CONTEXT: Assessing the impact of climate change on extensive pasture-based beef production across varied agro-ecological regions is crucial for designing customized adaptation measures. OBJECTIVE: This study assesses the effects of climate change on extensive pasture-based beef production systems in three South African agro-ecological regions (Bloemfontein, Phalaborwa and Buffalo Berlin) under two climate change scenarios, namely the representative concentration pathways (RCPs) 4.5 and 8.5. METHODS: The LiGAPS-Beef model, previously calibrated for the region, was used to evaluate the impact of climate change on beef cattle production under pasture-based extensive systems. Four breed types, namely Bos taurus, Composite, Zebu indicine and Sanga cattle were included in this study. Genetic parameters for each breed were obtained from SA Stud Book, Livestock Registering Federation (LRF) and literature. Measured historical weather data was obtained from the South African Weather Service for the three agro-ecological regions. An ensemble of eight regional climate model (RCA4) simulations from the CORDEX Africa initiative was used to generate future climate change projection data for the period 2036-2065 under RCP 4.5 and RCP 8.5 scenarios. The future nutritional composition data for forage was collected from studies that simulated and predicted future forage quality under climate change conditions. RESULTS AND CONCLUSION: The study found that the baseline average daily gain (ADG) was significantly higher (0.40 kg/head/day) than the simulated RCP 4.5 (0.21 kg/head/day,-48 %) and RCP 8.5 (0.20 kg/head/ day,-51 %) ADGs regardless of breed type when both feed quality and feed quantity limited growth. Although the effect of the climate change scenarios on beef production was agro-ecological region dependent, the performance of Bos taurus declined more than other breeds under future climate scenarios while the Sanga and the Composite types were the most resilient, especially in hot climate areas. Model simulations predict that future climate change will have a greater negative impact on cattle in Buffalo Berlin and Phalaborwa, while those in Bloemfontein will be least affected. The study also highlights that under future climate change scenarios, pasture quality will be the key factor influencing cattle growth in Bloemfontein and Buffalo Berlin, while pasture quantity will be the dominant factor in Phalaborwa if stocking rates remain unchanged. The study highlights the need for nutritional and pasture management interventions for pasture-based extensive system (e.g., feed supplementation, adjusting the stocking rate to match pasture availability, identifying and integrating drought and/ or heat tolerant ecotypes, fodder trees that provide shade for the animals) to mitigate the expected decline in beef cattle performance in South African agro-ecological regions. SIGNIFICANCE: Quantifying the impact of anticipated climate change on pasture-based extensive beef production and identifying specific factors that limit beef production per breed type in the different agro-ecological regions is crucial for assessing the potential ramifications on beef production. This information empowers farmers and policy makers to develop targeted mitigation and adaptation strategies that promote resilience of the beef production system in the respective regions.

Dairy production is a major source of global greenhouse gas (GHG) emissions, especially non‑carbon dioxide (CO2) emissions. Climate impact assessments of dairy farming need to account for differences between short- and long-lived GHGs, and for the net contribution of dairy production to human food supply. Based on survey data from 48 confined dairy farms in Henan, China, we found that in the production of one kg of human-edible protein (HEP) in milk and meat, an average of 18.1 kg CO2, 1.16 kg methane (CH4) and 0.03 kg nitrous oxide (N2O) were emitted. Results from a reduced-complexity climate model showed that, under constant emission rates, the near-term warming caused by Henan dairy production was largely dominated by CH4 emissions (from 84 % in Year 1 to 55 % in Year 100), while long-term warming was mostly dominated by CO2 emissions (from 11 % in Year 1 to 56 % in Year 500). Reduction of CH4 emissions on these dairy farms is essential in the coming years given the urgency to tackle climate change, but full decarbonization of CO2 emissions along the dairy chain -including those from energy used during upstream processes such as feed production and processing -is vital to limit global warming in the long run. In addition, 1.5 kg of HEP in cattle feed was used to produce 1 kg of HEP in milk and meat. Using these 1.5 kg of HEP for human consumption directly would therefore save 0.5 kg of HEP, and prevent the emission of an additional 12.6 kg CO2, 1.15 kg CH4 and 0.02 kg N2O. Results indicate the high cost of the dairy systems in terms of both climate change and food security, and could help to guide climate actions in the Chinese dairy sector while considering food security objectives.

