Industrial hemp (Cannabis sativa L.) holds promise as a crop for more sustainable supply chains given its potential as a source of high-strength fibers, adsorbents, and nutrient-dense feedstuffs. Developing nutrient management guidelines for hemp will be an important part of optimizing the crop’s sustainability attributes. This study measured hemp seed yield in response to N fertilization rate (0, 60, 120, 180, and 240 kg N ha⁻¹). Treatments were tested with four hemp cultivars (‘Joey’ and ‘Grandi’ in 2020, 2021, and 2022 and ‘NWG 2463’ and ‘NWG 4113’ in 2023) in Virginia. Nitrogen input influenced (p ≤ 0.0177) seed yield in all four experimental years, although the pattern of response varied substantially. In 2020, following delayed seeding, hemp showed a weak quadratic (p = 0.0113) response to N inputs, with peak yield (1640 kg ha⁻¹) occurring with 120 kg N ha⁻¹. In 2021, hemp displayed a strong linear (p < 0.0001) response to N inputs, with the highest seed yield (2510 kg ha⁻¹) at 240 kg N ha⁻¹. In 2022, a season characterized by low precipitation and high weed pressure, a weak, linear (p = 0.0111) response to the N rate was observed. The greatest seed yield (380 kg ha⁻¹) was again observed with 240 kg N ha⁻¹. In 2023, weed pressure remained an issue, but the response to N was strong and linear (p < 0.0001), with the greatest seed yield (831 kg ha⁻¹) again measured at 240 kg N ha⁻¹. These findings indicate hemp can be quite responsive to N inputs but that the magnitude of response is sensitive to other factors such as available soil moisture, weed pressure, and growing period.

The use of crop residues for biofuel production has the potential to provide environmental and economic benefits to modern societies. Because of the profound impacts that crop residues have on agricultural productivity and soil health, a sustainable utilization of these residues is required. Thus, we determined crop yield and quality response for a range of biomass retention rates in grain cropping systems. Combinations of corn (Zea mays L.) stover (0, 3.33, 6.66 and 10 Mgha⁻¹) and wheat (Triticum aestivum L.) straw (0, 1.0, 2.0, and 3.0 Mgha⁻¹) were soil applied in a corn-wheat/soybean (Glycine max L. Merr.) rotation in Virginia’s Coastal Plain. Corn stover (0, 3.33, 6.66, 10 and 20 Mg ha⁻¹) was applied in a continuous corn cropping system in the Ridge/Valley province. For each system, residues were applied following grain harvest over two production cycles. Each experiment was conducted as a randomized complete block design with four replications. Two cycles of crop residue management, with retention rates of up to 20 Mg ha⁻¹ of corn stover retention in Blacksburg, and up to 13 Mg ha⁻¹ of corn stover and wheat straw in New Kent, had no effect on total nitrogen (TN) and carbon (TC) concentrations, CN ratios, bulk density (BD), soil pH, field capacity, permanent wilting point, plant available water and water aggregate stability across soil depths and aggregate sizes in Virginia. In one situation when residue management slightly affected BD (0–2.5 cm depth, NK1), differences across the sixteen total retained residues treatments were less than 5%, thus rendering them not biologically or environmentally meaningful. Overall, results of this study did not show any clear short-term impact, resulting from various rates of crop residue retention in Virginia cropping systems. These incipient negative impacts resulting from very low rates of residue return warrant further studies to corroborate whether these results are to be found following long-term scenarios of crop residue management.

Maize (corn) (Zea mays L.) yield is influenced by complex factors, including abiotic and biotic stress and inconsistent nutrient use efficiency, which challenge optimal yield. Standard management recommendations often fall short, prompting interest in intensive management strategies within an Adaptive Maize Management System (ACMS). To investigate this, we employed an addition/omission technique within a randomized complete block design (RCBD) to compare standard maize management recommendations with an intensive management protocol aimed at identifying yield-limiting factors. Our intensive management approach combined early-season biostimulant applications with mid-season supplementation of phosphorus (P), potassium (K), and nitrogen (N) at the V7 stage, followed by foliar fungicides and additional foliar N at the R1 stage. Field trials spanned five Virginia locations over 2022 and 2023 under both irrigated and non-irrigated conditions, yielding ten site-years of data. Analysis via ANOVA in JMP^® Version 18 with Dunnett’s test revealed that the intensive management approach significantly increased grain yield in 3 of 10 experiments. Under non-irrigated conditions, the intensive management practices averaged 5.9% higher yield than the standard management check. We observed a higher response to irrigation in standard management check (34%) than in intensive management check (8.9%). Site-specific irrigation impacts ranged from 14% to 61%. Results emphasize site-specific input recommendations for yield enhancement.

Maize (corn) (Zea mays L.)) yield is influenced by complex factors including abiotic and biotic stress and inconsistent nutrient use effi-ciency that challenge optimal yield. Standard management recommendations often fall short, prompting interest in intensive manage-ment strategies within an Adaptive Corn Management System (ACMS). To investigate this, we employed an addition/omission tech-nique within a randomized complete block design (RCBD) to compare standard maize management recommendations with an inten-sive management protocol aimed at identifying yield-limiting factors. Our intensive management approach combined early-season bi-ostimulant applications with mid-season supplementation of phosphorus (P), potassium (K), and nitrogen (N) at the V7 stage, followed by foliar fungicides and additional foliar N at the R1 stage. Field trials spanned five Virginia locations over 2022 and 2023 under both ir-rigated and non-irrigated conditions, yielding ten site-years of data. Analysis via ANOVA in JMP® Version 18 with Dunnett’s test re-vealed that the intensive management approach significantly increased grain yield in 3 of 10 experiments. Under non-irrigated condi-tions, the intensive management practices averaged 5.9% higher yield than the standard management check. We observed a higher re-sponse to irrigation in standard management check (34%) than in intensive management check (8.9%). Site-specific irrigation impacts ranged from 14% to 61%. Results emphasize site-specific input recommendations for yield enhancement.

Tiller density is indicative of the overall health of winter wheat (Triticum aestivum L.) and is used to determine in-season nitrogen (N) application. If tiller density exceeds 538 tillers per m² at GS 25, then an N application at that stage is not needed, only at GS 30. However, it is often difficult to obtain an accurate representation of tiller density across an entire field. Normalized difference vegetative index (NDVI) and normalized difference red edge (NDRE) have been significantly correlated with tiller density when collected from the ground. With the advent of unmanned aerial vehicles (UAVs) and their ability to collect NDVI and NDRE from the air, this study was established to examine the relationship between NDVI, NDRE, and tiller density, and to verify whether N could be applied based on these two indices. From 2018 to 2020, research trials were established across Virginia to develop a model describing the relationship between aerial NDVI, aerial NDRE, and tiller density counted on the ground, in small plots. In 2021, the model was used to apply N in small plots at two locations, where the obtained grain yield was the same whether N was applied based on tiller density, NDVI, or NDRE. From 2022 to 2023, the model was applied at six locations across the state on large scale growers’ fields to compare the amount of GS 25 N recommended by tiller density, NDVI, and NDRE. At three locations, NDVI and NDRE recommended the same N rates as the tiller density method, while at two locations, NDVI and NDRE recommended less N than tiller density. At one location, NDVI and NDRE recommended more N than tiller density. However, across all six locations, there was no difference in grain yield whether N was applied based on tiller density, NDVI, or NDRE. This study indicated that UAV-based NDVI and NDRE are excellent proxies for tiller density determination, and can be used to accurately and economically apply N at GS 25 in winter wheat production.