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Determination of the effect of climate change on small cattle milk yield in Iğdır province via machine learning

Year 2024, Volume: 28 Issue: 3, 374 - 384, 28.09.2024
https://doi.org/10.29050/harranziraat.1464601

Abstract

This study examines the potential impact of climate change on small cattle livestock and milk productivity in Iğdır province. The study takes into account various factors, including the effects of climate change on animal stress levels, nutrient quality in grazing areas, and the spread of parasites or diseases, which may indirectly affect milk productivity. To evaluate this impact, the study utilizes eXtreme Gradient Boosting (XGBoost) machine learning models with five different climate variables, analyzing the small cattle data from Iğdır province between 2004 and 2023. Two machine learning models were created to investigate the effect of climate variables on milk yield in small cattle in Iğdır province, using a dataset of 10820 rows and 16 columns. The machine learning models revealed that five different climate variables had no significant effect on milk yield. This finding is important for the economic welfare of the region, as cattle farming plays a crucial role in the economy of Iğdır province. The neutral effect of climate change is therefore evaluated positively for Iğdır province. The study suggests that there has been no significant change in milk productivity over the last 20 years due to the constant percentage of sheep that produce milk. It is recommended that farmers in Iğdır province consider increasing the number of lactating sheep to enhance overall cattle milk production.