Enhancing nitrogen (N) circularity is crucial to mitigate the environmental impacts of N losses in food systems. Substance flow analysis (SFA) effectively assesses N flows, but its application to evaluating food system circularity in China remains limited. We used a SFA model of food system with detailed representation of animals and waste in the North China Plain, an agricultural-intensive area, to assess eight circularity indicators. Findings revealed that the area imported 49 % of feed N yet maintained food N self-sufficiency by producing 110 % of consumed food N. Nitrogen Use Efficiency was 19 %, with 56 % of waste N recycled, contributing half and one-third of fertilizer and feed N inputs. Furthermore, circularity performance varied among prefecture-level cities, with better outcomes in agriculturally active, less populated, and less urbanized areas. We illustrate SFA's value in assessing circularity in Chinese food systems while advocating for improved model accuracy and complementary indicators, emphasizing tailored strategies.

Sustainable forest management in South-East Asia is challenged by smallholder livestock husbandry as farmers supplement insufficient on-farm forage with resources extracted from forests. This study assesses which determinants affect forest resource extraction by investigating teak forest usage by Indonesian cattle farmers. Based on a survey of 600 smallholders, we provide an overview of which resources are extracted, assess the factors influencing the likelihood of becoming a forest user group member and analyse characteristics that differ between members and non-members as well as the effect of membership on extraction frequency. Almost half of the farmers collect animal forage and heating material from teak forests or farm forestland. Two thirds of farmers who extract resources are not institutionally organized. Increasing distance to forests as well as diversity of extraction are found to be related with increased odds for highest extraction frequency. Group members differ from non-members mainly in the number of resources extracted as well as the usage of forest grass as a main forage source. Farmers are more likely to be group members with increasing farm size and poverty levels. Socio-economic benefits obtained from group membership should be redesigned so that more smallholders are motivated to join this governance scheme. If sustainable forest management schemes are scaled up to agroforestry-based agricultural intensification, a broad set of national and international benefits could be realized.

Breeding is a promising greenhouse gas (GHG) mitigation option for the dairy sector that offers potential permanent and cumulative effects. However, there is limited understanding of how genetic traits affect GHG emissions from the dairy production chain and how breeding indices could be used to find a balance between GHG emissions and farm profit. Using a typical Chinese dairy farm as a case study, we developed a novel method to address these gaps. The farm comprised of 1523 Holstein-Friesian dairy cows and 1429 young stock. The average milk yield at the farm was 11,533 kg per cow per year. Life cycle assessment was combined with an existing bio-economic model to determine the emission intensity values (IV) of six genetic traits: milk yield, protein yield, fat yield, calving interval, productive life, and incidence of clinical mastitis. The IVs and economic values of the traits were used to form different breeding indices, of which the economic and environmental consequences were assessed. Results showed that for the next generation, breeding animals with optimal indices could reduce carbon dioxide equivalents per ton of fat-and-protein-corrected milk by six to 10 kilogrammes, while increasing profitability by 822 to 1355 Chinese Yuan per cow unit. Different indices can balance farm profit and GHG emissions to different degrees. However, the indices with higher profit showed less potential in reducing GHG emissions. This study provides insights into how breeding strategies could contribute to GHG mitigation in the dairy sector.

CONTEXT: Nitrogen (N) and phosphorus (P) imbalances from dairy farming systems (DFSs) lead to environmental problems, such as eutrophication. OBJECTIVE: This study aimed to quantify nutrient deficits and losses from DFSs with different manure management systems (MMSs) at the farm level and at the levels of its sub-systems. METHODS: We compared NP balances of 30 farms with four different MMSs: applying manure directly on forage land, without treatment (ADL), selling or exporting manure (SEL), using manure for anaerobic digestion (ADI), and discharging manure (DIS). NP balances were calculated based on differences between in- and outflows. RESULTS AND CONCLUSIONS: Results showed that N balances at DFS averaged 222 kg N farm(-1) yr(-1) and did not differ between MMSs. Average P balances at DFS differed between MMSs; balances were highest for DIS (83 kg P farm(-1) yr(-1)), and lowest for SEL (-25 kg P farm(-1) yr(-1)). Soil P balances did not differ between MMSs and were mostly negative, except for four ADL farms. Annually, all dairy farms in Lembang region are estimated to cause a nutrient loss of similar to 1061 tons of N and similar to 290 tons of P, and extract 8 tons of P from soils. Overall, high NP imbalances are caused by discharging manure into the environment. SIGNIFICANCE: To reduce imbalances, collection and on-farm use of manure must be improved, and excess manure needs to be sold to crop farms. The carrying capacity for high-input high-output dairy farming is determined by the capacity of arable farms to apply the manure surpluses.

