Modeling the profitability of investing in cooling systems in dairy farms under several intensities of heat stress in the Mediterranean

Heat stress (HS) in dairy cows affects dry matter intake, milk yield, reproduction, and culling rate. Cooling systems (CS) may partially revert these effects, but their profitability depends on the price of milk and effectiveness and cost of the CS. Because these effects may interact over time, stochastic dynamic models are useful tools to evaluate the effects of HS and the profitability of CS. Several HS intensity scenarios, from 1,000 to 31,000 temperature and humidity index load (THI_Load, units/yr), were simulated in a stochastic dynamic dairy herd simulator, with 3 milk prices (€0.28, €0.32, and €0.36/L) and 2 initial investment costs in fans and sprinklers (€100 and €200/cow). The HS and CS scenarios simulated were modeled as a function of the THI_Load to predict the technical and economic performance in 21 selected locations of the Mediterranean. The THI_Load mean of the 21 selected locations was 12,530 (ranging from 6,908 to 31,424). Heat stress reduced milk yield in a range of 346 to 1,696 L/cow per year, feeding costs in a range of €63 to €266/cow per year, and pregnancy rate in a range of 1.0 to 3.0%/yr and increased culling rates in a range of 5.7 to 16.4%/yr compared with the control scenario. The implementation of CS increased milk yield in a range from 173 to 859 L/cow per year, feeding cost in a range from €26 to €139/cow per year, and pregnancy rate in a range from 0.1 to 1.0%/yr and reduced culling rate in a range from 1.0 to 3.9%/yr compared with HS scenarios. When the THI_Load was ≤6,300, the implementation of CS was never profitable, from 6,300 to 11,000 was dependent on milk price and CS cost, and over 11,000 was consistently profitable. The Δnet margin (€/cow per year) for CS at an initial investment cost of €100/cow ranged from -9 to 239 and at an initial investment cost of €200/cow ranged from -24 to 225. The profitability of CS depends on the THI_Load, milk price, and CS cost.

Milk metabolome reveals pyrimidine and its degradation products as the discriminant markers of different corn silage-based nutritional strategies

The purpose of this study was to evaluate the effect of 6 different feeding systems (based on corn silage as the main ingredient) on the chemical composition of milk and to highlight the potential of untargeted metabolomics to find discriminant marker compounds of different nutritional strategies. Interestingly, the multivariate statistical analysis discriminated milk samples mainly according to the high-moisture ear corn (HMC) included in the diet formulation. Overall, the most discriminant compounds, identified as a function of the HMC, belonged to AA (10 compounds), peptides (71 compounds), pyrimidines (38 compounds), purines (15 compounds), and pyridines (14 compounds). The discriminant milk metabolites were found to significantly explain the metabolic pathways of pyrimidines and vitamin B₆. Interestingly, pathway analyses revealed that the inclusion of HMC in the diet formulation strongly affected the pyrimidine metabolism in milk, determining a significant up-accumulation of pyrimidine degradation products, such as 3-ureidopropionic acid, 3-ureidoisobutyric acid, and 3-aminoisobutyric acid. Also, some pyrimidine intermediates (such as l-aspartic acid, N-carbamoyl-l-aspartic acid, and orotic acid) were found to possess a high discrimination degree. Additionally, our findings suggested that the inclusion of alfalfa silage in the diet formulation was potentially correlated with the vitamin B₆ metabolism in milk, being 4-pyridoxic acid (a pyridoxal phosphate degradation product) the most significant and up-accumulated compound. Taken together, the accumulation trends of different marker compounds revealed that both pyrimidine intermediates and degradation products are potential marker compounds of HMC-based diets, likely involving a complex metabolism of microbial nitrogen based on total splanchnic fluxes from the rumen to mammary gland in dairy cows. Also, our findings highlight the potential of untargeted metabolomics in both foodomics and foodomics-based studies involving dairy products.

Assessment of the Relations for Determining the Profitability of Dairy Farms, A Premise of Their Economic Sustainability

The profitability of dairy farms is a broadly addressed issue in research, for different farming systems and even more so now, when it comes to the issue of sustainability in different agricultural fields. The present study presents an evaluation of the relations used for the determination of profitability of various categories of dairy farms, in terms of size, geographical area, and total milk production. In order to analyze the associated influence exerted on the level of profitability by the selected technical and economic indicators, regression functions were applied. The TableCurve program was used to determine the ideal equation that describes the data entered in a two- or three-dimensional representation. The research results showed that the size of farms and the level and value of milk production are directly correlated with profitability, and the unit cost is inversely correlated with it.

