What is the Value of Corn Residue to Grazing Cattle?

The United States produces over 370,840,000,000 kg of corn grain and concomitantly more than 303,410,000,000 kg DM of non-grain corn residues (i.e., leaves, husks and stalk) from grain production annually. Although there is an abundance of available corn residue, only 12% of land planted to corn is grazed after harvest (Schmer, 2017), and based on current estimates of nutrient composition (NASEM, 2016), while 30% of available corn residues could maintain the entire United States cow herd. Grazing cattle often select diets with greater nutrient density and digestibility in comparison to the overall biomass available; however, most estimates of nutrient density in corn residues are based on analyses of mechanically harvested residues (i.e., the overall biomass available for grazing). More accurate measurements of selection of botanical parts by grazing cattle and subsequent nutrient intake can allow for improved estimates of performance of cattle grazing corn residues and for development of management strategies that can optimize forage utilization. The first experiment used 6 ruminally cannulated cows to evaluate predictions of diet selection based on chemical components and post-sampling processing techniques in diet samples collected through ruminal evacuation. Predictions of diet composition were improved by increasing differences in concentration of chemical components between cornstalk and leaf and husk (LH) residues up to a coefficient of variation of 22.6 ± 5.4%. Acid detergent insoluble ash, acid detergent lignin, and near infrared reflectance spectroscopy provided the most accurate estimates of composition of the diet. A second experiment was conducted with 6 ruminally cannulated cows to estimate the caloric value and digestibility of corn husk, leaf and stalk. Cattle were fed to near to their maintenance energy requirements with either corn leaves, husk or stalk and corn steep liquor. Net energy available for maintenance value from corn leaves, husks and stalks were 1.80 Mcal/kg DM, 1.15 Mcal/kg DM and 0.83 Mcal/kg DM, respectively. Differences in energy available for maintenance were largely described by differences in methane emissions; cattle fed leaf residue had 116 and 66% less energy losses from methane than cattle fed husk and stalk, respectively. Clearly, selection of different botanical parts by cattle grazing corn residues can change nutrient and energy intake and diet digestibility. Estimates of nutrient composition of corn residues based on total harvestable biomass are unlikely to accurately reflect diets selected by grazing cows. Development of more accurate estimates of diet selection among grazing cattle and feed values of each botanical part in corn residue will allow more accurate estimates of cattle performance. Further, an improved understanding of the feed value of each botanical part in corn residue to cattle may allow for a better evaluation of different management strategies that influence intake of different botanical parts in corn residues by grazing cattle

Using machine learning to estimate herbage production and nutrient uptake on Irish dairy farms

peer-reviewedNutrient management on grazed grasslands is of critical importance to maintain productivity levels, as grass is the cheapest feed for ruminants and underpins these meat and milk production systems. Many attempts have been made to model the relationships between controllable (crop and soil fertility management) and noncontrollable influencing factors (weather, soil drainage) and nutrient/productivity levels. However, to the best of our knowledge not much research has been performed on modeling the interconnections between the influencing factors on one hand and nutrient uptake/herbage production on the other hand, by using data-driven modeling techniques. Our paper proposes to use predictive clustering trees (PCT) learned for building models on data from dairy farms in the Republic of Ireland. The PCT models show good accuracy in estimating herbage production and nutrient uptake. They are also interpretable and are found to embody knowledge that is in accordance with existing theoretical understanding of the task at hand. Moreover, if we combine more PCT into an ensemble of PCT (random forest of PCT), we can achieve improved accuracy of the estimates. In practical terms, the number of grazings, which is related proportionally with soil drainage class, is one of the most important factors that moderates the herbage production potential and nutrient uptake. Furthermore, we found the nutrient (N, P, and K) uptake and herbage nutrient concentration to be conservative in fields that had medium yield potential (11 t of dry matter per hectare on average), whereas nutrient uptake was more variable and potentially limiting in fields that had higher and lower herbage production. Our models also show that phosphorus is the most limiting nutrient for herbage production across the fields on these Irish dairy farms, followed by nitrogen and potassium

Dietary crude protein and nitrogen utilisation in two contrasting dairy systems : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Animal Science, School of Agriculture and Environment, Massey University, Palmerston North, New Zealand

This thesis evaluated the efficiency of crude protein utilisation (ECPU) in dairy cows and nitrogen (N) utilisation efficiency (NUE) of two pasture-based dairy systems differing in intensification levels in New Zealand. During two consecutive seasons, in the low-intensity production system (LIPS), 257 cows were milked once-daily with low supplementation, and in the high-intensity production system (HIPS), 210 cows were milked twice-daily with higher supplementation. At every herd test, ECPU was calculated as protein yield (PY) divided by crude protein intake (CPI), estimated from feed intake. Milk urea (MU) was measured in early-, mid-, and late-lactation. Urinary N was estimated by back-calculation from estimated faecal N, taking into consideration N contained in milk and in body tissues. Pasture allocation represented 93% and 65% of the total intake for LIPS and HIPS cows, respectively, resulting in higher CPI for LIPS cows throughout the lactation. Compared to HIPS cows, LIPS cows produced 22% and 16% less milk and protein, with 32% higher MU, and 25% lower ECPU. Urine N was 34% higher in LIPS cows but faecal N was 5% higher for HIPS cows. A multivariate predictive model of ECPU was developed, including milk production performance, live weight variation, diet composition and quality along with climatic variables. The model accurately predicted the ECPU in an internal validation dataset (RPE = 6.96%, R2 = 0.95). Milk urea was not selected as a predictive variable of ECPU, considering that cows of higher ECPU also had higher MU. Compared with cows of high MU genetic merit, cows of lower MU genetic merit had lower milk production and similar ECPU. A whole-farm assessment of NUE, N losses and financial analysis was undertaken. On whole-farm level, LIPS produced 23% less milk and NUE was 31% lower when compared to HIPS. The lower MY along with the 35% higher N fertiliser applied on LIPS produced a higher N surplus per ha causing higher N losses when compared to HIPS. Despite the higher feed costs of HIPS, profitability was 16% higher because of milking more cows with higher MY when compared to LIPS

