or fresh pea harvest conflicts with spring planting of cash crops on many Vermont and Northeastern farms, greatly limiting the potential of double cropping to increase winter cover cropping uptake. Consequently, some reported barriers to winter cover crop adoption in the far Northeast, such as high seed cost and time constraints, cannot easily be solved by double cropping.

Small grains provide agronomic benefits that are critical to the success of organic production, and opportunities within local food movements create expanded markets for small grains. However, diversifying rotations with small grains can present challenges related to production, infrastructure, and markets. Here, we draw upon over two decades of integrated research and Extension efforts to support organic small grain production in the Upper Midwest, Northeast, and other regions of the United States where these crops are underutilized. Lessons learned have led to the development of guiding principles for a systems-level approach to support regional organic small grain production. Forming innovative partnerships between farmers, researchers, and end users is critical. This enables research, production, and markets to adjust to local needs, adapt to available infrastructure, and foster local grain economies. The key research challenges that lie ahead are also discussed, especially adapting organic grain production practices to regional conditions and changing climates. The systems-level approach to organic small grain research highlighted here will increase the success and resilience of organic farms across the United States and expand the adoption of organic small grain production. Local food movements are creating markets for small grains that provide opportunities for organic farmers. Growing small grains provides agronomic benefits that are critical to successful organic field crop production. Production challenges include lack of local infrastructure, adapting to local climate, and local market demands. A systems-level approach of regional partnerships between farmers, researchers, and end users enables farm success. Research must continue to adapt organic grain production practices to regional conditions and changing climates.

Food system sustainability, and ways of measuring it, are widely explored and discussed in academic literature. Measurement efforts are challenging because food systems are inherently complex and multifaceted, spanning diverse components, indus­tries, sectors, and scales. Several systems of indica­tors and metrics have been proposed to measure sustainability; however, most existing research focuses either on narrow scales (e.g., farm level or within a single supply chain), expansive scales that can gloss over complexity (e.g., national or global assessments), or limited scopes (e.g., only consider­ing environmental factors). A gap in the literature is a holistic local or regional approach to food sys­tem sustainability that integrates components across the system at a regional scale. In this reflec­tive essay, we describe our development of a framework to measure and track sustainability in such systems. We use a tiered framework that includes five sustainability dimensions and a system of indices, indicators, and metrics that allows for the measurement of important food system charac­teristics in a feasible and reproducible way. We employ a collaborative, transdisciplinary, facilitated team science process to first propose, and then refine, a sustainability assessment framework, using the U.S. state of Vermont as a case study. This paper details our process and progress, as well as reflections on challenges and recommendations for other team scientists. We further propose a plan to implement the framework, collect data, and engage with community members. The experiences and findings described here serve as a foundation for our own team’s continued work, as well as a springboard for other similar research efforts.

The viability of organic dairy operations in the United States (US) relies on forage production. The objectives of this study were to (1) assess producer and farm information regarding current forage production practices and producer knowledge gaps and (2) identify forage research and educational needs of organic dairy producers across the US. A survey was distributed to 643 organic dairy producers across the US, with 165 respondents (26% response rate). A focus group consisting of extension professionals, university researchers and staff, consultants, dairy industry representatives and organic dairy producers was also consulted for forage research needs. Results showed that approximately half (51%) of surveyed producers were somewhat satisfied with their forage production systems and sometimes experienced negative weather-related impacts on forage yield and quality. A majority (64%) of producers felt their knowledge to meet farm goals was adequate but they reported a lack of resources to implement this knowledge especially for balancing high-forage diets and selecting soil amendments. This study revealed that 54% of producers rely on peer experiences as information resources to make decisions on forage programs. Producer knowledge gaps included pasture renovation with reduced or no-tillage, forage mixtures that match their needs, and forage management practices aiming for high-quality forage. Based on the survey and focus group findings, forage research and educational activities should foster climate change resilience regarding forage diversity adapted to local and regional climatic conditions, improve forage quality, enhance economic returns from soil fertility amendments and pasture renovation, and introduce new forages and forage mixtures that suit economical, agronomical, and environmental needs.

Winter cover crop performance metrics (i.e., vegetative biomass quantity and quality) affect ecosystem services provisions, but they vary widely due to differences in agronomic practices, soil properties, and climate. Cereal rye (Secale cereale) is the most common winter cover crop in the United States due to its winter hardiness, low seed cost, and high biomass production. We compiled data on cereal rye winter cover crop performance metrics, agronomic practices, and soil properties across the eastern half of the United States. The dataset includes a total of 5,695 cereal rye biomass observations across 208 site-years between 2001–2022 and encompasses a wide range of agronomic, soils, and climate conditions. Cereal rye biomass values had a mean of 3,428 kg ha−1, a median of 2,458 kg ha−1, and a standard deviation of 3,163 kg ha−1. The data can be used for empirical analyses, to calibrate, validate, and evaluate process-based models, and to develop decision support tools for management and policy decisions.