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Quantifying the performance of beef cattle in diverse agro-ecological regions with different climatic conditions could be used by stakeholders to develop region-specific resilience strategies and optimize livestock production systems. This study evaluated the ability of the mechanistic LiGAPS-Beef model as a tool to quantify beef production of selected cattle breed types in diverse agro-ecological regions in South Africa. The average daily gain (ADG) was simulated for Bos taurus, composite and Sanga breed types in three different agro-ecological regions i. e. Bloemfontein (semi-arid), Phalaborwa (semi-desert) and Buffalo Berlin (temperate oceanic). Simulated ADGs were compared to measured ADGs from eight experiments for calibration and validation. After calibration, the model simulated ADG of breed types with a mean absolute percentage error (MAPE) of 23%. Simulated and measured values were positively correlated (r = 0.88) and largely in agreement (index of agreement=0.92). Factors that define and limit growth, such as the genotype, heat stress, cold stress, and digestion capacity limitation, were identified. Consistent with literature, the model showed more heat stress on Bos taurus breed types than on composite and Sanga breed types in Phalaborwa, which was the warmest region included in the study. Sensitivity analysis by changing input parameters by & PLUSMN; 10% showed that the model was more sensitive to changes in metabolizable energy (ME) than crude protein (CP) implying that the accuracy of ME must be prioritised. Overall, the results showed that model performance was adequate and thus the LiGAPS-Beef model can be used to explore the effects of climate change and adaptive breeding strategies in future studies in South Africa.

CONTEXT: The livestock sector in Sub-Saharan Africa (SSA) is under increasing pressure to define its role in jointly addressing food security and climate change. Climate-smart agriculture (CSA) has been widely leveraged as an approach to achieving both food security and climate change outcomes through suites of interventions that maximize synergies and reduce tradeoffs among three pillars: productivity, climate change resilience, and climate change mitigation. However, operationalization of the CSA approach in the livestock sector is hindered by a lack of clarity around what the pillars mean for livestock systems, given their fundamental attributes compared to crops and the spatial and temporal dimensions of these attributes. A conceptual framework is also lacking for assessing and comparing the potential CSA synergies and tradeoffs that different livestock systems and interventions may generate.OBJECTIVE: In this paper we aim to offer guidance on the operationalization of the CSA approach in the livestock sector.METHODS: We draw on a literature review to explore the essential attributes of livestock systems in SSA as they relate to CSA objectives over different temporal and spatial scales. Based on this review, we propose a practical and flexible framework for assessing and comparing the synergies and tradeoffs that different livestock systems may generate among food security and climate change objectives over different spatial and temporal scales. The framework consists of four elements: CSA pillars, spatial-temporal scales, CSA objectives mapped to each spatialtemporal scale, and indicator guidance. Using farm survey data and national statistics, an illustrative application of the framework to two dairy farms in Rwanda is presented and discussed.RESULTS AND CONCLUSIONS: The illustrative application demonstrates how the framework can be used to identify important, spatial-temporal CSA synergies and tradeoffs that otherwise may go unrecognized. Additional applications are needed to assess the utility, practicality, and potential of the framework to guide CSA operationalization in the livestock sector in SSA.SIGNIFICANCE: Maximizing synergies and reducing tradeoffs among food security and climate change outcomes in the livestock sector is critical for a sustainable food future. With an emphasis on flexibility for tailoring to specific development contexts and compatibility with varying levels of data availability and methodological complexity, the framework is intended to support diverse stakeholders involved in policy and development seeking to identify those livestock systems that contribute most to food security and climate change objectives over time and space.