References

  • Amin Sheikh, A., Tajamul Islam, S., Rashid Dar, R., Ahmad Sheikh, S., Mohammad Wani, J., Dogra, P., Amir Amin Sheikh, C., & Bhagat, R. (2017). Effect of climate change on reproduction and milk production performance of livestock: A review. Journal of Pharmacognosy and Phytochemistry, 6(6), 2062–2064.
  • Anne, S., & Gueye, A. D. (2024). XGBoost Algorithm to Predict a Patient’s Risk of Stroke. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 541, 151–160. https://doi.org/10.1007/978-3-031-51849-2_10
  • Baumgard, L. H., Rhoads, R. P., Rhoads, M. L., Gabler, N. K., Ross, J. W., Keating, A. F., Boddicker, R. L., Lenka, S., & Sejian, V. (2012). Impact of Climate Change on Livestock Production. Environmental Stress and Amelioration in Livestock Production, 9783642292057, 413–468. https://doi.org/10.1007/978-3-642-29205-7_15
  • Becker, C. A., Aghalari, A., Marufuzzaman, M., & Stone, A. E. (2021). Predicting dairy cattle heat stress using machine learning techniques. Journal of Dairy Science, 104(1), 501–524. https://doi.org/10.3168/JDS.2020-18653
  • Bonavita, M., & Laloyaux, P. (2020). Machine Learning for Model Error Inference and Correction. Journal of Advances in Modeling Earth Systems, 12(12), e2020MS002232.
  • Bui, Q. T., Chou, T. Y., Hoang, T. Van, Fang, Y. M., Mu, C. Y., Huang, P. H., Pham, V. D., Nguyen, Q. H., Anh, D. T. N., Pham, V. M., & Meadows, M. E. (2021). Gradient Boosting Machine and Object-Based CNN for Land Cover Classification. Remote Sensing, 13(14), 2709. https://doi.org/10.3390/RS13142709
  • Castro, H. M., & Ferreira, J. C. (2023). Linear and logistic regression models: when to use and how to interpret them? Jornal Brasileiro de Pneumologia, 48(6), e20220439. https://doi.org/10.36416/1806-3756/E20220439
  • Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/10.1145/2939672.2939785
  • Copernicus, (2024). Climate Change. Retrieved from: https://climate.copernicus.eu/
  • Dalal, S., Onyema, E. M., & Malik, A. (2022). Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy. World Journal of Gastroenterology, 28(46), 6551. https://doi.org/10.3748/WJG.V28.I46.6551
  • Dong, J., Chen, Y., Yao, B., Zhang, X., & Zeng, N. (2022). A neural network boosting regression model based on XGBoost. Applied Soft Computing, 125, 109067. https://doi.org/10.1016/J.ASOC.2022.109067
  • Ebrahimie, E., Ebrahimi, F., Ebrahimi, M., Tomlinson, S., & Petrovski, K. R. (2018). Hierarchical pattern recognition in milking parameters predicts mastitis prevalence. Computers and Electronics in Agriculture, 147, 6–11. https://doi.org/10.1016/J.COMPAG.2018.02.003
  • Garamu, K. (2019). Significance of Feed Supplementation on Milk Yield and Milk Composition of Dairy Cow. Journal of Dairy & Veterinary Sciences, 13(2), 1-9.
  • Garg, M. R., Sherasia, P. L., Bhanderi, B. M., Phondba, B. T., Shelke, S. K., & Makkar, H. P. S. (2013). Effects of feeding nutritionally balanced rations on animal productivity, feed conversion efficiency, feed nitrogen use efficiency, rumen microbial protein supply, parasitic load, immunity and enteric methane emissions of milking animals under field conditions. Animal Feed Science and Technology, 179(1–4), 24–35. https://doi.org/10.1016/J.ANIFEEDSCI.2012.11.005
  • Grace, D., Bett, B. K., Lindahl, J. F., & Robinson, T. P. (2015). Climate and livestock disease: assessing the vulnerability of agricultural systems to livestock pests under climate change scenarios. https://hdl.handle.net/10568/66595
  • Haenlein, G. F. W. (2007). About the evolution of goat and sheep milk production. Small Ruminant Research, 68(1–2), 3–6. https://doi.org/10.1016/J.SMALLRUMRES.2006.09.021
  • Hennessy, D., Luc, D., Agnes van den P-v. D., & Laurence S.. (2020). Increasing Grazing in Dairy Cow Milk Production Systems in Europe. Sustainability, 12(6), 2443. https://doi.org/10.3390/su12062443
  • Hill, D. L., & Wall, E. (2015). Dairy cattle in a temperate climate: the effects of weather on milk yield and composition depend on management. Animal, 9(1), 138–149. https://doi.org/10.1017/S1751731114002456
  • Huang, J., Li, Y-F. & Xie, M. (2015). An empirical analysis of data preprocessing for machine learning-based software cost estimation. Information and Software Technology, 67, 108-127.
  • Ji, B., Banhazi, T., Phillips, C. J. C., Wang, C., & Li, B. (2022). A machine learning framework to predict the next month’s daily milk yield, milk composition and milking frequency for cows in a robotic dairy farm. Biosystems Engineering, 216, 186–197. https://doi.org/10.1016/J.BIOSYSTEMSENG.2022.02.013
  • Kamphuis, C., Mollenhorst, H., Feelders, A., Pietersma, D., & Hogeveen, H. (2010). Decision-tree induction to detect clinical mastitis with automatic milking. Computers and Electronics in Agriculture, 70(1), 60–68. https://doi.org/10.1016/J.COMPAG.2009.08.012
