Heat stress, especially in countries with hot climates, is a major cause of low fertility in high-producing dairy herds. Management strategies are needed to help producers improve the reproductive performance of their dairy animals under such conditions. The current study aims to evaluate the effects of pre- and post-milking insemination on the conception rate (CR) in dairy cows. The dataset included 1294 insemination records leading to pregnancy in 708 lactating Holstein dairy cows. The GLIMMIX model procedure of SAS based on the generalized linear mixed model methodology was used to analyse the results of insemination (success or failure) as a binomial distribution with the logit link function. Differences were observed in CRs between pre- and post-milking insemination. The pregnancy odds ratio (OR) for post-milking insemination relative to that for pre-milking one was estimated at 1.90 [1.23‒2.91; 95% confidence interval (CI)]. Different levels of interaction were obtained between average daily milk production and time of insemination. In the high-producing group, the pregnancy OR for the post-milking relative to that for the pre-milking insemination was estimated at 3.53 (2.00‒6.24; 95% CI). A significant interaction effect was obtained between insemination time and the temperature-humidity index. A pregnancy OR of 2.52 (1.22‒4.14; 95% CI) was recorded for the cows inseminated after milking on days with higher levels of heat and humidity stress v. the pre-milking inseminated ones. Based on the results, post-milking insemination of high-producing cows increased CRs, especially on days with high heat and humidity stress.

Optimization of production factors plays a central role in efficient milk production operations. Causal relationships between production parameters (health, fertility, feeding, performance and farm size) on the one hand and efficiency parameters on the other have been identified in several studies. In recent years, structural equation modelling (SEM) has not only gained importance in agriculture but also in milk production, providing the opportunity to investigate multilateral relationships. Additionally, SEM enables an estimation of parameters which are not themselves measurable, the so-called latent variables. The current study was based on the data of 943 branch settlements (including the years 2012 and 2013) of dairy farms keeping German Holstein cows in Schleswig-Holstein (Northern Germany) which provided a combination of the structural parameters, economic parameters and biological performance of the farms. An SEM using this combined data was applied to investigate the complexity of influences on efficiency parameters in milk production. Efficiency was sub-divided into and evaluated by two effect variables (economic efficiency and biological efficiency). Economic efficiency was defined as a conventional efficiency assessment criterion from full-cost accounting, whereas biological efficiency was used to evaluate the quality of herd management. Performance was identified as the key parameter for independent evaluation of efficiency by assessing biological (γ41 = 0.644) or economic efficiency (γ42 = 0.266). The SEM explained more than three times higher proportion of the variance in biological efficiency than in economic efficiency. The investigation proved the eligibility of partial least squares SEM for the evaluation of efficiency in milk production.

A stochastic capital budget was used to analyze the effect of net metering policies and carbon credits on profitability of anaerobic digesters on dairy farms in Pennsylvania. We analyzed three different farm sizes—500, 1, 000, and 2,000 cows—and considered the addition of a solids separator to the project. Results indicate that net metering policies and carbon credits increase the expected net present value (NPV) of digesters. Moreover, the addition of a solids separator further increases the mean NPV of the venture. In general, the technology is profitable only for very large farms (1, 000+ cows) that use the separated solids as bedding material.

