Research Article
Farklı Veri Madenciliği Algoritmalarının Domates Verimindeki Tahmin Performanslarının Karşılaştırılması: Iğdır İli Örneği
Abstract
Domates sebze türleri arasında en fazla ekim alanına sahip bitkidir. Domates veriminin artırılması dünya ekonomisi ve çiftçi gelirine daha fazla katkı sağlaması açısından önemlidir. Yazılım teknolojilerinin ilerlemesi ile regresyon ve sınıflandırma problemlerine daha gelişmiş çözümlerin sunulması veri madenciliğinin önemi artırmaktadır. Bu çalışmada domates verimini etkileyen faktörlerin belirlenmesi ve domates veriminin tahmininde farklı veri madenciliği algoritmalarının karşılaştırılması amaçlanmıştır. Bu amaç ile Iğdır ilinde 105 çiftçi ile anket çalışması yapılmıştır. Sınıflandırma ve Regresyon Ağacı (CART), Ki-Kare Otomatik Etkileşim Dedektörü (CHAID), Exhaustive CHAID, Yapay Sinir Ağı Algoritması (ANN), Çok Değişkenli Uyarlamalı Regresyon Analizi (MARS) ve Genel Doğrusal Model (GLM) gibi farklı veri madenciliği algoritmaları kullanılarak tahmin performansları karşılaştırılmıştır. MARS karar ağacı, en yüksek tahmin doğruluğuna sahip modeli oluşturmuştur. Tahmin performanslarına göre diğer algoritmalar ANN> GLM> CART> CHAID> Exhaustive CHAID’dır. MARS modelinde, sulama sayısı, kimyasal gübre miktarı, çiftçi yaşı, fide sayısı, eğitim düzeyi, toprak analiz durumu ve ekim bölgesi değişkenleri istatistiksel olarak anlamlı bulunmuştur (P˂0.05). MARS modelinin tercih edilmesi, domates verimini etkileyen faktörleri ve bunların etkileşimlerini daha yüksek doğrulukla tespit edilmesini sağlayacaktır. Verim artışı için dekara en az 1450 fide dikilmeli ve en az 5 defa sulama yapılmalıdır.
References
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- Anonymous, (2019). Crop Production Statistics. https://www.tuik.gov.tr/Home/Index (Date accessed: 12.02.2021).
- Anonymous, (2020). Temperature Data for the Province of Igdir. https://tr.climate-data.org/asya/tuerkiye/igd%C4%B1r-693/ (Date accessed: 12.03.2021).
- Aytekin, İ., Eyduran, E., Karadaş, K., Akşahan, R., & Keskin, İ. (2018). Prediction of fattening final live weight from some body measurements and fattening period in young bulls of crossbred and exotic breeds using MARS data mining algorithm. Revista Brasileira de Zootecnia 50(1), 189-195. http://doi.org/10.17582/journal.pjz/2018.50.1.189.195
- Bostancı, B. & Eren-Atay, C. (2018). Decision support tools for barley yield: the case of Menemen – Turkey. Dokuz Eylul University Faculty of Engineering Journal of Science and Engineering 20(60), 1057-1067. https://doi.org/10.21205/deufmd.2018206085
- Camdeviren, H.A., Yazici, A.C., Akkus, Z., Bugdayci, R., & Sungur, M.A. (2007). Comparison of logistic regression model and classification tree: an application to postpartum depression data. Expert Systems with Applications 32(4), 987–994. https://doi.org/10.1016/j.eswa.2006.02.022
- Celik, S., Eyduran, E., Tatliyer, A., Karadas, K., Kara, M.K., & Waheed, A. (2018). comparing predictive performances of some nonlinear functions and multivariate adaptive regression splines (MARS) for describing the growth of daera dın panah (DDP) goat in Pakistan. Pakistan Journal of Zoology 50(3): 1-4. http://doi.org/10.17582/journal.pjz/2018.50.3.sc2
- Cho, W., Na, M. & Park, Y. (2018). Extraction of optimum condition of cultivation factors to improve tomato production using statistical regression analysis and response surface methodology. Advanced Science Letters 24(3), 2084-2087.
- Comlekcioglu, N. & Şimşek, M. (2014). The effect of gibberellic acid (GA3) on fruit set in industrial tomato at high temperature conditions and different water level. Yuzuncu Yil University Journal of Agricultural Science 24(3), 270- 279.