  • Koc, A., Turk, S., & Şahin, G. (2019). Multi-criteria of wind-solar site selection problem using a GIS-AHP-based approach with an application in Igdir Province/Turkey. Environmental Science and Pollution Research, 26(31), 32298–32310. https://doi.org/10.1007/S11356-019-06260-1/TABLES/10
  • Kurani, A., Doshi, P., Vakharia, A., & Shah, M. (2023). A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting. Annals of Data Science, 10(1), 183–208. https://doi.org/10.1007/S40745-021-00344-X/TABLES/2
  • Liang, W., Luo, S., Zhao, G., & Wu, H. (2020). Predicting Hard Rock Pillar Stability Using GBDT, XGBoost, and LightGBM Algorithms. Mathematics, 8(5), 765. https://doi.org/10.3390/MATH8050765
  • Maphill, (2024). Physical 3D Map of Iğdır. Retrieved from: http://www.maphill.com/turkey/kars/igdir/3d-maps/physical-map/
  • Marumo, J. L., Lusseau, D., Speakman, J. R., Mackie, M., & Hambly, C. (2022). Influence of environmental factors and parity on milk yield dynamics in barn-housed dairy cattle. Journal of Dairy Science, 105(2), 1225–1241. https://doi.org/10.3168/JDS.2021-20698
  • Mohamed, S., Rosca, M., Figurnov, M. & Mnih A. (2020). Monte Carlo Gradient Estimation in Machine Learning. Journal of Machine Learning Research, 21(132), 1−62.
  • Natekin, A., & Knoll, A. (2013). Gradient boosting machines, a tutorial. Frontiers in Neurorobotics, 7, 63623. https://doi.org/10.3389/FNBOT.2013.00021/BIBTEX
  • Noorunnahar, M., Chowdhury, A. H., & Arefeen, F. (2023). A tree based eXtreme Gradient Boosting (XGBoost) machine learning model to forecast the annual rice production in Bangladesh. PLOS ONE, 18(3), e0283452. https://doi.org/10.1371/JOURNAL.PONE.0283452
  • Öztürk, Y., Yulu, A., & Turgay, O. (2023). Remote sensing supported analysis of the effect of wind erosion on local air pollution in arid regions: a case study from Iğdır province in eastern Türkiye. Environmental Systems Research, 12(1), 1–23. https://doi.org/10.1186/S40068-023-00294-8
  • Pereira, P. C. (2014). Milk nutritional composition and its role in human health. Nutrition, 30(6), 619–627. https://doi.org/10.1016/J.NUT.2013.10.011
  • Perri, A.F., Mejía, M.E., Licoff, N., Lazaro, L., Miglierina, M., Ornstein, A., Becu-Villalobos, D., Lacau-Mengido, I.M. (2011). Gastrointestinal parasites presence during the peripartum decreases total milk production in grazing dairy Holstein cows. Veterinary Parasitology, 178(3–4), 311-318. https://doi.org/10.1016/j.vetpar.2010.12.045.
  • Polsky, L., & von Keyserlingk, M. A. G. (2017). Invited review: Effects of heat stress on dairy cattle welfare. Journal of Dairy Science, 100(11), 8645–8657. https://doi.org/10.3168/JDS.2017-12651
  • Putatunda, S., & Rama, K. (2018). A comparative analysis of hyperopt as against other approaches for hyper-parameter optimization of XGBoost. ACM International Conference Proceeding Series, 6–10. https://doi.org/10.1145/3297067.3297080
  • Sahin Demirel, A.N. (2024). Investigating the effect of climate factors on fig production efficiency with machine learning approach. Journal of the Science of Food and Agriculture. https://doi.org/10.1002/JSFA.13619
  • Shrestha, D.L. & Solomatine, D.P. (2006). Machine learning approaches for estimation of prediction interval for the model output. Neural Networks, 19(2), 225-235.
  • Soori, M., Arezoo, B., & Dastres, R. (2023). Artificial intelligence, machine learning and deep learning in advanced robotics, a review. Cognitive Robotics, 3, 54–70. https://doi.org/10.1016/J.COGR.2023.04.001
  • Thornton, P., Nelson, G., Mayberry, D., & Herrero, M. (2021). Increases in extreme heat stress in domesticated livestock species during the twenty-first century. Global Change Biology, 27(22), 5762–5772. https://doi.org/10.1111/GCB.15825
  • TOB, (2024). Iğdır İl Tarım ve Orman Müdürlüğü. Retrieved from: https://igdir.tarimorman.gov.tr/
  • Türkeş, M., & Tatli, H. (2011). Zirai Meteorolojik Açıdan Iğdır İklim Etüdü. Journal of the Institute of Science and Technology, 1(1), 97–104. https://doi.org/10.1002/JOC.1862
  • Xie, Y., Jiang, B., Gong, E., Li, Y., Zhu, G., Michel, P., Wintermark, M., & Zaharchuk, G. (2019).Use of Gradient Boosting Machine Learning to Predict Patient Outcome in Acute Ischemic Stroke on the Basis of Imaging, Demographic, and Clinical Information. American Journal of Roentgenology, 212(1), 1-232.
  • Yılmaz, A.C.I. (2022). Milk Yield, Fertility, Udder Characteristics, and Raw Milk Somatic Cell Count of the Damascus Goats Reared in Iğdır Conditions. International Journal of Agricultural and Wildlife Sciences, 8(2), 358–367. https://doi.org/10.24180/IJAWS.1090613

