Predicting nitrogen use efficiency, nitrogen loss and dry matter intake of individual dairy cows in late lactation by including mid-infrared spectra of milk samples.

BACKGROUND: Nitrate leaching to groundwater and surface water and ammonia volatilization from dairy farms have negative impacts on the environment. Meanwhile, the increasing demand for dairy products will result in more pollution if N losses are not controlled. Therefore, a more efficient, and environmentally friendly production system is needed, in which nitrogen use efficiency (NUE) of dairy cows plays a key role. To genetically improve NUE, extensively recorded and cost-effective proxies are essential, which can be obtained by including mid-infrared (MIR) spectra of milk in prediction models for NUE. This study aimed to develop and validate the best prediction model of NUE, nitrogen loss (NL) and dry matter intake (DMI) for individual dairy cows in China. RESULTS: A total of 86 lactating Chinese Holstein cows were used in this study. After data editing, 704 records were obtained for calibration and validation. Six prediction models with three different machine learning algorithms and three kinds of pre-processed MIR spectra were developed for each trait. Results showed that the coefficient of determination (R2) of the best model in within-herd validation was 0.66 for NUE, 0.58 for NL and 0.63 for DMI. For external validation, reasonable prediction results were only observed for NUE, with R2 ranging from 0.58 to 0.63, while the R2 of the other two traits was below 0.50. The infrared waves from 973.54 to 988.46 cm-1 and daily milk yield were the most important variables for prediction. CONCLUSION: The results showed that individual NUE can be predicted with a moderate accuracy in both within-herd and external validations. The model of NUE could be used for the datasets that are similar to the calibration dataset. The prediction models for NL and 3-day moving average of DMI (DMI_a) generated lower accuracies in within-herd validation. Results also indicated that information of MIR spectra variables increased the predictive ability of models. Additionally, pre-processed MIR spectra do not result in higher accuracy than original MIR spectra in the external validation. These models will be applied to large-scale data to further investigate the genetic architecture of N efficiency and further reduce the adverse impacts on the environment after more data is collected.

Yield gap analysis in dairy production systems using the mechanistic model LiGAPS-Dairy.

The difference between the theoretical maximum (potential) production and the actual production realized by farmers is referred to as the yield gap. The objectives of this study are to develop a mechanistic model for dairy cows that allows yield gap analysis in dairy production systems and to evaluate model performance. We extended and adapted an existing model for beef cattle to dairy cattle, and the new model was named Livestock simulator for Generic analysis of Animal Production Systems-Dairy cattle (LiGAPS-Dairy). Milk production and growth of an individual cow over its entire lifespan were described as a function of the animal's genotype, the ambient climate, feed quality, and available feed quantity. The model was parameterized for Holstein-Friesian cows. After calibration, we evaluated model performance by comparing simulated results and measured results from experimental farms in the Netherlands, which were not used for model calibration. Cows were permanently housed in stables, where the diet consisted of predetermined amounts of concentrates and ad libitum high-quality roughage. The mean absolute error (MAE) for simulated milk production per lactation was 12% of the measured milk production, whereas the MAE for simulated daily milk yields was 19%. The MAE for simulated feed intake per lactation was 10% of the measured feed intake, whereas the MAE for simulated daily feed intake was 19%. The average yield gap for dairy cows was 11% of the potential milk production (YP). Yield gap analysis indicated that for experimental farms in the Netherlands, the difference between YP and feed quality limited milk production (YL) of 1,009 kg fat- and protein-corrected milk was mainly explained by feed intake capacity (33%), protein deficiency (25%), cow weight at the start of experiments (23%), and heat stress (19%). The LiGAPS-Dairy model also indicated the periods during lactation in which these factors affected milk production. In our opinion, the overall model performance is acceptable for permanently housed cows under Dutch conditions. The model needs to be evaluated further for other production systems, countries and breeds. Thereafter, LiGAPS-Dairy can be used for yield gap analysis and exploration of options to increase resource use efficiency in dairy production.

Understanding transitions in farming systems and their effects on livestock rearing and smallholder livelihoods in Telangana, India.

Increasing food demands are causing rapid transitions in farming systems, often involving intensified land and resource use. While transitioning has benefits regarding poverty alleviation and food outputs, it also causes environmental and social issues over time. This study aims to understand the transitions in farming systems in a region in Telangana, from 1997 to 2015, and their effect on livestock rearing and smallholder livelihoods. We also examine the impact of the transitions on lower caste groups and women in particular. We collected data using a combination of methods, i.e., a household survey, focus group discussions, and secondary data sources, to build a comprehensive picture of the transitions in the region. We found that subsistence mixed farming systems transitioned to market-orientated specialized systems over a short time span. As the transition process gained momentum, households either intensified their production or got marginalized. Technological interventions, development programs with integrated approaches, and market demand for certain agricultural produce triggered increased regional production but also led to the scarcity of water, land, and labor. The transitions marginalized some of the households, changed the role of livestock in farming, and have been inclusive of both lower caste groups and women in terms of increased ownership of large ruminants and access to technologies. However, for women specifically, further increase in workload in the context of farming is also found.

Predicting nutrient excretion from dairy cows on smallholder farms in Indonesia using readily available farm data.

OBJECTIVE: This study was conducted to provide models to accurately predict nitrogen (N) and phosphorus (P) excretion of dairy cows on smallholder farms in Indonesia based on readily available farm data. METHODS: The generic model in this study is based on the principles of the Lucas equation, describing the relation between dry matter intake (DMI) and faecal N excretion to predict the quantity of faecal N (QFN). Excretion of urinary N and faecal P were calculated based on National Research Council recommendations for dairy cows. A farm survey was conducted to collect input parameters for the models. The data set was used to calibrate the model to predict QFN for the specific case. The model was validated by comparing the predicted quantity of faecal N with the actual quantity of faecal N (QFNACT) based on measurements, and the calibrated model was compared to the Lucas equation. The models were used to predict N and P excretion of all 144 dairy cows in the data set. RESULTS: Our estimate of true N digestibility equalled the standard value of 92% in the original Lucas equation, whereas our estimate of metabolic faecal N was -0.60 g/100 g DMI, with the standard value being -0.61 g/100 g DMI. Results of the model validation showed that the R2 was 0.63, the MAE was 15 g/animal/d (17% from QFNACT), and the RMSE was 20 g/animal/d (22% from QFNACT). We predicted that the total N excretion of dairy cows in Indonesia was on average 197 g/animal/d, whereas P excretion was on average 56 g/animal/d. CONCLUSION: The proposed models can be used with reasonable accuracy to predict N and P excretion of dairy cattle on smallholder farms in Indonesia, which can contribute to improving manure management and reduce environmental issues related to nutrient losses.

Pastoralists in a changing environment: The competition for grazing land in and around the W Biosphere Reserve, Benin Republic.

Pastoralists face increasing competition for land with crop farmers and nature in and around the W Biosphere Reserve (WBR) in Benin. Our aim was to describe and analyse land use changes in order to understand their drivers, and to describe and analyse the viewpoints of relevant stakeholders in order to understand the competition for land. To this end, remote sensing data, regional statistics, and survey data were collected. We found that crop land expansion around the WBR was the direct driver of decrease of the grazing land area. Population growth and rising demand for food crops, and government support to the cotton sector were indirect drivers of grazing land reduction. Furthermore, competing claims on land among users arose from the complex interaction of crop expansion, presence of WBR and the way it is governed, the lack of support to pastoralists, and the increasing shift of pastoralists' lifestyle into one of settled crop farmers. Pastoralism is under threat and its survival depends on the successful implementation of policies to support pastoralists and protect grazing lands.