Source-tracking cadmium in New Zealand agricultural soils: a stable isotope approach

Cadmium (Cd) is a toxic heavy metal, which is accumulated by plants and animals and therefore enters the human food chain. In New Zealand (NZ), where Cd mainly originates from the application of phosphate fertilisers, stable isotopes can be used to trace the fate of Cd in soils and potentially the wider environment due to the limited number of sources in this setting. Prior to 1997, extraneous Cd added to soils in P fertilisers was essentially limited to a single source, the small pacific island of Nauru. Analysis of Cd isotope ratios (ɛ114/110Cd) in Nauru rock phosphate, pre-1997 superphosphate fertilisers, and Canterbury (Lismore Stony Silt Loam) topsoils (Winchmore Research Farm) has demonstrated their close similarity with respect to ɛ114/110Cd. We report a consistent ɛ114/110Cd signature in fertiliser-derived Cd throughout the latter twentieth century. This finding is useful because it allows the application of mixing models to determine the proportions of fertiliser-derived Cd in the wider environment. We believe this approach has good potential because we also found the ɛ114/110Cd in fertilisers to be distinct from unfertilised Canterbury subsoils. In our analysis of the Winchmore topsoil series (1949-2015), the ɛ114/110Cd remained quite constant following the change from Nauru to other rock phosphate sources in 1997, despite a corresponding shift in fertiliser ɛ114/110Cd at this time. We can conclude that to the present day, the Cd in topsoil at Winchmore still mainly originates from historical phosphate fertilisers. One implication of this finding is that the current applications of P fertiliser are not resulting in further Cd accumulation. We aim to continue our research into Cd fate, mobility and transformations in the NZ environment by applying Cd isotopes in soils and aquatic environments across the country

ASAS–NANP Symposium: Mathematical Modeling in Animal Nutrition: Opportunities and Challenges of Confned and Extensive Precision Livestock Production

Modern animal scientists, industry, and managers have never faced a more complex world. Precision livestock technologies have altered management in confned operations to meet production, environmental, and consumer goals. Applications of precision technologies have been limited in extensive systems such as rangelands due to lack of infrastructure, electrical power, communication, and durability. However, advancements in technology have helped to overcome many of these challenges. Investment in precision technologies is growing within the livestock sector, requiring the need to assess opportunities and challenges associated with implementation to enhance livestock production systems. In this review, precision livestock farming and digital livestock farming are explained in the context of a logical and iterative fve-step process to successfully integrate precision livestock measurement and management tools, emphasizing the need for precision system models (PSMs). This fve-step process acts as a guide to realize anticipated benefts from precision technologies and avoid unintended consequences. Consequently, the synthesis of precision livestock and modeling examples and key case studies help highlight past challenges and current opportunities within confned and extensive systems. Successfully developing PSM requires appropriate model(s) selection that aligns with desired management goals and precision technology capabilities. Therefore, it is imperative to consider the entire system to ensure that precision technology integration achieves desired goals while remaining economically and managerially sustainable. Achieving long-term success using precision technology requires the next generation of animal scientists to obtain additional skills to keep up with the rapid pace of technology innovation. Building workforce capacity and synergistic relationships between research, industry, and managers will be critical. As the process of precision technology adoption continues in more challenging and harsh, extensive systems, it is likely that confned operations will beneft from required advances in precision technology and PSMs, ultimately strengthening the benefts from precision technology to achieve short- and long-term goals

Data-Driven Air Quality and Environmental Evaluation for Cattle Farms

The expansion of agricultural practices and the raising of animals are key contributors to air pollution. Cattle farms contain hazardous gases, so we developed a cattle farm air pollution analyzer to count the number of cattle and provide comprehensive statistics on different air pollutant concentrations based on severity over various time periods. The modeling was performed in two parts: the first stage focused on object detection using satellite data of farm images to identify and count the number of cattle; the second stage predicted the next hour air pollutant concentration of the seven cattle farm air pollutants considered. The output from the second stage was then visualized based on severity, and analytics were performed on the historical data. The visualization illustrates the relationship between cattle count and air pollutants, an important factor for analyzing the pollutant concentration trend. We proposed the models Detectron2, YOLOv4, RetinaNet, and YOLOv5 for the first stage, and LSTM (single/multi lag), CNN-LSTM, and Bi-LSTM for the second stage. YOLOv5 performed best in stage one with an average precision of 0.916 and recall of 0.912, with the average precision and recall for all models being above 0.87. For stage two, CNN-LSTM performed well with an MAE of 3.511 and an MAPE of 0.016, while a stacked model had an MAE of 5.010 and an MAPE of 0.023