Iğdır ilinde iklim değişikliğinin küçükbaş süt verimi üzerine etkisinin makine öğrenmesi ile belirlenmesi

Year 2024, Volume: 28 Issue: 3, 374 - 384, 28.09.2024
https://doi.org/10.29050/harranziraat.1464601

Abstract

Bu çalışma, iklim değişikliğinin Iğdır ilindeki küçükbaş hayvancılık ve süt verimliliği üzerindeki potansiyel etkisini incelemektedir. Çalışmada, iklim değişikliğinin hayvanların stres seviyeleri üzerindeki etkileri, otlatma alanlarındaki besin kalitesi ve süt verimliliğini dolaylı olarak etkileyebilecek parazitlerin veya hastalıkların yayılması gibi çeşitli faktörler dikkate alınmaktadır. Bu etkiyi değerlendirmek için çalışmada beş farklı iklim değişkeni ile eXtreme Gradient Boosting (XGBoost) makine öğrenimi modelleri kullanılmış ve Iğdır ilinin 2004-2023 yılları arasındaki küçükbaş hayvan verileri analiz edilmiştir. Iğdır ilindeki küçükbaş hayvanlarda iklim değişkenlerinin süt verimi üzerindeki etkisini araştırmak için 10820 satır ve 16 sütundan oluşan bir veri seti kullanılarak iki makine öğrenmesi modeli oluşturulmuştur. Makine öğrenimi modelleri, beş farklı iklim değişkeninin süt verimi üzerinde önemli bir etkisi olmadığını ortaya koymuştur. Büyükbaş hayvancılık Iğdır ilinin ekonomisinde önemli bir rol oynadığından, bu bulgu bölgenin ekonomik refahı açısından önemlidir. Dolayısıyla iklim değişikliğinin nötr etkisi Iğdır ili için olumlu olarak değerlendirilmektedir. Çalışma, süt üreten koyun oranının sabit kalması nedeniyle son 20 yılda süt verimliliğinde önemli bir değişiklik olmadığını göstermektedir. Iğdır ilindeki çiftçilere, toplam sığır sütü üretimini artırmak için süt veren koyun sayısını artırmayı düşünmeleri önerilmektedir.

