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In the discourse about the development of farmed animal production (terrestrial livestock production and aquaculture) in the tropics, two important food system outcomes emerge: (1) to supply animal-sourced food (ASF) at a level that suffices healthy future diets, including for poor people, and (2) to contribute to climate change mitigation and minimize pollution with nitrogen and phosphorus. Livestock production and aquaculture contribute to food security directly by increasing producers' food diversity and availability, but also that of urban consumers, and indirectly through income generation and increased farm resilience. Recently, circularity has come to the fore as an integrated approach to food system development. Circularity has four cornerstones: (1) food crops have highest priority (which implies no food-feed competition), (2) avoid losses, (3) recycle waste and (4) use animals to unlock biomass that humans cannot eat. In this review, the role of farmed animals in circular food systems in the tropics is presented in four case studies and the impacts of circularity on food security and environmental impact mitigation are discussed. The cases are ruminants in grazing systems in West Africa and in Colombia, fish in pond aquaculture in general, and land-limited dairy production in Indonesia. Additionally, options for novel protein sources for use in livestock and fish feeding are presented. It is concluded that farmed animals are important in circular food systems because of their use of land unsuited for crop production, their upgrading of crop residues, and their supply of manure to crop production. Nevertheless, the increasing demand for ASF puts pressure on important characteristics of circularity, such as minimizing food-feed competition, maximization of use of waste streams in feed, and the value of manure for fertilization. Hence, in line with conclusions for Western countries, maximum circularity and sustainability of food systems can only be achieved b

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.

Poor milk safety constitutes a persistent public health risk in Kenya. Poor milk composition, microbial contamination and adulteration is a constraint to dairy sector development. We hypothesise that variation in milk quality and safety depends on variation between farming systems. We argue that this variation between farming systems is associated with spatial location which affects the agro-ecological conditions and the avail-ability of labour and land. We used a spatial framework based on the distance to urban markets to distinguish the following farming systems: relatively intensive dairy systems in urban locations (UL), semi-intensive dairy systems in mid-rural locations (MRL) and extensive dairy systems in extreme rural locations (ERL). We aimed to investigate the variation in the quality of raw milk in these dairy farming systems and associated value chains in central Kenya. For this reason, we combined several methods such as participatory rural appraisal, participant observation, and milk physicochemical and microbiological analyses to collect data. Milk samples were collected at the informal and informal value chain nodes farms, informal collection centres, informal retailing centres including milk vending machines, and formal bulking centres where milk changes hands between value chain actors. Milk quality was compared to standards recommended by the Kenya Bureau of Standards (KeBS). There were no differences in the quality of raw milk between locations or between nodes. The overall milk physicochemical composition means (standard error) of the milk were within KeBS standards: fat 3.61 (0.05), protein 3.46 (0.06), solid-not fats 9.18 (0.04), density 1.031 (0.0002) and freezing point-0.597 (0.019). The protein percentage was below KeBS standards at all value chain nodes, except at the formal bulking node. There was significant contamination of milk samples: 16.7% of samples had added water, 8.8% had somatic cell count SCC above 300,000, 42.4% had E. coli, 4

Milk production is an important livelihood source for smallholder dairy farmers in low-to-middle-income countries (LMICs) such as Kenya. However, milk quality and safety are a challenge due to unhygienic handling and non-adherence to food safety standards. The objective of this study was to investigate the knowledge, attitudes and adoption of milk quality and food safety practices by smallholder farmers in Kenya. Ten Focus Group Discussions (FGDs), involving 71 smallholder farmers, were held to collect qualitative data on knowledge, attitudes and practices (KAPs) of smallholder dairy farmers in Laikipia, Nakuru, and Nyandarua counties. Additionally, data were collected through a cross-sectional administered to 652 smallholder farming households. The results of the study revealed low knowledge level and negative attitudes towards respecting antibiotics treatment withdrawal periods, milk quality standards and food safety regulations. Farmers stated they had received low levels of training on milk quality and safety standards. The majority of farmers adopted animal health measures and hygienic measures such as hand washing and udder cleaning. However, unhygienic milking environments, the use of plastic containers, the use of untreated water, and lack of teat dipping compromised milk quality and safety. Currently, milk production, handling and consumption could expose actors along the dairy value chain to health risks. The adoption of milk quality and food safety practices was influenced by farmers' knowledge, socioeconomic characteristics, and choice of marketing channel. There is a need to improve farmers' knowledge and attitudes and implement hygienic control, disease control and antibiotic residue control practices in the milk production process to meet required milk quality and food safety standards. Awareness campaigns and training programmes for smallholder dairy farmers could foster behavioural change and lead to an improvement in milk quality in Kenya.

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 perfor

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.