- Debela, K. B., Belew, D., & Nego, J. (2016). Evaluation of tomato (Lycopersicon Esculentum Mill.) varieties for growth and seed quality under jimma condition, South Western Ethiopia. International Journal of Crop Science and Technology 2(2), 69-77.
- Degefa, G., Benti, G., Jafar, M., Tadesse, F., & Berhanu, H. (2019). Effects of intra-row spacing and n fertilizer rates on yield and yield components of tomato (Lycopersicon Esculentum L.) at Harawe, Eastern Ethiopia, Journal of Plant Sciences 7(1), 8-12. https://doi.org/10.11648/j.jps.20190701.12
- Duru, M., Duru, A., Karadas, K., Eyduran, E., Cinli, H., & Tariq, M.M. (2017). Effect of carrot (Daucus carota) leaf powder on external and internal egg characteristics of hy-line white laying hens. Pakistan Journal of Zoology 49(1), 125-132. https://doi.org/10.17582/journal.pjz/2017.49.1.125
- Estrada-Ortiz, E., Trejo-Tellez, L.I., Gomez-Merino, F.C., Nunez-Escobar, R., & Sandoval-Villa, M. (2013). The effects of phosphite on tomato yield and fruit quality. The Journal of Soil Science and Plant Nutrition 13(3), 612–620. https://doi.org/10.4067/S0718-95162013005000049
- Eyduran, E., Zaborski, D., Waheed, A., Celik, S., Karadas, K. & Grzesiak, W. (2017). Comparison of the predictive capabilities of several data mining algorithms and multiple linear regression in the prediction of body weight by means of body measurements in the indigenous beetal goat of Pakistan. Pakistan Journal of Zoology 49(1), 257-265. https://doi.org/10.17582/journal.pjz/2017.49.1.257.265
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- Haworth, F. (1961). The effects of organic and inorganic nitrogen fertilisers on the yield of early potatoes, spring cabbage, leeks and summer cabbage. Journal of Horticultural Science 36, 202-205
- Helyes, L., Lugasi, A., & Pek, Z. (2012). Effect of irrigation on processing tomato yield and antioxidant components. The Turkish Journal of Agriculture and Forestry 36(6), 702-709. https://doi.org/10.3906/tar-1107-9
- Irmak, S., & Ercan, U. (2017). Determining of the affecting factors edible oil consumption using data mining method. Kafkas University Economics and Administrative Sciences Faculty the Journal 8(15), 57-79.
- Karadas, K. & Birinci, A. (2019). Determination of factors affecting dairy cattle: a case study of ardahan province using data mining algorithms. Revista Brasileira de Zootecnia 48: 1-11. https://doi.org/10.1590/rbz4820170263
- Karadas, K. & Kadirhanogullari, I.H. (2017). Predicting honey production using data mining and artificial neural network algorithms in apiculture. Pakistan Journal of Zoology 49(5), 1611-1619. https://doi.org/10.0.68.174/journal.pjz/2017.49.5.1611.1619
- Kibria, G., Islam, M., & Alamgir, M. (2016). Yield and nutritional quality of tomato as affected by chemical fertilizer and biogas plant residues. International Journal of Plant & Soil Science 13(2), 1-10. https://doi.org/10.9734/IJPSS/2016/29434
- Kiracı, S. & Karataş, A. (2015). Organic tomato growing plant activator applications effects on yield and quality. Journal of Adnan Menderes University Agricultural Faculty 12(1), 17–22.
- Küçükönder, H., Vursavuş, K.K., & Üçkardeş, F. (2015). Determining the effect of some mechanical properties on color maturity of tomato with K-Star, Random Forest and Decision Tree (C4.5) Classification Algorithms. Turkish Journal of Agriculture - Food Science and Technology 3(5), 300-306. https://doi.org/10.7161/omuanajas.952786
- Letourneau, G., Caron, J., Anderson, L., & Cormier, J. (2015). Matric potential-based irrigation management of field-grown tomato: effects on yield and water use efficiency. Agricultural Water Management 161, 102–113. https://doi.org/10.1016/j.agwat.2015.07.005
- Liu, J., Hu, T., Feng, P., Wang, L., & Yang, S. (2019). Tomato yield and water use efficiency change with various soil moisture and potassium levels during different growth stages. Plos One 14(3), 1-14. https://doi.org/10.1371/journal. pone.0213643.