References

  • Amin Sheikh, A., Tajamul Islam, S., Rashid Dar, R., Ahmad Sheikh, S., Mohammad Wani, J., Dogra, P., Amir Amin Sheikh, C., & Bhagat, R. (2017). Effect of climate change on reproduction and milk production performance of livestock: A review. Journal of Pharmacognosy and Phytochemistry, 6(6), 2062–2064.
  • Anne, S., & Gueye, A. D. (2024). XGBoost Algorithm to Predict a Patient’s Risk of Stroke. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 541, 151–160. https://doi.org/10.1007/978-3-031-51849-2_10
  • Baumgard, L. H., Rhoads, R. P., Rhoads, M. L., Gabler, N. K., Ross, J. W., Keating, A. F., Boddicker, R. L., Lenka, S., & Sejian, V. (2012). Impact of Climate Change on Livestock Production. Environmental Stress and Amelioration in Livestock Production, 9783642292057, 413–468. https://doi.org/10.1007/978-3-642-29205-7_15
  • Becker, C. A., Aghalari, A., Marufuzzaman, M., & Stone, A. E. (2021). Predicting dairy cattle heat stress using machine learning techniques. Journal of Dairy Science, 104(1), 501–524. https://doi.org/10.3168/JDS.2020-18653
  • Bonavita, M., & Laloyaux, P. (2020). Machine Learning for Model Error Inference and Correction. Journal of Advances in Modeling Earth Systems, 12(12), e2020MS002232.
  • Bui, Q. T., Chou, T. Y., Hoang, T. Van, Fang, Y. M., Mu, C. Y., Huang, P. H., Pham, V. D., Nguyen, Q. H., Anh, D. T. N., Pham, V. M., & Meadows, M. E. (2021). Gradient Boosting Machine and Object-Based CNN for Land Cover Classification. Remote Sensing, 13(14), 2709. https://doi.org/10.3390/RS13142709
  • Castro, H. M., & Ferreira, J. C. (2023). Linear and logistic regression models: when to use and how to interpret them? Jornal Brasileiro de Pneumologia, 48(6), e20220439. https://doi.org/10.36416/1806-3756/E20220439
  • Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/10.1145/2939672.2939785
  • Copernicus, (2024). Climate Change. Retrieved from: https://climate.copernicus.eu/
  • Dalal, S., Onyema, E. M., & Malik, A. (2022). Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy. World Journal of Gastroenterology, 28(46), 6551. https://doi.org/10.3748/WJG.V28.I46.6551
  • Dong, J., Chen, Y., Yao, B., Zhang, X., & Zeng, N. (2022). A neural network boosting regression model based on XGBoost. Applied Soft Computing, 125, 109067. https://doi.org/10.1016/J.ASOC.2022.109067
  • Ebrahimie, E., Ebrahimi, F., Ebrahimi, M., Tomlinson, S., & Petrovski, K. R. (2018). Hierarchical pattern recognition in milking parameters predicts mastitis prevalence. Computers and Electronics in Agriculture, 147, 6–11. https://doi.org/10.1016/J.COMPAG.2018.02.003
  • Garamu, K. (2019). Significance of Feed Supplementation on Milk Yield and Milk Composition of Dairy Cow. Journal of Dairy & Veterinary Sciences, 13(2), 1-9.
  • Garg, M. R., Sherasia, P. L., Bhanderi, B. M., Phondba, B. T., Shelke, S. K., & Makkar, H. P. S. (2013). Effects of feeding nutritionally balanced rations on animal productivity, feed conversion efficiency, feed nitrogen use efficiency, rumen microbial protein supply, parasitic load, immunity and enteric methane emissions of milking animals under field conditions. Animal Feed Science and Technology, 179(1–4), 24–35. https://doi.org/10.1016/J.ANIFEEDSCI.2012.11.005
  • Grace, D., Bett, B. K., Lindahl, J. F., & Robinson, T. P. (2015). Climate and livestock disease: assessing the vulnerability of agricultural systems to livestock pests under climate change scenarios. https://hdl.handle.net/10568/66595
  • Haenlein, G. F. W. (2007). About the evolution of goat and sheep milk production. Small Ruminant Research, 68(1–2), 3–6. https://doi.org/10.1016/J.SMALLRUMRES.2006.09.021
  • Hennessy, D., Luc, D., Agnes van den P-v. D., & Laurence S.. (2020). Increasing Grazing in Dairy Cow Milk Production Systems in Europe. Sustainability, 12(6), 2443. https://doi.org/10.3390/su12062443
  • Hill, D. L., & Wall, E. (2015). Dairy cattle in a temperate climate: the effects of weather on milk yield and composition depend on management. Animal, 9(1), 138–149. https://doi.org/10.1017/S1751731114002456
  • Huang, J., Li, Y-F. & Xie, M. (2015). An empirical analysis of data preprocessing for machine learning-based software cost estimation. Information and Software Technology, 67, 108-127.
  • Ji, B., Banhazi, T., Phillips, C. J. C., Wang, C., & Li, B. (2022). A machine learning framework to predict the next month’s daily milk yield, milk composition and milking frequency for cows in a robotic dairy farm. Biosystems Engineering, 216, 186–197. https://doi.org/10.1016/J.BIOSYSTEMSENG.2022.02.013
  • Kamphuis, C., Mollenhorst, H., Feelders, A., Pietersma, D., & Hogeveen, H. (2010). Decision-tree induction to detect clinical mastitis with automatic milking. Computers and Electronics in Agriculture, 70(1), 60–68. https://doi.org/10.1016/J.COMPAG.2009.08.012
  • Koc, A., Turk, S., & Şahin, G. (2019). Multi-criteria of wind-solar site selection problem using a GIS-AHP-based approach with an application in Igdir Province/Turkey. Environmental Science and Pollution Research, 26(31), 32298–32310. https://doi.org/10.1007/S11356-019-06260-1/TABLES/10