Household surveys are one of the most commonly used tools for generating insight into rural communities. Despite their prevalence, few studies comprehensively evaluate the quality of data derived from farm household surveys. We critically evaluated a series of standard reported values and indicators that are captured in multiple farm household surveys, and then quantified their credibility, consistency and, thus, their reliability. Surprisingly, even variables which might be considered ‘easy to estimate’ had instances of non-credible observations. In addition, measurements of maize yields and land owned were found to be less reliable than other stationary variables. This lack of reliability has implications for monitoring food security status, poverty status and the land productivity of households. Despite this rather bleak picture, our analysis also shows that if the same farm households are followed over time, the sample sizes needed to detect substantial changes are in the order of hundreds of surveys, and not in the thousands. Our research highlights the value of targeted and systematised household surveys and the importance of ongoing efforts to improve data quality. Improvements must be based on the foundations of robust survey design, transparency of experimental design and effective training. The quality and usability of such data can be further enhanced by improving coordination between agencies, incorporating mixed modes of data collection and continuing systematic validation programmes.

Increasing milk yield per cow is considered a promising climate change mitigation strategy for small-scale dairy farms in developing countries. As it can be difficult to increase cow productivity, mitigation options beyond this production strategy need to be identified. The aim of this study was to identify entry points for mitigation of GHG emissions in small-scale dairy farms in Lembang Sub-district, West Java, Indonesia. Data on herd composition, productivity, feeding, and manure management were collected in a survey of 300 randomly selected dairy farms. Characteristics of farms with the 25% lowest (<3291 kg milk/cow/y), medium 50% (3291–4975 kg milk/cow/y), and 25% highest milk yields (≥4976 kg milk/cow/y) were compared. Life cycle assessment was then performed to estimate the cradle-to-farm gate GHG emission intensity (EI) of farms. The relationship between EI and milk yield per cow for all farms was modeled and farms with an EI below and above their predicted EI were compared (“low” and “high” EI farms). Results showed that milk yield explained 57% of the variance in EI among farms. Farms with medium and high milk yields were more often specialized farms, fed more tofu waste and compound feed, and had higher feed costs than farms with low milk yields (P < 0.05). Farms with high milk yields also applied less manure on farm land than farms with low milk yields (P < 0.05). Low EI farms had fewer cows, and fed less rice straw, more cassava waste, and more compound concentrate feed (particularly the type of concentrates consisting largely of by-products from milling industries) than high EI farms (P < 0.05). In addition, low EI farms discharged more manure, stored less solid manure, used less manure for anaerobic digestion followed by daily spreading, and applied less manure N on farmland than high EI farms (P < 0.05). Some associations were affected by confounding factors. Farm management factors associated with milk yield and the residual variation in EI were con

Our understanding of food security in sub-Saharan Africa (SSA) has been hampered by limitations in the temporal and spatial representativeness of data. Food balance sheets provide scalable estimates of per capita food availability, but fail to represent food access, stability and their causal linkages. In contrast, rural household surveys represent detailed conditions for one or multiple points in time, but are influenced by survey timing and are often limited in geographical coverage. This study draws on a large sample of rural land-holding households in SSA (n = 6,353) to identify household level food access deficiencies and to understand the associations with rural livelihoods and food sourcing behavior throughout the year. Food access deficiencies were identified using food security of access and diet diversity indicators. Dietary diversity and channel of access (farm or purchased) were enumerated for the “flush” and “lean” periods and food security of access was enumerated for the lean period only - making the results of this study independent of survey timing. As many as 39% of households were classified as severely food insecure (in terms of food access) and as many as 49% of households were likely to be deficient in micronutrients in the lean period. Vulnerability to food insecurity and micronutrient deficiencies differed by household composition, agricultural livelihood characteristics and agro-ecological zone. Dairy, fruit and vitamin A-rich produce were predominantly accessed through the farm channel. Households with a livestock component to their farm had a lower prevalence of severe food insecurity and higher diet diversity scores. These findings have implications for the development of nutrition-sensitive and nutrition-specific interventions. Interventions need to be tailored to agro-ecological zone, household composition, scale of operation and production mix. Increasing income will not necessarily result in improved diet diversity or healthy dietary

National challenges of food insecurity and unemployment in South Africa prompted an increase in expectations for agricultural land acquired through land reform programmes to make meaningful contributions. Embedded in these expectations is the need for understanding the situation in reformed farms. This study reviewed policies and literature on land reform, and analyzed beneficiary participation in reformed farms and the impact of land reform on land use in land restitution and land redistribution farms in the Waterberg District Municipality. Data were collected through individual surveys, key informants interviews and stakeholder workshop. Beneficiary participation levels were significantly lower in restitution farms (18% per farm) than in redistribution farms (65% per farm). The changes in land redistribution policy over time resulted in significant differences in beneficiary participation among land redistribution models, with participation levels increasing with time. Land redistribution model SLAG had the lowest beneficiary participation level (19% per farm) while the latest model PLAS had the highest (100% per farm). The changes in land redistribution policy over time resulted in significant differences in extent of land used among land redistribution models, though the trend was not systemic. On average, redistribution farms under SLAG and LRAD2 models used ≤70% of the farm land, while farms under LRAD1 and PLAS models used more than 90% of the farm land. The research approach used in this study found similar results in beneficiary participation to those in literature where case studies approach was used in restitution farms. On the contrary, in redistribution farms the research approach resulted in findings that differed from case study literature and revealed the needs for representative sample and time if conclusive recommendations were to be reached.