Data is always the bottleneck of assessing greenhouse gas (GHG) emissions in smallholder dairy farms. More data is needed to understand variation in farming practices adopted by smallholder dairy farmers due to seasonality. With more accurate and precise data of when farmers use which practices, policymakers and practitioners can refine interventions with the greatest potential to reduce GHG emissions on smallholder dairy farms according to key localities and seasonal practices.

However, collecting this data is time-consuming and labor-intensive. Often, GHG emission assessment applies a cross-sectional approach, or data collection at one moment in time. These approaches cannot capture the variation of farming practices throughout the year. Therefore, researchers from the Animal Production Systems group at Wageningen University (WUR), supported by the CGIAR Research Program on Climate Change, Agriculture and Food Security, conducted a study to assess seasonal differences in GHG emissions from Indonesian dairy farms, and evaluate the implications of the number of visits per farm on the variation of the estimated GHG emissions.

dairy cow stalls in Indonesia

A smallholder dairy farm in Indonesia

Seasonal GHG emission variability

The tropical climate in Indonesia has two distinctive seasons: rainy and dry. To evaluate the impact of farming practices on GHG emission assessments, we compared GHG emissions between the two seasons. We found that the GHG emission per unit of milk was significantly higher in the rainy season than in the dry season. The contributions of GHG emissions from different farming processes determined the GHG emission estimates in both seasons. The processes were categorized into four groups:

  1. Enteric fermentation
  2. Manure management
  3. Forage cultivation
  4. Purchased feeds

Among all processes, enteric fermentation was the largest contributor to GHG emissions per unit of milk from Indonesian smallholder dairy farms. Enteric fermentation is a digestive process in cattle and other ruminants by which carbohydrates are broken down by microorganisms, releasing methane through burps. Feed plays a major role in enteric fermentation. Highly digestible feeds can reduce methane emissions from enteric fermentation.

Our study revealed that Indonesian smallholder dairy farmers increase the amount of concentrate and rice straw in the dry season to compensate for the low availability of elephant grass. This strategy improves feed digestibility, hence reducing enteric fermentation. Although feeding concentrate is favorable to digestibility, this practice should be applied cautiously to avoid feed-food competition when the ingredients of concentrate are edible for humans. However, this is not the case in Indonesian smallholder dairy farms because the concentrate for dairy cattle is mostly made from wheat pollard and rice bran, by-products of the milling industry.

Manure management was the second-largest contributor to GHG emissions in this study. It includes methane and nitrous oxide emissions from manure collection for biogas production and application on the land. The GHG emission from manure management in the rainy season was higher than in the dry season. We also observed that the farmers collected less manure in the dry season. Consequently, manure from the farms ends up in water bodies. In this case, GHG emissions from manure in water is very low, but this practice leads to other environmental burdens such as leaching.

Another difference in manure management across seasons is higher manure collection for biogas production in the rainy season than in the dry season. This practice leads to higher methane emissions related to biogas losses. We also observed more manure application to grow elephant grass in the rainy season. Simultaneously, the farmers also apply inorganic fertilizer in the rainy season as an attempt to maximize yield during the high rainfall. Therefore, over-fertilization increases GHG emissions in the rainy season (Figure 1).

''''Figure 1. GHG emissions per unit of milk (kg CO2-eq kg-1 FPCM) produced by Indonesian smallholder dairy farms in the rainy and the dry season (adopted from Table 3 Apdini et al. 2021).

DIVE INTO DETAIL: How the number of farm visits affects GHG emission estimates

To understand the relation between the number of farm visits and variability, or differences, in estimated GHG emissions per unit of milk, we analyzed the total variance in both seasons. The total variance consists of the between and within farm variance. The between farm variance refers to a systematic difference in GHG emissions estimates across farms, while the within farm variance refers to differences in GHG emissions estimates across visits to the same farm. The within farm variance provides insight about the performance of an individual farm (e.g., compared to another farm, or overtime) and variability of a specific farm population (i.e., 32 farms in this study).

The analysis showed that the within farm variance of the estimated GHG emission per unit of milk for a single farm mean and population mean was higher than the between farm variance. This indicates that the farms in our study are rather homogenous in terms of estimated GHG emission per unit of milk. Increasing the number of visits per farm could provide more precise estimates. We assumed a weekly visit (i.e., 26 visits in each season) and analyzed the variances. As a result, we observed a decrease within farm variance in both seasons. Furthermore, a higher value of the within farm variance in the rainy than the dry season points out a need to collect more data in the rainy season.

We investigated the variability of GHG emissions from different processes because those contribute to the estimate of GHG emission per unit of milk. The largest between farm variance was observed in the estimated GHG emissions from manure management. It can be explained by a large variation in the size of land and yield, and in the quantity of manure and fertilizer applied to grow forage. In the case of within farm variance, enteric fermentation had the largest variance among all processes. This is related to changes in diet composition over time as a consequence of the availability of forage throughout the year.

Recommendations for future data collection

This study gives insight into the variability of farming practices and GHG emissions across seasons in smallholder dairy farms. Based on these findings, the authors recommend implementing intensive recording systems to collect data at smallholder dairy farms to improve the assessment of GHG emissions throughout the seasons.

Increasing our understanding of how seasonal practices and data collection techniques effect the variability of GHG emissions can render a more complete story of what contributes to GHG emission levels and mitigation strategies.

Titis Apdini is a PhD Candidate in Animal Production Systems, Wageningen University. She is also a former CLIFF-GRADS Fellow where she worked at Bangor University on developing a marginal abatement cost curve (MACC) to evaluate mitigation strategies as one of the objectives of 'Sustainable futures for the Costa Rica dairy sector' (SusCoRiDa) project.