  • Kurani, A., Doshi, P., Vakharia, A., & Shah, M. (2023). A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting. Annals of Data Science, 10(1), 183–208. https://doi.org/10.1007/S40745-021-00344-X/TABLES/2
  • Liang, W., Luo, S., Zhao, G., & Wu, H. (2020). Predicting Hard Rock Pillar Stability Using GBDT, XGBoost, and LightGBM Algorithms. Mathematics, 8(5), 765. https://doi.org/10.3390/MATH8050765
  • Maphill, (2024). Physical 3D Map of Iğdır. Retrieved from: http://www.maphill.com/turkey/kars/igdir/3d-maps/physical-map/
  • Marumo, J. L., Lusseau, D., Speakman, J. R., Mackie, M., & Hambly, C. (2022). Influence of environmental factors and parity on milk yield dynamics in barn-housed dairy cattle. Journal of Dairy Science, 105(2), 1225–1241. https://doi.org/10.3168/JDS.2021-20698
  • Mohamed, S., Rosca, M., Figurnov, M. & Mnih A. (2020). Monte Carlo Gradient Estimation in Machine Learning. Journal of Machine Learning Research, 21(132), 1−62.
  • Natekin, A., & Knoll, A. (2013). Gradient boosting machines, a tutorial. Frontiers in Neurorobotics, 7, 63623. https://doi.org/10.3389/FNBOT.2013.00021/BIBTEX
  • Noorunnahar, M., Chowdhury, A. H., & Arefeen, F. (2023). A tree based eXtreme Gradient Boosting (XGBoost) machine learning model to forecast the annual rice production in Bangladesh. PLOS ONE, 18(3), e0283452. https://doi.org/10.1371/JOURNAL.PONE.0283452
  • Öztürk, Y., Yulu, A., & Turgay, O. (2023). Remote sensing supported analysis of the effect of wind erosion on local air pollution in arid regions: a case study from Iğdır province in eastern Türkiye. Environmental Systems Research, 12(1), 1–23. https://doi.org/10.1186/S40068-023-00294-8
  • Pereira, P. C. (2014). Milk nutritional composition and its role in human health. Nutrition, 30(6), 619–627. https://doi.org/10.1016/J.NUT.2013.10.011
  • Perri, A.F., Mejía, M.E., Licoff, N., Lazaro, L., Miglierina, M., Ornstein, A., Becu-Villalobos, D., Lacau-Mengido, I.M. (2011). Gastrointestinal parasites presence during the peripartum decreases total milk production in grazing dairy Holstein cows. Veterinary Parasitology, 178(3–4), 311-318. https://doi.org/10.1016/j.vetpar.2010.12.045.
  • Polsky, L., & von Keyserlingk, M. A. G. (2017). Invited review: Effects of heat stress on dairy cattle welfare. Journal of Dairy Science, 100(11), 8645–8657. https://doi.org/10.3168/JDS.2017-12651
  • Putatunda, S., & Rama, K. (2018). A comparative analysis of hyperopt as against other approaches for hyper-parameter optimization of XGBoost. ACM International Conference Proceeding Series, 6–10. https://doi.org/10.1145/3297067.3297080
  • Sahin Demirel, A.N. (2024). Investigating the effect of climate factors on fig production efficiency with machine learning approach. Journal of the Science of Food and Agriculture. https://doi.org/10.1002/JSFA.13619
  • Shrestha, D.L. & Solomatine, D.P. (2006). Machine learning approaches for estimation of prediction interval for the model output. Neural Networks, 19(2), 225-235.
  • Soori, M., Arezoo, B., & Dastres, R. (2023). Artificial intelligence, machine learning and deep learning in advanced robotics, a review. Cognitive Robotics, 3, 54–70. https://doi.org/10.1016/J.COGR.2023.04.001
  • Thornton, P., Nelson, G., Mayberry, D., & Herrero, M. (2021). Increases in extreme heat stress in domesticated livestock species during the twenty-first century. Global Change Biology, 27(22), 5762–5772. https://doi.org/10.1111/GCB.15825
  • TOB, (2024). Iğdır İl Tarım ve Orman Müdürlüğü. Retrieved from: https://igdir.tarimorman.gov.tr/
  • Türkeş, M., & Tatli, H. (2011). Zirai Meteorolojik Açıdan Iğdır İklim Etüdü. Journal of the Institute of Science and Technology, 1(1), 97–104. https://doi.org/10.1002/JOC.1862
  • Xie, Y., Jiang, B., Gong, E., Li, Y., Zhu, G., Michel, P., Wintermark, M., & Zaharchuk, G. (2019).Use of Gradient Boosting Machine Learning to Predict Patient Outcome in Acute Ischemic Stroke on the Basis of Imaging, Demographic, and Clinical Information. American Journal of Roentgenology, 212(1), 1-232.
  • Yılmaz, A.C.I. (2022). Milk Yield, Fertility, Udder Characteristics, and Raw Milk Somatic Cell Count of the Damascus Goats Reared in Iğdır Conditions. International Journal of Agricultural and Wildlife Sciences, 8(2), 358–367. https://doi.org/10.24180/IJAWS.1090613
There are 42 citations in total.

Details

Primary Language English
Subjects Agribusiness
Journal Section Araştırma Makaleleri
Authors

Ayça Nur Şahin Demirel 0000-0003-2988-8448

Taner Erik 0009-0009-9443-2552

Early Pub Date September 28, 2024
Publication Date September 28, 2024
Submission Date April 3, 2024
Acceptance Date July 9, 2024
Published in Issue Year 2024 Volume: 28 Issue: 3

Cite

APA Şahin Demirel, A. N., & Erik, T. (2024). Determination of the effect of climate change on small cattle milk yield in Iğdır province via machine learning. Harran Tarım Ve Gıda Bilimleri Dergisi, 28(3), 374-384. https://doi.org/10.29050/harranziraat.1464601

Indexing and Abstracting 

13435  19617 13436 13440 13441 13442 13443

13445 13447 13449 13464 13466


10749  Harran Journal of Agricultural and Food Science is licensed under Creative Commons 4.0 International License.