The demand for chicken meat and eggs exceeds what can be produced in Tanzania, largely due to low productivity of the sector. Feed quantity and quality are the major factors determining the potential production and productivity of chickens. The present study explored the yield gap in chicken production in Tanzania and analyses the potential of increased chicken production as a result of closing the feed gaps. The study focused on feed aspects limiting dual-purpose chicken production in semi-intensive and intensive systems. A total of 101 farmers were interviewed using a semistructured questionnaire and the amount of feed provided to chickens per day was quantified. Feed was sampled for laboratory analysis and physical assessments were made of weights of chicken bodies and eggs. The results were compared with the recommendations for improved dual-purpose crossbred chickens, exotic layers, and broilers. The results show that the feeds were offered in insufficient quantity compared with the recommendations for laying hens (125 g/chicken unit/d). Indigenous chickens were fed 111 and 67 while the improved crossbred chickens were fed 118 and 119 g/chicken unit/d under semi-intensive and intensive systems, respectively. Most feeds fed to dual-purpose chickens were of low nutritional quality, particularly lacking in crude protein and essential amino acids in both rearing systems and breeds. Maize bran, sunflower seedcake, and fishmeal were the main sources of energy and protein in the study area. The study findings show that the important feed ingredients: protein sources, essential amino acids, and premixes were expensive, and were not included in formulating compound feeds by most chicken farmers. Of all 101 respondents interviewed, only one was aware of aflatoxin contamination and its effects on animal and human health. All feed samples contained a detectable concentration of aflatoxins and 16% of them exceeded the allowed toxicity thresholds (>20 µg/kg). We highlight the need for a stronger focus on feeding strategies and ensuring the availability of suitable and safe feed formulations.

The objective of this study was to examine social networks in dairy value chains (DVCs) in Kenya and understand how DVC actors’ power relationships and trust influence their behaviour regarding milk quality. We conducted a stakeholder analysis using the Net-Map tool in Laikipia, Nakuru and Nyandarua counties in Kenya. VisuaLyzer software was used to analyse the social networks. Thematic content analysis of the discussions, recorded during the mapping exercise, was undertaken using ATLAS.ti. Formal DVC had more actors and dense social networks characterised by vertical and horizontal integration, high levels of power asymmetries between actors, limited trust and short-term contractual arrangements. Informal DVC was characterised by fewer actors and less dense social networks, low levels of power asymmetries between actors and a high level of trust due to the existence of reciprocal personal relationships. Milk was perceived to be of higher quality in the formal value chain reflecting top-down enforcement of milk standards, bottom-up collective action, power asymmetries and contractual relationships. Poor milk quality management in the informal DVC underscores the need for powerful actors, e.g. regulatory agencies, and buyers such as processors, to influence other DVC actors’ behavioural change. Understanding and leveraging DVC social networks and actors’ power and addressing power asymmetries and enhancing trust between actors will increase compliance with milk quality standards. There is an urgent imperative to design policies and interventions which empower DVC actors, by providing economic incentives, enhancing their skills and knowledge and their access to infrastructure which facilitates milk quality improvement.

The understanding of the role of using forest resources in the livelihood strategies of smallholder farmers is limited. Rural household surveys often omit this aspect. From a survey of 600 Indonesian cattle farmers, we apply the sustainable livelihood framework to investigate the role extracting forest resources has in livelihood strategies and household income. We also quantify which farmers’ characteristics impact the decision to extract them. Forest extraction appears a core livelihood strategy of farmers who rely in some way on forests, which are on average poorer. Our findings suggest that forest extraction increases with increased income diversification. Farmers who specialize as feeders in the cattle supply chain engage significantly less in that. The promotion of sustainable forest resource usage schemes, such as agroforestry or silvopastoral systems that facilitate, and support cattle breeding would maintain the supply of youngstock for feeders and contribute to sustainable future use of forest resources.

