Dairy Farm Manure Management

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ISBN 13 : 9789463959988
Total Pages : 180 pages
Book Rating : 4.9/5 (599 download)

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Download or read book Improving Manure Management at Smallholder Dairy Farms in Indonesia written by and published by . This book was released on 2021 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: As a response to a high demand for milk and low national milk supply, the Indonesian government aims to increase national milk production by, among others, increasing the dairy cattle population. This will have consequences especially for manure production. Manure is an inevitable by-product of dairy production and has a number of benefits if it is appropriately managed, but can also cause environmental impacts when high manure production is followed by improper manure management. To avoid these adverse environmental impacts, manure needs to be managed appropriately. Smallholder dairy farms in Indonesia, however, are currently characterized by poor manure management, and with the expected increase in manure production, the importance of improving manure management is increasing. Improving manure management on smallholder farms involves many aspects, such as feed management, land for storing and applying manure, and costs associated with manure management. Knowledge about many of these aspects is lacking. The overall aim of the studies in this PhD thesis was to evaluate emissions to the environment associated with manure management and to identify improvement options on smallholder dairy farms in Indonesia. To this end, the studies in this PhD thesis analysed various aspect of manure management at different aggregation levels (i.e., the animal, farm, regional, and value chain level). At the animal level (Chapter 2), the models to accurately predict N-P excretion of dairy cows on smallholder farms in Indonesia based on readily available farm data were developed. The model predicted actual nutrient excretions with reasonable accuracy. The total N excretion of dairy cows in Indonesia was on average 197 g animal-1 d-1, whereas P excretion was on average 56 g animal-1 d-1. At the farm and regional level (Chapter 3), nutrient balances from dairy farming systems with different manure management systems (MMSs) were analyzed. Furthermore, nutrient balances from farm level were upscaled to regional level to determine the sector’s contribution to the pollution of the Citarum river and to identify potential options for improvement. Results showed that the N balances of all 30 dairy farms averaged 222 kg N farm-1 yr-1 and did not differ between MMSs. The P balances of the farms differed between MMSs; balances were highest for farms that discharge manure (83 kg P farm-1 yr-1) and lowest for farms that sell or export manure (-25 kg P farm-1 yr-1). Annually, all dairy farms in the Lembang region caused a loss of 1,061 tons of N and 290 tons of P into the environment and they extracted 8 tons of P from soils. At the farm and value chain level (Chapter 4 and 5), greenhouse gas emission (GHGE) at the value chain level by means of life cycle assessment (LCA) was estimated. Chapter 4 assessed seasonal differences in GHGE from Indonesian dairy farms by means of longitudinal observations and evaluated the implications of number of farm visits on the variance of the estimated GHGE per kg milk (GHGEI) for a single farm mean, and for the population mean. Results showed that GHGEI was higher in the rainy (1.32 kg CO2-eq kg-1 FPCM) than in the dry (0.91 kg CO2-eq kg-1 FPCM) season. The between farm variance was 0.025 kg CO2-eq kg-1 FPCM in both seasons. The within farm variance in the estimate for a single farm mean and the population mean decreased with an increase in number of farm visits. Variability in GHGEI can therefore be reduced by increasing the number of visits per farm. Forage cultivation was the main source of between farm variance, enteric fermentation the main source of within farm variance. Chapter 5 identified mitigation strategies of GHGE at smallholder dairy farms. The relationship between GHGEI and milk yield per cow for all farms was modelled and farms with an GHGEI below and above their predicted GHGEI were compared (‘low’ and ‘high’ GHGEI farms). Results showed that milk yield explained 57% of the variance in GHGEI among farms. Low GHGEI 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 GHGEI farms. In addition, low GHGEI 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 GHGEI farms. At the farm and regional level (Chapter 6), the constraints on manure management on smallholder dairy farms and potential opportunities for improvement were identified. There are 20 constraints on manure management, of which availability of space to store manure on the farm, and costs of manure management are regarded most important. Stakeholders proposed strategies to improve manure management: communal manure storage (CMS), a structured manure market, and providing economic and institutional support such as access to credits and financial incentives for good manure management. The cost of manure management was high, and farms that sell or export manure, and farms that have a bio-digester had higher net total cost than farms that discharge manure. Total revenue (TR) differed between manure management systems and farms that apply manure had lower TR than farm that sell or export manure. All MMSs had negative net gross margins which could be explained by the high costs attributed to labour (i.e., family labour) and low revenue from manure. In Chapter 7, the methodological issues of the study, including the scope of the models and the method of data collection were discussed. Chapter 7 integrates the knowledge gained in the various studies and identifies a series of improvement options that connect the aggregation levels animal, farm, region, and value chain. It further suggests ways to create an enabling environment required to implement and effectuate the improvement options.