Search

Manure is an important source of plant nutrients and organic matter that can benefit soil health over time. However, indiscriminate use of manure can lead to environmental problems including eutrophication of water bodies caused by nutrient runoff during precipitation events. There is a need to understand the effect of manure source and application rate on nitrogen (N) and phosphorus (P) forms and their losses during runoff events. In this study, we assessed the impact of application rate of broiler litter (BL) and swine liquid manure (SLM) on runoff volume, soil, N, and P losses using a rainfall simulator and three consecutive 2.45 cm rainfall events. Surface Decatur silty clay loam (0–0.06 m) was collected and packed in trays (0.55 x 0.30 × 0.06 m3). Manure was surface-applied to the soil at equivalent P rates ranging from 62 to 249 kg ha−1 for BL and 5–18 kg ha−1 for SLM, respectively. Rainfall events took place 7, 14, and 21 d after manure application. Results indicated that the runoff volume decreased at the highest manure application rate compared to the lowest manure application rate. The total suspended solids (TSS) loss was lower in control compared to BL and SLM treatments. The loss of nitrate-N (NO3-N) dissolved reactive P (DRP) and dissolved organic P (DOP) in runoff water was maximum at the highest application rates for both the BL and SLM with respect to control. Mehlich 3 extractable P (M3P) and water soluble P (WSP) increased with increasing P application rates for both manure types in soils following post rainfall simulation study. An increase in M3P and WSP, along with a decrease in soil P storage capacity following post-rainfall simulation for higher P application rates, indicates caution should be taken for considering the manure application rate to prevent environmental nutrient loss during runoff events.

Corn (Zea mays L.) silage is an important feed ingredient in dairy cow diets in New York (NY). Improving corn nitrogen (N) management will help increase farm profitability while reducing environmental impacts from N losses. The objectives of this study were to (1) characterize field-based N balances and other N use indicators for corn silage, and (2) describe major contributors to high balances and inefficiencies as a first step to understand potential opportunities to improve N management. Field-level N balances (N supply – N uptake) and associated N use indicators were derived for 994 field observations across eight NY dairy farms and 5 years. Available and total N balances per ha, which differed only in the fraction of manure N accounted for (plant-available N or total N, respectively), yield-scaled N balances, and N uptake/N supply were calculated. The median balance across all fields was 111 kg N ha−1 for available N and 245 kg N ha−1 for total N. Median yield-scaled available and total N balances were 2.7 and 6.0 kg N Mg−1, respectively. Median N uptake/N supply was 0.60 for available N and 0.41 for total N. Differences in N use indicators were larger among farms than among years within a farm. The amount of N supply greatly influenced N use indicators, manure N supply explaining the largest portion of the variability. At the whole-farm level, balances per ha were positively related to farm’s animal density and impacted by farm crop rotations and within-farm allocation of manure N. We conclude that farms have opportunities to improve upon N management for corn by adjusting N supply based on realistically attainable yield, fully crediting manure and sod N contributions, improving manure inorganic N utilization efficiency, optimizing animal density, and/or exporting manure. Future work is needed to identify feasible ranges for field-level N balances and incentivize the implementation of this assessment through adaptive nutrient management policies.

Abstract 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.

Corn (Zea mays L.) silage yield monitor data offer crucial insights into spatial and temporal yield variability. However, equipment’s sensor malfunctioning can result in data loss, and yield sensor systems are expensive to purchase and maintain. In this study, we analyzed corn silage yield data from two fields and three years each for two dairy farms (Farm A and B). We aimed to explore the potential of integrating high-resolution satellite data, topography, and climate data with machine learning models to estimate missing yield data for a field or a year. Our objectives were to identify key yield-explaining features and assess the accuracy of different machine learning models in estimating silage yield. Results showed that the features differed among farms with a Two-Band Enhanced Vegetation Index, EVI2 (Farm A), and elevation (Farm B) emerging as the most prominent predictors. Ensemble-based models like XGBoost, Random Forest, and Extra Tree regressors exhibited superior predictive performance. However, XGBoost performed poorly when applied to unseen fields or years, whereas Extra Tree regressor, followed closely by Random Forest, emerged as a more reliable model for predicting missing data. Despite achieving reasonable accuracy, the best performance for estimating data for a missing field (6.46 Mg/ha) and year (5.51 Mg/ha) fell short of the acceptable error threshold of 4.9 Mg/ha currently used in state policy to evaluate if a management change resulted in a yield increase. These findings emphasize the need for higher-resolution data and extended years of yield records to better capture the trends in farm-scale yield applications.

