Effect of introducing weather parameters on the accuracy of milk production forecast models

Fan, Zhongwei; Upton, John; Shalloo, L.; Shine, Philip; Murphy, Michael D.

doi:10.1016/j.inpa.2019.04.004

Cited by 7 publications

(3 citation statements)

References 34 publications

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“…Zhang et al [16] stated that precision milk production was beneficial to farm management and helpful in decision-making to create economic benefits. Also, the ability to predict the weather and manage the consequences of bad weather was practically useful for farmers.…”

Section: Introductionmentioning

confidence: 99%

Dairy Farmers’ Intention to Use High-Precision Area-Level Weather Warning Technology in Dairy Farms in Thailand Through an Application on Mobile Phones

Rajchamaha,

Makararpong

2023

Int. J. Interact. Mob. Technol.

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Data from information technology has been crucial in advancing the agricultural and livestock industries, especially for Thai dairy farms located in tropical regions where sudden weather changes can occur. This study uses the technology acceptance model (TAM) and the innovation diffusion theory (IDT) to predict the Thai dairy farmers’ intention to use a high-precision area-level weather warning technology through a mobile phone application. A questionnaire was collected from dairy farm owners in Thailand’s central and northeastern regions, and the data was analyzed using stepwise multiple linear regression. The main factor influencing the acceptance of this technology was its compatibility with existing technology, and it needed to be available on mobile phones for instant access. The role of technology had to provide dairy farmers with information for decision-making, especially during extreme weather conditions, to mitigate potential damage. Training sessions and workshops on the implementation of this technology were necessary, and young farmers could share content on social media to promote its benefits. Service providers should set up call centers to help dairy farmers make decisions. This study identifies factors that can increase farmers’ intentions to use digital technology. By addressing these factors, the adoption of technology can improve the efficiency and productivity of dairy farming in Thailand.

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Section: Introductionmentioning

confidence: 99%

Dairy Farmers’ Intention to Use High-Precision Area-Level Weather Warning Technology in Dairy Farms in Thailand Through an Application on Mobile Phones

Rajchamaha,

Makararpong

2023

Int. J. Interact. Mob. Technol.

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“…8 In Brazil, about 90% of cattle are raised on pasture, 11,12 and they are exposed to climate conditions that can interfere with their performance, especially milk production. 13,14 Air temperature and relative humidity are the elements that have the greatest influence on animal performance, 15 as they hinder heat dissipation. 16 Cattle are homeothermic animalsthey maintain the body core temperature at an approximately stable level from 38 to 39.3 °C.…”

Section: Introductionmentioning

confidence: 99%

Bioclimatic zoning for dairy cows in Brazil by statistical modeling

Aparecido¹,

Lorençone²,

Lorençone³

et al. 2022

J Sci Food Agric

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BACKGROUND: Climate conditions affect animal welfare directly, influencing milk production. The Midwest region is the largest cattle-producing region in Brazil. The objective of this study was to elaborate on bioclimatic zoning for dairy cattle in the Midwest region of Brazil. Air temperature (Ta, °C) and relative humidity (%, RH) data from a 30-year historical series collected by the National Aeronautics and Space Administration/Prediction of Worldwide Energy Resources (NASA/POWER) platform were used. The Temperature and Humidity Index (THI) was determined for the hottest and coldest months. Milk production losses due to climate factors in the Midwest of Brazil for two daily production levels, 10 kg Milk (PL10) and 25 kg Milk (PL25), were estimated. RESULTS: The Midwest presented three THI classifications throughout the year: 'normal', 'alert', and 'critical alert'. The entire Midwest region was classified as 'normal' (THI < 70) between autumn and winter. The decrease in milk production (DMP) during the autumn and winter presented no loss for both production levels (PL10 and PL25).CONCLUSION: On the other hand, a 1 to 2 kg reduction in milk production was observed for cows with a PL25 production level between spring and summer in the southern Midwest region, while cows with a PL10 production level showed no reduction in milk production. Only the cities of Sinop and Cuiabá did not present a 'critical alert' during spring/summer for the risk of heat stress.

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“…Research is already done to create models that include all aspects of a dairy farm, from expected feed crop yields to individual cow management (Lianga et al, 2018;Ferris et al, 2020). These developments are promising for the prediction of survival traits, as survival is influenced by both individual and farm-level factors (Rutten et al, 2013;Zhang et al, 2020).…”

Section: Increasing the Amount Of Datamentioning

confidence: 99%

Predicting survival in dairy cattle using machine learning

Heide¹,

Maria²

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VAN DER HEIDE. (2020). Predicting survival in dairy cattle using machine learning. PhD thesis, Wageningen University, the Netherlands A lthough cows can live to be twenty years old, the average lifespan for a dairy cow is only five to six years. Improving the lifespan of dairy cows would have several benefits such as increasing farm profitability and reducing the environmental impact of milk production. However, the complexity of survival makes it difficult to improve this trait in practice. In this thesis, I proposed using phenotypic prediction of survival to select young cows for the dairy herd, improving survival through increased lifespan of selected cows and better heifer management. The aim of this thesis was to investigate if it was possible to predict survival phenotype accurately enough to be of use in selection. I investigated three different methods to predict survival: multiple logistic regression, random forest and naive Bayes. In chapters two to four of this thesis I predicted the survival trait "survival to second lactation" using all three aforementioned methods. In chapter five, I predicted the survival trait "number of parities reached" using only the random forest method. Random forest and naive Bayes proved the best methods for predicting survival to second lactation, although predictive performance overall was low. The correlations between predictions for individual cows were much lower than expected, which indicated that the models predicted individual cows differently. Therefore, in chapter four I investigated if combining the results into an ensemble could improve predictive performance. An ensemble using multiple logistic regression resulted in the largest increase in performance, although none of the explored ensemble methods improved performance consistently across datasets. I further investigated if there was a benefit in including genomic information or a farm-specific effect. In chapter two, I investigated the benefit of combining genomic and phenotypic information. Genomic breeding values especially improved the prediction of survival early in life, with breeding values for fertility and longevity remained informative even after first calving. In chapter five I tested several different methods to include a farm effect and described the advantages and disadvantages of the various approaches. The results of this thesis provide valuable insights in the challenges of predicting survival traits and the suitability of various (machine learning) methods for the prediction of survival in dairy cattle.

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Effect of introducing weather parameters on the accuracy of milk production forecast models

Cited by 7 publications

References 34 publications

Dairy Farmers’ Intention to Use High-Precision Area-Level Weather Warning Technology in Dairy Farms in Thailand Through an Application on Mobile Phones

Dairy Farmers’ Intention to Use High-Precision Area-Level Weather Warning Technology in Dairy Farms in Thailand Through an Application on Mobile Phones

Bioclimatic zoning for dairy cows in Brazil by statistical modeling

Predicting survival in dairy cattle using machine learning

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