- Manan, A., Ayyub, C.M., Aslam, Pervez, M., & Ahmad, R. (2016). Methyl jasmonate brings about resistance against salinity stressed tomato plants by altering biochemical and physiological processes. The Pakistan Journal of Agricultural Sciences 53(1), 35-41. https://doi.org/10.21162/PAKJAS/16.4441
- Na, M., Park, Y., & Cho, W. (2017). A study on optimal environmental factors of tomato using smart farm data. Journal of the Korean Data & Information Science Society 28(6), 1427–1435. https://doi.org/10.7465/jkdi.2017.28.6.1427
- Navarro-Gonzále, I., García-Alonso, J., & Periago, M. J. (2018). Bioactive compounds of tomato: Cancer chemopreventive effects and influence on the transcriptome in hepatocytes. Journal of Functional Foods 42, 271-280. https://doi.org/10.1016/j.jff.2018.01.003
- Neta, M.N.A., Mota, W.F., Pegoraro, R.F., Pacheco, M.C., Batista, C.M., & Sorases, M.C. (2019). Agronomic yield and quality of industrial tomatoes under NPK doses. Revista Brasileira de Engenharia Agrícola e Ambiental 24(1), 59-64. https://doi.org/10.1590/1807-1929/agriambi.v24n1p59-64
- Özer, H. (2016). Organic tomato production. international journal of agricultural and wildlife sciences, Abant Izzet Baysal University Faculty of Agriculture and Natural Sciences 2(1), 43–53.
- Özkan, Z., Ünlü, L., & Ögür, E. (2017). Comparison of the efficiency of pheromone and pherolite traps used against tomato moth (tuta absoluta meyrick) in greenhouse tomato growing. Harran Journal of Agricultural and Food Sciences 21(4), 394-403. https://doi.org/10.29050/harranziraat.290747
- Sajjad, M., Ashfaq, M., Suhail, A., & Akhtar, S. (2011). Screening of tomato genotypes for resistance to tomato fruit borer (helicoverpa armiger hubner) in Pakistan. The Pakistan Journal of Agricultural Sciences 48(1), 59-62.
- Söylemez, S., & Pakyürek, A. Y. (2017). Effect of different rootstocks and nutrient induced ec levels on element content of the tomato fruits. Turkish Journal of Agricultural and Natural Sciences 4(2), 155–161.
- Sun, J. & Hui, L. (2008). Data mining method for listed companies, financial distress prediction. Knowledge-Based Systems 21(1), 1-5. https://10.1016/j.knosys.2006.11.003
- Svec, L. V., Thoroughgood, C. A., & Mok, H. C. S. (1976). Chemical evaluation of vegetables grown with conventional or organic soil ammendments. Communications in Soil Science and Plant Analysis 7(2), 213-228. https://doi.org/10.1080/00103627 609366634
- Tapiero, H., Townsend, D. M., & Tew, K. D. (2004). The role of carotenoids in the prevention of human pathologies. Biomedicine and Pharmacotherapy 58(2): 100-110. https://doi.org/10.1016/j.biopha.2003.12.006
- Tatar, M., & Pirinç, V. (2017). Potential of industrial tomato production of Southeast Anatolian Region in Turkey. Iğdır University Journal of the Institute of Science and Technology 7(2), 11-20. https://doi.org/10.21597/jist.2017.121
- Tesafay, T., Gebremariam, M., Gebredsadik, K., Hagazi, M., & Girmay, S. (2018). Tomato yield and economic performance under vermicompost and mineral fertilizer applications. The Open Agriculture Journal 12(1), 262-269. https://doi.org/10.2174/1874331501812010262
- Toor, R.K., Geoffrey, P.S., & Anuschka, H. (2006). Influence of different types of fertilisers on the major antioxidant components of tomatoes. Journal of Food Composition and Analysis 19(1), 20–27. https://doi.org/10.1016/j.jfca.2005.03.003
- Wang, X., & Xing, Y. (2017). Evaluation of the effects of irrigation and fertilization on tomato fruit yield and quality: a principal component analysis. Scientific reports 7(1), 350. https://doi.org/10.1038/s41598-017-00373-8
- Yamane, T. (2001). Turkish Translation of the Basic Sampling Methods. Translators: Esin, A., Aydın, C., Bakır, M.A., Gürbüzsel, E., Literatür Yayınları, Tukey. pp.509.
