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With the increasing availability of sexed semen, farms have the opportunity to select genetically superior dams to produce their replacement animals and to produce crossbred calves for beef production of higher economic value than the remainder of the herd. However, higher costs and reduced fertility of sexed semen complicate the decision of when and to what extent sexed semen should be applied in a herd. The objective of this study was to explore the economically optimal utilization of sexed semen and crossbreeding among North Rhine-Westphalian dairy farms in a holistic single-farm model. For the analysis, we derived a representative sample of farms from Latin Hypercube sampling based on the observed distribution of farm characteristics from official North Rhine-Westphalian Farm Structure Survey data. Market- and technology-related input parameters such as output prices and sexed semen accuracy and fertility were included in the sampling procedure. Modeling results of the systematic sensitivity analysis were evaluated in a statistical meta-model. We found that the profit-maximizing utilization of sexed semen and crossbreeding was highly heterogeneous among the farms. Farms with lower stocking densities, <2 livestock units (LU)/ha, were generally found to produce excess heifers for sale, whereas farms with stocking densities >2 LU/ha were producing crossbred calves and using sexed semen only to produce replacement animals. On average, female-sexed dairy semen was used on 25.3% of all inseminations. Beef semen (both sexed and conventional) for producing crossbred calves was used in an average of 21.5% of the inseminations. The combination of sexed semen and crossbreeding increased profits from €0 to €568 per cow per year, with an average of €79.42 per cow per year. Farms characterized by low stocking densities (<2 LU/ha) and above-average replacement rates (>40%) were found to have higher profit increases as a result of selling more heifers from the use of sexed semen. Overall, sexed semen and crossbreeding adoption were most sensitive to stocking density and average cow longevity, as well as to additional costs for sexed semen and sexed semen accuracy. Our results show the potential of modern breeding technologies to improve dairy farm profits and the need to judge their profitability in the light of farm-specific production settings. [ABSTRACT FROM AUTHOR]

Livestock production, and more particularly ruminants, is criticized for its low conversion efficiency of natural resources into edible food. The objectives of this paper are to propose an evaluation of the contribution to food security of different European cattle farms through three criteria: 1) food production assessed by the amount of human-edible protein (HEP) and energy (HEE) produced at farm level, 2) feed-food competition at the beef production scale estimated in terms of net human-edible protein and energy and in terms of land used, and 3) food affordability assessed by the production cost of meat, protein and energy. The analysis is based on 16 representative beef production systems in France, Belgium, Ireland, Italy and Germany and covers cow-calf systems, finishing systems, dairy and mixed dairy- finishing systems, with or without cash crops. The results show that, at the farm level, systems producing both beef and milk or cereals have higher HEP and HEE production per hectare (up to 370 kg of HEP and 60,000 106J.ha−1) than specialized beef systems (up to 50 kg of HEP and 1600 106J.ha−1) and have lower production costs (approximately €6 kg−1 of HEP in mixed beef system and €29 kg−1 of HEP in a specialized cow-calf-fattener system). Beef systems are almost all HEE net consumers. Results are more variable concerning net HEP efficiency. The cow-calf enterprises are mostly net producers of HEP but, in order to produce human edible meat, these systems need to be combined with finishing systems that are mostly net consumers of HEP. In most cases, cow-calf-finishing systems are net consumers of HEP (between 0.6 and 0.7) but grass-based systems using very little concentrates or systems using co-products not edible by humans are net HEP producers. The grass-based systems use more land area per kilogram of carcass but a major part of this area is non-tilled land, thus these systems are not in direct competition with human food production. The lowest meat production costs are the finishing systems producing the most live weight per livestock unit (LU) per year and dairy systems in lowland which share the costs between milk and meat. Although most of HEE and HEP efficient farms typically have higher meat production costs, some grassland based systems stand out positively for all indicators. These results pave the way for improvements of the contribution of beef production systems to food security. Net Human Edible Protein and Energy Efficiencies of meat production (M_HEP_eff and M_HEE_eff). Notes: M_HEP_eff = HEP of meat HEP of feed and M_HEE_eff = HEE of meat HEE of feed . Calculations are based on farm case studies. [Display omitted] • The systems with both beef and milk or cereals produce more protein and energy per ha. • Unlike the finishing systems, the cow-calf ones are mostly net producers of protein. • Cow-calf-finishing systems using very little concentrates are net protein producers. • The grass-based systems use more land area per kg of carcass but mostly non-tilled land. • Some trade off exist between meat production cost and feed-food competition. [ABSTRACT FROM AUTHOR]

Farm-scale and agent-based models draw typically on detailed and preferably spatially explicit single farm data. Data protection standards however restrict or exclude their access, as for example in Germany. We provide data on a synthetic farm population of the German federal state of North Rhine-Westphalia, mainly based on the German Farm Structure Survey 2016 and plot specific crop data from 2019/2020. The population is derived from farm typology at administrative unit level to which the observed plots are allocated afterwards. The data contains 25,858 farms and covers 1.3 million ha of agricultural land, provided at plot scale in a geospatial vector and at farm scale in tabular format. For each plot, the managing farm (including the estimated farm's location), the number of livestock, the cultivated crop, as well as the corresponding administration units are indicated. Furthermore, spatial data such as yield information, soil characteristics, as well as monitoring data on environmental status are attached. The provided data allows for diverse analysis on the farm population in the federal state of North Rhine-Westphalia with farm, agent-based or different bio-physical models. Furthermore, it can serve as a test data set for models which require detailed and spatially explicit farm data., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships which have or could be perceived to have influenced the work reported in this article., (© 2021 The Authors.)