New York (NY) state is one of five states that collectively produce >50% of the annual milk supply within the United States. The local environment allows farmers to integrate crop and livestock systems, facilitating cycling of manure nutrients back to cropland. Thus, dairy provides NY with benefits, opportunities, and challenges in terms of environmental sustainability, climate resiliency, and a circular agricultural economy. Improved balancing of crop nitrogen (N) and phosphorus (P) needs and supply with manure and fertilizer, is key for a circular agricultural economy. The objectives of this study were to calculate and evaluate (1) regional and county level N and P balances of harvested cropland; and (2) contribution of manure to a circular agricultural economy for NY. Nutrient balances were calculated for 2017 (most recent Census year) as the difference in the amount of nutrient input through purchased fertilizer and recoverable manure, and the amount removed in harvested crops. Atmospheric N deposition and legume N fixation, and manure nutrient losses during collection, transfer, storage, and treatment were estimated too. The 2017 P balance was 10 kg P ha−1 (9 lb. P ac−1). The N balance was between 39 kg N ha−1 (35 lb. N ac−1) and 95 kg N ha−1 (85 lb. N ac−1), depending on the proportion of legume cropland assumed to receive manure. Redistribution and application of manure to meet P-removal on non-legume cropland only leaves a surplus of 3 kg P ha−1 (3 lb. P ac−1). Applying surplus manure to legume and non-legume cropland results in a slight, state-level, P deficit. In both scenarios, the large N deficit that cannot be met through legume N fixation alone indicates purchased fertilizer is required. These results show NY’s ability to capitalize on the value of manure without excessive risk to the environment. Greater circularity of agricultural production in NY will need to consider benefits and trade-offs of applying manure to legume cropland. As milk production increases in the state, implementation of advanced manure treatment, storage, and manure application and livestock feeding practices will be needed to increase the circularity and sustainability of the agricultural sector in NY.

The accurate and rapid estimation of canopy nitrogen content (CNC) in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture. However, the determination of CNC from field sampling data for leaf area index (LAI), canopy photosynthetic pigments (CPP; including chlorophyll a, chlorophyll b and carotenoids) and leaf nitrogen concentration (LNC) can be time-consuming and costly. Here we evaluated the use of high-precision unmanned aerial vehicle (UAV) multispectral imagery for estimating the LAI, CPP and CNC of winter wheat over the whole growth period. A total of 23 spectral features (SFs; five original spectrum bands, 17 vegetation indices and the gray scale of the RGB image) and eight texture features (TFs; contrast, entropy, variance, mean, homogeneity, dissimilarity, second moment, and correlation) were selected as inputs for the models. Six machine learning methods, i.e., multiple stepwise regression (MSR), support vector regression (SVR), gradient boosting decision tree (GBDT), Gaussian process regression (GPR), back propagation neural network (BPNN) and radial basis function neural network (RBFNN), were compared for the retrieval of winter wheat LAI, CPP and CNC values, and a double-layer model was proposed for estimating CNC based on LAI and CPP. The results showed that the inversion of winter wheat LAI, CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs. The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI, CPP and CNC. The proposed double-layer models (R2=0.67–0.89, RMSE=13.63–23.71 mg g–1, MAE=10.75–17.59 mg g–1) performed better than the direct inversion models (R2=0.61–0.80, RMSE=18.01–25.12 mg g–1, MAE=12.96–18.88 mg g–1) in estimating winter wheat CNC. The best winter wheat CNC accuracy was obtained by the double-layer RBFNN model with SFs+TFs as inputs (R2=0.89, RMSE=13.63 mg g–1, MAE=10.75 mg g–1). The results of this study can provide guidance for the accurate and rapid determination of winter wheat canopy nitrogen content in the field.