In this study, we aimed to analyse the strengths and weaknesses of land reform (LR) farms (‘farm issues’) because the lack of understanding of these issues could partly be the cause of limited livelihood gains from these farms. Furthermore, the examination of these issues provided specific recommendations to each group of farms, which could improve LR farmers' livelihood gains. We investigated 50 LR farms in the Waterberg District Municipality (WDM), Limpopo Province, in South Africa. A survey was conducted, literature about the consequences of climate change and prospects for financial support from the state was reviewed, and a stakeholder workshop was organised. We used Fisher-Freeman-Halton exact test to analyse survey data and conducted strengths, weaknesses, opportunities and threats (SWOT) analysis to explore strategic issues. Farms were classified into 3 groups based on their SWOT issues: A-better-off farms dominated by extensive land uses (n = 22), B- better-off farms dominated by intensive land uses (n = 24), and C- poor farms dominated by either extensive or intensive uses (n = 4). Farmers' social class, conditions of farm physical capital endowment, and the characteristics of the land use activities determined the strengths and weaknesses of LR farms. Strengths of group A farms were good physical capital and less external financial support requirements, and weaknesses were that partnerships were unlikely and family labour was costly. The distinct strength of group B farms was that partnerships were likely, and their high need for external financial support was a distinct weakness. Group C farms had distinct strengths in that family labour was cheap, and insufficient physical capital was their weakness. Acknowledging the diversity in strengths and weaknesses of farms is essential for land reform to play a critical role in rural development. We envisage that financial support from the state, will yield improved production in poor farms. Private investors will yield high production in the better-off farms only if farmers adopt climate-smart agricultural practices.

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 pol-lution 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,nitro-gen 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 deter-mination(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 vali-dation.These modelswill 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.

Intensification of agriculture in India has increased food self-sufficiency. However, it has also led to unwanted environmental impacts, particularly the increased pressure on groundwater resources. These impacts are most severe in the dryland regions of the country. Therefore, this paper aims to understand the impact of intensified forms of agriculture on the availability of water resources in a dryland watershed in Telangana, India. To achieve this, we first assessed the water use of three main farming systems in the study region. We then calculated the water balance at the watershed level to understand the agricultural impact on groundwater availability within the watershed. The three farming systems studied were the crop without livestock system (CWL; 48% of households), the crop-dairy system (CD; 38% of households), and the crop with small ruminants system (CSR; 6% of households). The results indicated that the CD system used the highest quantity of water (19,668 m3/household/y), followed by the CSR (8645 m3/household/y) and CWL (4403 m3/household/y). CWL and CD systems comprise 86% of the households, making these systems the largest water users. Finally, the water balance of the whole watershed showed a deficit of – 13.9 Mm3/y. Cultivation of water-demanding non-dryland crops, increased specialization of farming systems, and management practices in current farming systems are the factors causing over-utilization of water and subsequent groundwater depletion. We also realize that the current policy environment and other drivers such as decreasing landholdings and market forces, also induce increased water use in production. We, therefore, conclude that there is a need to promote agro-ecologically suitable farming strategies, improve the existing technological options and introduce new policies that reduce the over-use of water resources for sustainable agricultural production in dryland regions.

Dairy farming may have negative impacts on the environment, such as ammonia emissions to air and nitrate leaching to water. By selecting more efficient cows, the amount of nitrogen excreted per cow and consequently the adverse environmental impacts will be reduced. The aim of this study was to predict nitrogen use efficiency (NUE) of individual dairy cows using mid-infrared (MIR) spectra of milk. A total of 600 feeding and MIR records of 56 Holstein cows were collected from a farm in Beijing, China. NUE was calculated as the ratio of nitrogen in milk to nitrogen intake. The coefficient of determination of the best model was 0.69, 0.62 and 0.70 for NUE, nitrogen loss and dry mater intake, respectively. The MIR wavenumbers around 981.00 and 1506.93 cm-1 and daily milk yield were the most important variables for prediction. These results show potential for large-scale genetic evaluations of nitrogen efficiency.