- Yaraş, G., & Daşgan, H. Y. (2012). Effects of soil-applied micronized-sulphur with bentonite and organic matter on soil ph, tomato plant growth, yield and fruit quality under greenhouse conditions. Reserach Journal of Agricultural Sciences 5(1): 175-180
Comparison of Predictive Performance of Data Mining Algorithms in Predicting Tomato Yield with the A Case Study in Igdir
Abstract
Among the vegetable species in the world, the plant with the most cultivation area is tomato. Increasing tomato yield is important in terms of contributing more to the world economy, producer’s income and human health. With the advancement in software technologies, the importance of data mining algorithms is increasing due to the fact that these algorithms can produce more sophisticated solutions for regression and classification problems. Determining the factors affecting tomato yield and comparing different data mining algorithms on prediction of tomato yield are the purpose of this study. For this purpose, survey study was conducted with the 105 farmers, selected by Simple Random Sampling Method in Igdir province in 2016. Different data mining algorithms including Classification and Regression Tree, Exhaustive CHAID, Chi-Square Automatic Interaction Detector, Artificial Neural Network Algorithm, Multivariate Adaptive Regression Splines and General Linear Model were developed and compared their predictive performance. MARS decision tree has been able to build a model with greatest predictive accuracy, and the others are respectively ANN, GLM, CART, CHAID and Exhaustive CHAID. In the MARS model, number of irrigation , amount of chemical fertilizer , age of farmer , number of seedlings , education level , soil analysis status , sowing region were found statistically significant (P˂0.05). Preferring the MARS model could give an opportunity to detect factors affecting tomato yield and their interactions with higher accuracy. Moreover, results can be easily interpreted and the rules are understandable.
References
- Anonymous, (2018). Food and Agricultural Commodities Production Database. http://faostat.fao.org/site/339/default.aspx (Date accessed: 12.05.2021).
- Anonymous, (2019). Crop Production Statistics. https://www.tuik.gov.tr/Home/Index (Date accessed: 12.02.2021).
- Anonymous, (2020). Temperature Data for the Province of Igdir. https://tr.climate-data.org/asya/tuerkiye/igd%C4%B1r-693/ (Date accessed: 12.03.2021).
- Aytekin, İ., Eyduran, E., Karadaş, K., Akşahan, R., & Keskin, İ. (2018). Prediction of fattening final live weight from some body measurements and fattening period in young bulls of crossbred and exotic breeds using MARS data mining algorithm. Revista Brasileira de Zootecnia 50(1), 189-195. http://doi.org/10.17582/journal.pjz/2018.50.1.189.195
- Bostancı, B. & Eren-Atay, C. (2018). Decision support tools for barley yield: the case of Menemen – Turkey. Dokuz Eylul University Faculty of Engineering Journal of Science and Engineering 20(60), 1057-1067. https://doi.org/10.21205/deufmd.2018206085
- Camdeviren, H.A., Yazici, A.C., Akkus, Z., Bugdayci, R., & Sungur, M.A. (2007). Comparison of logistic regression model and classification tree: an application to postpartum depression data. Expert Systems with Applications 32(4), 987–994. https://doi.org/10.1016/j.eswa.2006.02.022
- Celik, S., Eyduran, E., Tatliyer, A., Karadas, K., Kara, M.K., & Waheed, A. (2018). comparing predictive performances of some nonlinear functions and multivariate adaptive regression splines (MARS) for describing the growth of daera dın panah (DDP) goat in Pakistan. Pakistan Journal of Zoology 50(3): 1-4. http://doi.org/10.17582/journal.pjz/2018.50.3.sc2
- Cho, W., Na, M. & Park, Y. (2018). Extraction of optimum condition of cultivation factors to improve tomato production using statistical regression analysis and response surface methodology. Advanced Science Letters 24(3), 2084-2087.
- Comlekcioglu, N. & Şimşek, M. (2014). The effect of gibberellic acid (GA3) on fruit set in industrial tomato at high temperature conditions and different water level. Yuzuncu Yil University Journal of Agricultural Science 24(3), 270- 279.
- Debela, K. B., Belew, D., & Nego, J. (2016). Evaluation of tomato (Lycopersicon Esculentum Mill.) varieties for growth and seed quality under jimma condition, South Western Ethiopia. International Journal of Crop Science and Technology 2(2), 69-77.