Volatile feed costs and extreme weather events are contributing to greater economic risk and precarity throughout much of the United States dairy industry. These challenges have prompted dairy farmers to seek ways to reduce feed imports without compromising milk production. For organic dairy farmers, the need to produce more homegrown forage is exacerbated by the high cost and limited supply of organic feed. Integrating winter cereals for forage as part of a double-cropping system is a potential solution, but increasing the amount of forage in dairy cow rations can reduce milk production if the forages are not managed for optimal quality. Organically managed field experiments in Maryland (MD) and New York (NY) were conducted to address two primary objectives: (1) determine the yield and quality of winter cereals—four cultivars each for barley (Hordeum vulgare L.), cereal rye (Secale cereale L.), and triticale (× Triticosecale Wittm. ex A. Camus.)—grown as forage and harvested at different crop growth stages, and (2) evaluate the trade-offs between yield and quality in relation to winter cereal phenology and harvest date. Mean yield at a commonly harvested growth stage, swollen boot (Zadoks 45), was 1.3, 2.2, and 2.2 Mg ha−1 in MD and 1.8, 2.5, and 2.9 Mg ha−1 in NY for barley, cereal rye, and triticale, respectively. Mean relative forage quality (RFQ) at the same growth stage was 180, 158, and 163 in MD and 179, 156, and 157 in NY for the three species. Overall, cereal rye reached swollen boot stage the earliest, barley produced the highest RFQ and retained high quality the longest, and cereal rye and triticale produced the highest yields. Based on these results, farmers should consider barley cultivars if quality is the priority and winter-hardiness is not a concern; cereal rye cultivars if an early harvest is most important; and triticale cultivars if greater harvest schedule flexibility would be most valuable. These findings can be used to better meet the needs of dairy farmers, enhance double-cropping system performance, and improve the synchronization of harvest timing with the specific needs of lactating dairy cows, dry cows, heifers, and calves.

A field nutrient balance (supplied minus harvested) can be an effective, end-of-season management evaluation tool. However, development of guidance for balance-based management requires knowledge of variability in balance inputs. To contribute to development of such guidelines, we evaluated the impact of corn silage hybrid selection, nutrient management, and growing conditions on field nitrogen (N) balances and documented variability in N and phosphorus (P) balances at the whole-farm, field, within-field levels. Variability in N removal among hybrids was evaluated using hybrid trials (5 locations, 4 years each). Variability in farm and field balances (4 farms, 2 years each) and within-field balances (2 farms, 2 years each) was assessed as well. Nitrogen supply comprised soil N (soil type-specific book values), rotation N, past manure N, and current year N (fertilizer and/or manure). Total N balances included all current year manure N while available N balances considered only plant-available N from manure. Phosphorus balances were derived as total P applied minus P harvested. Yield explained 81% of the variability in N uptake across hybrids. Nitrogen uptake intensity (NUI; N uptake per unit of yield) varied across locations and years, averaging 4.3 ± 0.1 kg N/Mg for short-season hybrids [≤95 days-to-maturity (DTM)] vs. 4.1 ± 0.1 kg N Mg−1 for longer-season hybrids. Whole-farm N balances ranged from 139 to 251 kg N ha−1 for total N and 43 to 106 kg N ha−1 for available N. Phosphorus balances ranged from 28 to 154 kg P ha−1. Balances per field ranged from −8 to 453, −66 to 250 kg N ha−1, and −30 to 315 kg P ha−1 for total N, available N, and total P, respectively, while within-field balances showed even larger ranges. We conclude that (1) variability in corn silage N and P balances at field and within-field scales and across year is large, emphasizing the need for field and within-field (where feasible) evaluation tools and management options, and (2) feasible limits for N balances should include both total and available N.

Weed communities can be influenced by nutrient availability, nutrient form (e. g., ammonium vs. nitrate), amendment timing, amendment type (e.g., organic vs. inorganic), and by immigration of seeds during amendment applications. The objective of this research was to compare the long-term effect of different fertility treatments in a corn (Zea mays L.)-alfalfa (Medicago sativa L.) rotation on taxonomic and functional structure and composition of weed communities by analyzing the soil weed seedbank. After 14 years of a long-term experiment in Aurora, NY, United States, soils were sampled in five fertility treatments for corn years in the rotation: liquid dairy manure, semi-composted separated dairy solids; or inorganic nitrogen (N) as starter fertilizer with either no sidedress N, a low rate or a high rate of inorganic N as sidedress fertilizer. Soil was collected in early spring 2015 and a greenhouse weed seed germination bioassay was used to quantify the germinable soil weed seedbank. Total weed seedbank density, species richness, and evenness did not vary by treatment. However, fertility treatments modified the ecological niche represented by 20 environmental descriptors, which filtered the weed community creating distinct functional group assemblages. A trait-based analysis revealed that nitrophilic dicotyledons preferring alkaline soil were associated with high concentrations of inorganic N fertilizer, whereas highly specialist monocotyledons preferring high amounts of light were associated with low concentrations of inorganic N fertilizer. Because fertility treatments affected weed community composition but not seed bank density and richness, results encourage the development of holistic management strategies that adopt coherent weed management and crop fertilization.