This longitudinal study explored intra-annual variation in feed availability and the chemical composition of milk and feed resources at smallholder dairy farms in Nakuru county, Kenya. Feed and milk samples were collected for a full year, every last week of the month, from 43 purposively selected farms. Feed and milk samples were analysed for nutritional composition using near infrared spectroscopy (NIRS) and Ekomilk milk analyser, respectively. The main basal feeds were indigenous grasses, Napier grass, maize and bean stover and maize silage, which farmers supplemented with purchased commercial concentrates and/or purchased or homemade total mixed rations (TMR). Commercial concentrates had the highest crude protein (CP) content (17.4 +/- 3.9)% dry matter (DM), while maize stover had the lowest (8.7 +/- 3.3% DM). All the feeds had low metabolisable energy (ME) that ranged from 7.0 +/- 0.8 (MJ/kg DM) megajoules per kilogram of dry matter (MJ/kg DM) for maize stover to 8.9 +/- 0.8 for dairy meal. Only grasses showed significant seasonal variation in CP and NDF (P > 0.00). Milk physicochemical composition was within the range stipulated by the Kenya Bureau of Standards (KEBS). Milk physicochemical composition showed negligible seasonal variations to significantly affect milk processing, which suggests that farmers can cope with feed scarcity. Nevertheless, seasonal feed availability is a persistent challenge in smallholder dairy farms. There is a need to ensure sufficient feed availability throughout the year in smallholder dairy farms through feed conservation, feeding management and ration preparation to enable consistent milk production and physicochemical composition.

The poultry industry in Tanzania has grown steadily over the past decade. We surveyed 121 chicken farming households along an intensification gradient from backyard to semi-intensive and intensive production systems based on rearing system and assumed purpose and poultry breed in the Iringa region. About 30% of households had more than one breed and/or rearing system combination. The subdivision of poultry systems was refined by adding the size of the flocks to highlight variation in scale of operations. On this basis we distinguished 3 main types: 1) subsistence small-scale free-range chicken production; 2) market-oriented small to medium scale semi-intensive and 3) small to medium-large scale intensive systems. 'Intensification' involves the transition from keeping indigenous chickens to improved dual-purpose and exotic breeds driven by greater productivity and potential for income generation. The more intensive the production system, the more the intensity and diversity of diseases identified by farmers as their main problem, which was partly attributed to the greater sensitivity of the improved breeds, poor veterinary measures, and the high chicken density facilitating disease spread. Based on the survey we constructed a problem tree to classify the underlying constraints and their interrelations, and to identify common root causes, based on which we propose practical solutions to improve chicken production. Development of medium-large scale systems is particularly constrained by a limited supply of 1-day-old chicks and theft. By contrast, intensification of small-scale systems is constrained by limited access to quality feed, vaccines and medicines, capital, and lack of a reliable market, partly due to the absence of farmer organization. These constraints can be addressed through formation of producer groups and promotion of outgrower and enterprise development models. Enterprise development appears to be the most promising business model for smallholder chicken farmers given that it allows farmers more freedom in decision-making and management while strengthening linkages with input suppliers and output markets to ensure a viable and profitable business.

Accurate and early identification of the likelihood of conception (LC) in cows is imperative for a profitable dairy farm. This study aims to use the milk mid-infrared (MIR) spectra in different intervals before the first insemination and partial least squares discriminant analysis (PLS-DA) to predict LC. The results show that the MIR data within 30 to 50 d after calving and close to insemination had a better prediction in LC (accuracy = 73.9 and 72.3%) than 0 to 30 d. And specificity (74.4 to 84.9%) was higher than sensitivity (67.8 to 73.1%). Once the expected date of insemination is given, the model can predict LC before the actual insemination, and intervene in advance for cows predicted poor LC such as delayed insemination and treatment can be initiated. The predicted LC also provide a novel and convenient way to accumulate extra reproductive phenotypes for genetic evaluation.

Smallholder farmers in developing countries often lack resources. They rely mostly on extensive production approaches, such as cattle keeping and resort more to extracting forest resources at no charge. Our objective is to assess the relationship between the diversification of income sources, poverty and livelihood capital for smallholder farm households which combine cattle farming with forest extraction. We collected 600 surveys from Indonesian farmers specialized along the cattle rearing supply chain (464 breeders, 66 feeders and 70 mixed breeder-feeders). We found no correlation between poverty and income diversification. Cattle breeders have been found to rely most on forest resources. Distance to cropland and forest correlated positively, whereas their education level correlated negatively with income diversification. Feeders who were owning other livestock, were a member of a forest user group and owned some modest capital like a motorbike showed increased income diversification. Crops are the most important source of income for farmers, whereas cattle keeping and forest extraction play a role in income diversification. Increasing ecological pressure caused by forest extraction due to expanding cattle production could be best avoided by extending those parts of the cattle sector that use forest resources in a sustainable manner, for instance, through silvopastural systems or agroforestry so that incomes of poor farmers get more diversified and, therefore stabilized.