Assessment of feed and economic efficiency of dairy farms based on multivariate aggregation of partial indicators measured on field

Many of the metrics used to evaluate farm performance are only partial indicators of farm operations, which are assumed to be best predictors of the whole farm efficiency. The main objective of this work was to identify aggregated multiple indexes of profitability using common partial indicators that are routinely available from individual farms to better support the short-term decision-making processes of the cattle-feeding process. Data were collected from face-to-face interviews with farmers from 90 dairy farms in Italy and used to calculate 16 partial indicators that covered almost all indicators currently used to target feeding and economic efficiency in dairy farms. These partial indicators described feed efficiency, energy utilization, feed costs, milk-to-feed price ratio, income over feed costs, income equal feed cost, money-corrected milk, and bargaining power for feed costs. Calculations of feeding costs were based on lactating cows or the whole herd, and income from milk deliveries was determined with or without considering the milk quality payment. Multivariate factor analysis was then applied to the 16 partial indicators to determine simplified and latent structures. The results indicated that 5 factors explained 70% of the variability. Each of the original partial indicator was associated with all factors in different proportions, as indicated by loading scores from the multivariate factor analysis. Based on the loading scores, we labeled these 5 factors as "economic efficiency," "energy utilization," "break-even point," "milk-to-feed price," and "bargaining power of the farm," in decreasing order of explained communality. The first 3 factors shared 83% of the total communality. Feed efficiency was similarly associated with factor 1 (53% loading) and factor 2 (66% loading). Only factor 4 was significantly affected by farm location. Milk production and herd size had significant effects on factor 1 and factor 2. Our multivariate approach eliminated the problem of multicollinearity of partial indicators, providing simple and effective descriptions of farm feeding economics. The proposed method allowed the evaluation, benchmarking, and ranking of dairy herd performance at the level of single farms and at territorial level with high opportunity to be used or replicated in other areas.

Cross-sectional study of the effect of diet composition on plasma folate and vitamin B12 concentrations in Holstein cows in the United States and Canada

The objective of this cross-sectional study was to assess the variability of plasma folate and vitamin B₁₂ concentrations in lactating Holstein cows across the United States and Canada. We also evaluated the effect of diet composition and cow characteristics on folate and vitamin B₁₂ plasma vitamin concentrations. A total of 22 and 24 US and Canadian dairy herds were enrolled, totaling 427 and 476 cows at 10 to 197 days in milk across all US and Canadian herds, respectively. Blood samples were taken to analyze plasma folate and vitamin B₁₂ concentrations, and ingredients of the diet were collected to determine nutrient composition. To reduce the number of interdependent variables in the analysis of the association of diet composition with plasma vitamin concentrations, we conducted a principal component analysis. Plasma folate concentrations were lower for US cows [13.4 ng/mL, 95% confidence interval (CI): 12.7-14.2] than for Canadian cows (14.5 ng/mL, 95% CI: 13.7-15.2), and the opposite was observed for plasma vitamin B₁₂ concentrations (US 206 pg/mL, 95% CI: 192-221; Canada 170 pg/mL, 95% CI: 159-181). The highest plasma concentrations of both vitamins were observed in the Northwest region of the United States (Oregon and Washington). Cows in California had the lowest plasma folate concentrations, and cows in Québec and New York State had the lowest plasma vitamin B₁₂ concentrations. Plasma folate concentrations were higher for multiparous than for primiparous cows and plasma vitamin B₁₂ concentrations progressively increased from parity 1 to 3 and higher. For both studied vitamins, plasma concentrations were lower at 0 to 55 than at 56 to 200 days in milk. Of 3 principal components, the one associated with dietary carbohydrates was significantly correlated with plasma folate and vitamin B₁₂ concentrations. Indeed, plasma folate concentrations decreased with dietary fiber concentrations (i.e., neutral and acid detergent fibers and lignin) and increased with dietary nonfiber carbohydrate concentrations. We obtained the opposite results for plasma vitamin B₁₂ concentrations. Both multivariable models explained 41% (pseudo-R²) of the variation in plasma folate and vitamin B₁₂ concentrations. Information gathered in this study is the first step toward determining sources of variation in plasma folate and vitamin B₁₂ concentrations, as well as the vitamin status of cows.

A Comprehensive Energy Analysis and Related Carbon Footprint of Dairy Farms, Part 1: Direct Energy Requirements

Dairy cattle farms are continuously developing more intensive systems of management which require higher utilization of durable and not-durable inputs. These inputs are responsible of significant direct and indirect fossil energy requirements which are related to remarkable emissions of CO2. This study aims to analyze direct energy requirements and the related carbon footprint of a large population of conventional dairy farms located in the south of Italy. A detailed survey of electricity, diesel and Liquefied Petroleum Gas (LPG) consumptions has been carried out among on-farm activities. The results of the analyses showed an annual average fuel consumption of 40 kg per tonne of milk, while electricity accounted for 73 kWh per tonne of milk produced. Expressing the direct energy inputs as primary energy, diesel fuel results the main resource used in on-farm activities, accounting for 72% of the total fossil primary energy requirement, while electricity represents only 27%. Moreover, larger farms were able to use more efficiently the direct energy inputs and reduce the related emissions of carbon dioxide per unit of milk produced, since the milk yield increases with the herd size. The global average farm emissions of carbon dioxide equivalent, due to all direct energy usages, accounted for 156 kg CO2-eq per tonne of Fat and Protein Corrected Milk (FPCM), while farms that raise more than 200 heads emitted 36% less than the average value. In this two-part series, the total energy demand (Part 1 + Part 2) per farm is mainly due to agricultural inputs and fuel consumption, which have the largest quota of the annual requirements for each milk yield class. These results also showed that large size farms held lower CO2-eq emissions when referred to the mass of milk produced.