On-farm experimentation (OFE) allows farmers to improve crop management over time. The randomized complete blocks design (RCBD) with field-length strips as individual plots is commonly used, but it requires advanced planning and has limited statistical power when only three to four replications are implemented. Harvester-mounted yield monitor systems generate high resolution data (1-s intervals), allowing for development of more meaningful, easily implementable OFE designs. Here we explored statistical frameworks to quantify the effect of a single treatment strip using georeferenced yield monitor data and yield stability-based management zones. Nitrogen-rich single treatment strips per field were implemented in 2018 and 2019 on three fields each on two farms in central New York. Least squares and generalized least squares approaches were evaluated for estimating treatment effects (assuming independence) versus spatial covariance for estimating standard errors. The analysis showed that estimates of treatment effects using the generalized least squares approach are unstable due to over-emphasis on certain data points, while assuming independence leads to underestimation of standard errors. We concluded that the least squares approach should be used to estimate treatment effects, while spatial covariance should be assumed when estimating standard errors for evaluation of zone-based treatment effects using the single-strip spatial evaluation approach.

Harvester-mounted yield monitor sensors are expensive and require calibration and data cleaning. Therefore, we evaluated six vegetation indices (VI) from unmanned aerial system (Quantix™ Mapper) imagery for corn (Zea mays L.) yield prediction. A field trial was conducted with N sidedress treatments applied at four growth stages (V4, V6, V8, or V10) compared against zero-N and N-rich controls. Normalized difference vegetation index (NDVI) and enhanced vegetation index 2 (EVI2), based on flights at R4, resulted in the most accurate yield estimations, as long as sidedressing was performed before V6. Yield estimations based on earlier flights were less accurate. Estimations were most accurate when imagery from both N-rich and zero-N control plots were included, but elimination of the zero-N data only slightly reduced the accuracy. Use of a ratio approach (VITrt/VIN-rich and YieldTrt/YieldN-rich) enables the extension of findings across fields and only slightly reduced the model performance. Finally, a smaller plot size (9 or 75 m2 compared to 150 m2) resulted in a slightly reduced model performance. We concluded that accurate yield estimates can be obtained using NDVI and EVI2, as long as there is an N-rich strip in the field, sidedressing is performed prior to V6, and sensing takes place at R3 or R4.

Increased production in smallholder beef systems requires improved forage management. Our objective was to evaluate the effects of composted cattle manure and mineral nitrogen (urea) application on soil fertility and partial nutrient balances in plots established to Brachiaria cv. Mulato II in south-central coastal Vietnam from 2010 to 2013. A randomized complete block design was implemented on six farms (blocks), with five rates of composted cattle manure (0, 4, 8, 12, and 24 Mg DM/ha per yr) and three urea rates (0, 60, and 120 kg N/ha per yr) in a factorial design. Soil was analyzed before and after the experiment. Compost increased soil pH, organic matter, Ca, Mg, and Mn. The effect of compost and urea applications on postexperiment soil fertility depended on preexperiment soil fertility for K, P, S, Mg, Zn, Mn, Cu, and organic matter, suggesting that the ability to maintain soil fertility depends on the interaction between soil organic and inorganic amendments and existing soil fertility. Highest farm yields were also achieved on farms with higher preexperiment soil fertility levels. Negative partial nutrient balances for N, P, and K suggest that yields will not be sustainable over time even for the highest fertilization inputs used in this experiment.

The common housefly, Musca domestica, is a considerable component of nutrient recycling in the environment. Use of housefly larvae to biodegrade manure presents an opportunity to reduce waste disposal while the rapidly assimilated insect biomass can also be used as a protein rich animal feed. In this study, we examine the biodegradation of dairy cattle manure using housefly larvae, and the nutritional value of the resulting larva meal as a feed ingredient. Our results demonstrated that dairy cattle manure presents a balanced substrate for larval growth, and the spent manure showed reductions in concentration of total nitrogen (24.9%) and phosphorus (6.2%) with an overall reduction in mass. Larva yield at an optimum density was approximately 2% of manure weight. Nutritional analysis of M. domestica larva meal showed values comparable to most high protein feed ingredients. Larva meal was 60% protein with a well-balanced amino acid profile, and 20% fat with 57% monounsaturated fatty acids, and 39% saturated fatty acids. Larva meal lacked any significant amount of omega-3 fatty acids. Evaluation of micronutrients in larva meal suggested that it is a good source of calcium and phosphorus (0.5% and 1.1% respectively). The nutritional value of larva meal closely matches that of fishmeal, making it a potentially attractive alternative for use as a protein-rich feed ingredient for livestock and aquaculture operations.