Accuracy of estimates of milk production per lactation from limited test-day and recall data collected at smallholder dairy farms

A review of on-farm recording tools for smallholder dairy farming in developing countries

The dairy industry has been expanding significantly recently, which has prompted the improvement and adoption of increasingly digital dairy recording tools with cutting-edge technology. The study aimed to identify smallholder dairy farmers' recording tools in developing countries. The study presents the results of an extensive literature review conducted using electronic journal databases. The review involved applying a combination of search terms and Boolean operators. The search found a total of 412 research publications. However, only 21 articles with 24 recording tools were deemed appropriate and were subsequently included in the study. Dairy recording entails gathering and managing data on animal information, traceability, health, and productivity that can be conducted using various methods, from manual record-keeping to digitization. The results show that most studies have endeavoured to develop digital recording tools that focus on production performance (PR), mainly milk production, using the Internet of Things (IoT) and mobile phone applications. Moreover, various technologies, such as networks, desktops, and web apps, have also been invented. Given the widespread ownership of mobile phones among the general population, the use of mobile phones continues to be an appealing choice for recording tools. To enhance the advancement of these tools, it is necessary to address technological obstacles, particularly those associated with access and connectivity. In addition, it is also important to consider the continuity of data input and feedback obtained to the farmers, thus helping them evaluate their farms periodically.

Enteric methane emission factors of smallholder dairy farming systems across intensification gradients in the central highlands of Ethiopia

BACKGROUND

Following global pledges to reduce greenhouse gas (GHG) emissions by 30% by 2030 compared to the baseline level of 2020, improved quantification of GHG emissions from developing countries has become crucial. However, national GHG inventories in most Sub-Saharan African countries use default (Tier I) emission factors (EFS) generated by the Intergovernmental Panel on Climate Change (IPCC) to estimate enteric CH4 emissions from animal agriculture. The present study provides an improved enteric CH4 emission estimate (Tier II) based on animal energy requirements derived from animal characteristics and performance data collected from about 2500 cattle in 480 households from three smallholder farming systems to represent the common dairy farming in the central highlands of Ethiopia. Using average seasonal feed digestibility data, we estimated daily methane production by class of animal and farming system and subsequently generated improved EF.

RESULTS

Our findings revealed that the estimated average EF and emission intensities (EI) vary significantly across farming systems. The estimated value of EF for adult dairy cows was 73, 69, and 34 kg CH4/cow/year for urban, peri-urban, and rural farming systems, respectively. Rural dairy farming had significantly higher emission intensity (EI) estimated at 1.78 CO2-eq per kg of fat protein-corrected milk (FPCM) than peri-urban and urban 0.71 and 0.64 CO2-eq kg-1 FPCM dairy farming systems, respectively. The EF estimates in this study are lower than the IPCC's (2019) default value for both stall-fed high-productive and dual-purpose low-productive cows.

CONCLUSIONS

The current findings can be used as a baseline for the national emission inventory, which can be used to quantify the effects of future interventions, potentially improving the country's commitment to reducing GHG emissions. Similarly, this study suggests that increased animal productivity through improved feed has a considerable mitigation potential for reducing enteric methane emissions in smallholder dairy farming systems in the study area.

Comparison of models for lactation curves of Holstein, Brown Swiss, and F1 crossbred cows under subtropical conditions

Tropical and subtropical milk production herds in Mexico generally generate different types of milk-yield records as milk yield per month and mean production per month. Lactation curves generated by these types of records may contribute to understand milk production in the tropical regions of Mexico. The aim of this study was to compare five lactation-curve models fitted to two types of milk-yield records of Holstein, Brown Swiss, and F1 crossbred cows under subtropical conditions. The two types of records (n = 3756) used were: (1) milk yield per month (TR) and 2) mean production per months (MR). Goodness-of-fit statistics, including Akaike's information criterion (AIC) and root mean square error (RMSE), were applied to compare the models for each type of records. The Brody model provided the best goodness-of-fit when using monthly milk-yield records, while the Wilmink model provided the best goodness-of-fit for lactation milk-yield records. The RMSE and AIC values were similar between datasets. The final third of the lactation curve showed a little difference between model predictions in both datasets. The comparison of several models was useful to better describe the actual lactation curves of the herd. The Wood model may be adequate to compare information as a reference with other models for decision making process at milk production.