- Degefa, G., Benti, G., Jafar, M., Tadesse, F., & Berhanu, H. (2019). Effects of intra-row spacing and n fertilizer rates on yield and yield components of tomato (Lycopersicon Esculentum L.) at Harawe, Eastern Ethiopia, Journal of Plant Sciences 7(1), 8-12. https://doi.org/10.11648/j.jps.20190701.12
- Duru, M., Duru, A., Karadas, K., Eyduran, E., Cinli, H., & Tariq, M.M. (2017). Effect of carrot (Daucus carota) leaf powder on external and internal egg characteristics of hy-line white laying hens. Pakistan Journal of Zoology 49(1), 125-132. https://doi.org/10.17582/journal.pjz/2017.49.1.125
- Estrada-Ortiz, E., Trejo-Tellez, L.I., Gomez-Merino, F.C., Nunez-Escobar, R., & Sandoval-Villa, M. (2013). The effects of phosphite on tomato yield and fruit quality. The Journal of Soil Science and Plant Nutrition 13(3), 612–620. https://doi.org/10.4067/S0718-95162013005000049
- Eyduran, E., Zaborski, D., Waheed, A., Celik, S., Karadas, K. & Grzesiak, W. (2017). Comparison of the predictive capabilities of several data mining algorithms and multiple linear regression in the prediction of body weight by means of body measurements in the indigenous beetal goat of Pakistan. Pakistan Journal of Zoology 49(1), 257-265. https://doi.org/10.17582/journal.pjz/2017.49.1.257.265
- Friedman, J.H. (1991). Multivariate adaptive regression splines. The Annals of Statistics 19(1), 1-67. https://doi.org/10.1214/aos/1176347963
- Hahn, F. (2013). Sensing, control and instrumentation during tomato growth. tomatoes: cultivation, varieties and nutrition. Food science and technology. Nova Science Publishers, 339pp.
- Haworth, F. (1961). The effects of organic and inorganic nitrogen fertilisers on the yield of early potatoes, spring cabbage, leeks and summer cabbage. Journal of Horticultural Science 36, 202-205
- Helyes, L., Lugasi, A., & Pek, Z. (2012). Effect of irrigation on processing tomato yield and antioxidant components. The Turkish Journal of Agriculture and Forestry 36(6), 702-709. https://doi.org/10.3906/tar-1107-9
- Irmak, S., & Ercan, U. (2017). Determining of the affecting factors edible oil consumption using data mining method. Kafkas University Economics and Administrative Sciences Faculty the Journal 8(15), 57-79.
- Karadas, K. & Birinci, A. (2019). Determination of factors affecting dairy cattle: a case study of ardahan province using data mining algorithms. Revista Brasileira de Zootecnia 48: 1-11. https://doi.org/10.1590/rbz4820170263
- Karadas, K. & Kadirhanogullari, I.H. (2017). Predicting honey production using data mining and artificial neural network algorithms in apiculture. Pakistan Journal of Zoology 49(5), 1611-1619. https://doi.org/10.0.68.174/journal.pjz/2017.49.5.1611.1619
- Kibria, G., Islam, M., & Alamgir, M. (2016). Yield and nutritional quality of tomato as affected by chemical fertilizer and biogas plant residues. International Journal of Plant & Soil Science 13(2), 1-10. https://doi.org/10.9734/IJPSS/2016/29434
- Kiracı, S. & Karataş, A. (2015). Organic tomato growing plant activator applications effects on yield and quality. Journal of Adnan Menderes University Agricultural Faculty 12(1), 17–22.
- Küçükönder, H., Vursavuş, K.K., & Üçkardeş, F. (2015). Determining the effect of some mechanical properties on color maturity of tomato with K-Star, Random Forest and Decision Tree (C4.5) Classification Algorithms. Turkish Journal of Agriculture - Food Science and Technology 3(5), 300-306. https://doi.org/10.7161/omuanajas.952786
- Letourneau, G., Caron, J., Anderson, L., & Cormier, J. (2015). Matric potential-based irrigation management of field-grown tomato: effects on yield and water use efficiency. Agricultural Water Management 161, 102–113. https://doi.org/10.1016/j.agwat.2015.07.005
- Liu, J., Hu, T., Feng, P., Wang, L., & Yang, S. (2019). Tomato yield and water use efficiency change with various soil moisture and potassium levels during different growth stages. Plos One 14(3), 1-14. https://doi.org/10.1371/journal. pone.0213643.