Agriculture and animal husbandry are important contributors to global emissions of greenhouse (GHG) and acidifying gases. Moreover, they contribute to water pollution and to consumption of non-renewable natural resources such as land and energy. The Life Cycle Assessment (LCA) methodology allows evaluation of the environmental impact of a process from the production of inputs to the final product and to assess simultaneously several environmental impact categories among which GHG emissions, acidification, eutrophication, land use and energy use. The main purpose of this study was to evaluate, using the LCA methodology, the environmental impact of milk production in a sample of 41 intensive Italian dairy farms and to identify, among different farming strategies, those associated with the best environmental performances. The functional unit was 1 kg Fat and Protein Corrected Milk (FPCM). Farms showed characteristics of high production intensity: FPCM, expressed as tonnes per hectare, was 30·8±15·1. Total GHG emission per kg FPCM at farm gate was 1·30±0·19 kg CO2 eq. The main contributors to climate change potential were emissions from barns and manure storage (50·1%) and emissions for production and transportation of purchased feeds (21·2%). Average emission of gases causing acidification to produce 1 kg FPCM was 19·7±3·6 g of SO2 eq. Eutrophication potential was 9·01±1·78 ${\rm PO}_{\rm 4}^{{\rm 3} -} {\rm eq}.$ per kg FPCM on average. Farms from this study needed on average 5·97±1·32 MJ per kg FPCM from non-renewable energy sources. Energy consumption was mainly due to off-farm activities (58%) associated with purchased factors. Land use was 1·51±0·25 m2 per kg FPCM. The farming strategy based on high conversion efficiency at animal level was identified as the most effective to mitigate the environmental impact per kg milk at farm gate, especially in terms of GHG production and non-renewable energy use per kg FPCM.

Defining the balance between socio-economical and bio-physical aspects in order to promote sustainable development in agriculture is a fundamental challenge for researchers. The aim of this study is to assist in constructing a science of sustainability in agriculture and to assess sustainability in various types of agroecosystem managements characterized by different agricultural and rural developments. This study evaluates sustainability on Terceira Island (Portugal) and in the Province of Viterbo (Italy) by using selected agroecosystem sustainability indicators capable of achieving an energetic and monetary input/output analysis. Overall, results showed two rural and agricultural realities that outline two different responses to world market demand, which are partly due to specific rural histories. In terms of energy supply, Terceira and Viterbo dairy farms use the same amount of input but in monetary terms the level of input is more than double in Viterbo (€1651 ha−1 versus €616 ha−1) which is due to the lower cost of direct energy (e.g., fuel) on the island. The use of direct energy input (fuel, electricity and lubricants) on the farms in Viterbo decreases in mixed farm systems and is at its minimum on dairy farms. On the other hand, the use of indirect energy input (fertilizers, herbicides, pesticides, seeds and feedstuffs) is greater on Terceira farms compared with those in Viterbo (0.76 versus 0.60, respectively). Generally speaking, the crops grown on Terceira are not diversified and this has caused environmental issues on the island due to the milk production that is mainly exported. In Europe, more intellectual and financial resources for measuring and monitoring sustainability conditions in agriculture are necessary, in order to appropriately inform decision makers at both institutional and individual level.

Organic farms are characterized as low external input agro-ecosystems. Currently, some organic dairy farmers feed higher amounts of concentrates and succulent feed, some of which is purchased, to increase the dairy performance of their cows. To assess the environmental impact of this practice, nutrient balances at the farm-gate level of 26 organic dairy farms located in two different regions in Germany were compiled and analysed. The farms are characterized by different production features and feeding intensity levels [0–2.72 t dry matter (DM) of concentrates per cow and year, which was 0–378 g kg−1 milk] yielding 5150–8790 kg milk on average per cow and year. The area- and product (milk)-related farm-gate nutrient budgets for P and K are almost balanced [mean −3 kg P ha−1, range (R): −14 to 4 kg P ha−1; −0.5 g P kg−1 milk, R: −2.8 to 0.9 g P kg−1 milk and 1 kg K ha−1, R: −13 to 15 kg K ha−1; 0.1 g K kg−1 milk, R: −2.4 to 3.9 g K kg−1 milk]. The N surplus averages only 43 kg ha−1 (R: 8–85 kg N ha−1) and 8.2 g kg−1 milk (R: 2.1–17.1 g kg−1 milk), but the correlation between the amount of feed purchased on a net basis and N surplus is significant (r=0.56, P=0.003). Average area-related nutrient use efficiency for all farms calculated as the proportion of input to output is high for N (45%), P (164%) and K (91%). The share of nutrient input and output components and correlations between parameters are presented. To classify the results, investigations comparing organic and conventional dairy farming in Europe are listed, indicating an N surplus for organic farms, which is often only half or a third of the surplus of conventional farms. However, intensification in organic dairy farming has, in some cases, significant impacts that need to be assessed to determine its environmental performance and profile.