- Manan, A., Ayyub, C.M., Aslam, Pervez, M., & Ahmad, R. (2016). Methyl jasmonate brings about resistance against salinity stressed tomato plants by altering biochemical and physiological processes. The Pakistan Journal of Agricultural Sciences 53(1), 35-41. https://doi.org/10.21162/PAKJAS/16.4441
- Na, M., Park, Y., & Cho, W. (2017). A study on optimal environmental factors of tomato using smart farm data. Journal of the Korean Data & Information Science Society 28(6), 1427–1435. https://doi.org/10.7465/jkdi.2017.28.6.1427
- Navarro-Gonzále, I., García-Alonso, J., & Periago, M. J. (2018). Bioactive compounds of tomato: Cancer chemopreventive effects and influence on the transcriptome in hepatocytes. Journal of Functional Foods 42, 271-280. https://doi.org/10.1016/j.jff.2018.01.003
- Neta, M.N.A., Mota, W.F., Pegoraro, R.F., Pacheco, M.C., Batista, C.M., & Sorases, M.C. (2019). Agronomic yield and quality of industrial tomatoes under NPK doses. Revista Brasileira de Engenharia Agrícola e Ambiental 24(1), 59-64. https://doi.org/10.1590/1807-1929/agriambi.v24n1p59-64
- Özer, H. (2016). Organic tomato production. international journal of agricultural and wildlife sciences, Abant Izzet Baysal University Faculty of Agriculture and Natural Sciences 2(1), 43–53.
- Özkan, Z., Ünlü, L., & Ögür, E. (2017). Comparison of the efficiency of pheromone and pherolite traps used against tomato moth (tuta absoluta meyrick) in greenhouse tomato growing. Harran Journal of Agricultural and Food Sciences 21(4), 394-403. https://doi.org/10.29050/harranziraat.290747
- Sajjad, M., Ashfaq, M., Suhail, A., & Akhtar, S. (2011). Screening of tomato genotypes for resistance to tomato fruit borer (helicoverpa armiger hubner) in Pakistan. The Pakistan Journal of Agricultural Sciences 48(1), 59-62.
- Söylemez, S., & Pakyürek, A. Y. (2017). Effect of different rootstocks and nutrient induced ec levels on element content of the tomato fruits. Turkish Journal of Agricultural and Natural Sciences 4(2), 155–161.
- Sun, J. & Hui, L. (2008). Data mining method for listed companies, financial distress prediction. Knowledge-Based Systems 21(1), 1-5. https://10.1016/j.knosys.2006.11.003
- Svec, L. V., Thoroughgood, C. A., & Mok, H. C. S. (1976). Chemical evaluation of vegetables grown with conventional or organic soil ammendments. Communications in Soil Science and Plant Analysis 7(2), 213-228. https://doi.org/10.1080/00103627 609366634
- Tapiero, H., Townsend, D. M., & Tew, K. D. (2004). The role of carotenoids in the prevention of human pathologies. Biomedicine and Pharmacotherapy 58(2): 100-110. https://doi.org/10.1016/j.biopha.2003.12.006
- Tatar, M., & Pirinç, V. (2017). Potential of industrial tomato production of Southeast Anatolian Region in Turkey. Iğdır University Journal of the Institute of Science and Technology 7(2), 11-20. https://doi.org/10.21597/jist.2017.121
- Tesafay, T., Gebremariam, M., Gebredsadik, K., Hagazi, M., & Girmay, S. (2018). Tomato yield and economic performance under vermicompost and mineral fertilizer applications. The Open Agriculture Journal 12(1), 262-269. https://doi.org/10.2174/1874331501812010262
- Toor, R.K., Geoffrey, P.S., & Anuschka, H. (2006). Influence of different types of fertilisers on the major antioxidant components of tomatoes. Journal of Food Composition and Analysis 19(1), 20–27. https://doi.org/10.1016/j.jfca.2005.03.003
- Wang, X., & Xing, Y. (2017). Evaluation of the effects of irrigation and fertilization on tomato fruit yield and quality: a principal component analysis. Scientific reports 7(1), 350. https://doi.org/10.1038/s41598-017-00373-8
- Yamane, T. (2001). Turkish Translation of the Basic Sampling Methods. Translators: Esin, A., Aydın, C., Bakır, M.A., Gürbüzsel, E., Literatür Yayınları, Tukey. pp.509.
- Yaraş, G., & Daşgan, H. Y. (2012). Effects of soil-applied micronized-sulphur with bentonite and organic matter on soil ph, tomato plant growth, yield and fruit quality under greenhouse conditions. Reserach Journal of Agricultural Sciences 5(1): 175-180
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Details
| Primary Language | English |
|---|---|
| Subjects | Agricultural, Veterinary and Food Sciences |
| Journal Section | RESEARCH ARTICLE |
| Authors | |
| Early Pub Date | January 21, 2024 |
| Publication Date | April 1, 2024 |
| Submission Date | December 7, 2022 |
| Acceptance Date | September 7, 2023 |
| Published in Issue | Year 2024Volume: 27 Issue: 2 |
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