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Investigation of Plant Height, Fresh Weight and Dry Weight of Sorghum with Growth Curve Models

Yıl 2024, Cilt: 27 Sayı: 4, 993 - 1004

Şenol Çelik Erdal Gönülal Halit Tutar

https://doi.org/10.18016/ksutarimdoga.vi.1310574

Öz

In this study, the sorghum plant, which is one of the most important plants in the world, was used as material. It was grown in Konya province of Türkiye, which has semi-arid climate conditions. Plant height, fresh weight, and dry weight were determined for 11 weeks during the vegetation period. To determine the shape of the plant growth, some growth models were used and the parameters of the models were tried to be defined. The coefficient of determination (R2), Pseudo R2, Mean Squares of Error (MSE), and Akaike Information Criteria (AIC) statistics were taken into account in comparing the performances of the Brody, Gompertz, Von Bertalanffy, Logistic, and Log-Logistic models. The R2, Pseudo R2, MSE and AIC values of the Gompertz model found suitable for plant height were found to be 0.998, 0.999, 23.162, and 21.013 respectively. The R2, Pseudo R2, MSE, and AIC values of the von Bertalanffy model, which was found suitable for wet weight estimation, were obtained as 0.995, 0.998, 1817.141, and 41.993 respectively. The R2, Pseudo R2, MSE, and AIC values of the Log-logistic model, which were found suitable for estimating the dry weight of the plant, were calculated as 0.998, 0.9993, 51.007, and 24.784 respectively. It can be suggested that nonlinear mathematical growth models are useful methods in terms of describing important plant characteristics such as plant height, and fresh and dry weight, calculating maximum plant height and weight, and determining the average growth rate. As a result, the growth curve models showed different results in different characteristics such as plant height, and fresh and dry weight of the plant.

Anahtar Kelimeler

Growth curve models Maturing index Inflection point Sorghum Plant Characteristics

Kaynakça

  • Abreha, K. B., Enyew, M. A., Carlsson, S., Vetukuri, R. R., Feyissa, T., Motlhaodi, T., Ng’uni, D., & Geleta, M. (2022). Sorghum in dryland: Morphological, physiological, and molecular responses of sorghum under drought stress. Planta, 255. [DOI: 10.1007/s00425-022-03808-0]
  • Alam, T., Suryanto, P., Susyanto, N., Kurniasih, B., Basunanda, P., Putra, E. T. S., Kastono, D., Respatite, D. W., Widyawan, M. H., Nurmansyah, N., Ansari, A., & Taryono, T. (2022). Performance of 45 non-linear models for determining the critical period of weed control and acceptable yield loss in soybean agroforestry systems. Sustainability, 14(13), 7636. [DOI: 10.3390/su14137636]
  • Almodares, A., Taheri, R., & Adeli, S. (2007). Inter-relationship between growth analysis and carbohydrate contents of sweet sorghum cultivars and lines. Journal of Environmental Biology, 28, 527-531.
  • Bahreini Behzadi, M. R., Aslaminejad, A. A., Sharifi, A. R., & Simianer, H. (2014). Comparison of mathematical models for describing the growth of Baluchi Sheep. J. Agr. Sci. Tech., 14, 57-68.
  • Bem, C. M., Filho, A. C., Facco, G., Schabarum, D. E., Silveira, D. L., Simões F. M., & Uliana, D. B. (2017). Growth models for morphological traits of sunn hemp. Seminar: Ciênc. Agrár., 38, 2933-2944.
  • Bem, C. M., Filho, A. C., Chaves, G. G., Kleinpaul, J. A., Pezzini, R. V., & Lavezo, A. (2018). Gompertz and logistic models for the productive traits of Sunn hemp. J. Agric. Sci., 10, 225-238.
  • Bertalanffy, L. (1957). Quantitative laws for metabolism and growth. The Quarterly Review of Biology, 32, 217-231.
  • Blasco, A., Piles, M., & Varona, L. (2003). A Bayesian analysis of the effect of selection for growth rate on growth curves in rabbits. Genet. Sel. Evol., 35, 21-41.
  • Brody, S. (1945). Bioenergetics and growth. Rheinhold Pub. Corp. N.Y.
  • Brown, J. E., Fitzhugh Jr, H. A., & Cartwright., T. C. (1976). A Comparison of nonlinear models for describing weight-age relationship in cattle. J. Anim. Sci., 42, 810-818.
  • Chrisostomo, P. H. B., Camilo, M. G., Baffa, D. F., Process, E. F., Gloria, L. S., Fernandes, A. M., & Oliveria, T. S. (2022). Biometric evaluation and nutrients of the corn, pearl millet, and sorghum crops. Pesqulsa Agropecuaria Brasilleira, Brasília, 57.
  • Echeverri, A. M. L., Bergmann, J. A. G., Toral, F. L. B., Osorio, J. P., Carmo, A. S., Mendonça, L. F., Moustacas, V. S., & Henry, M. (2013). Use of nonlinear models for describing scrotal circumference growth in Guzerat bulls raised under grazing conditions. Theriogenology, 79(5), 751-759. [DOI: 10.1016/j.theriogenology.2012.11.021]
  • FAO. (2022). Food and Agriculture Organization of the United Nations: Home. Retrieved from http://apps.fas.usda.gov/faostat/en/#data/QCL
  • Faci, J. M. (1991). Efecto de la densidad de plantas sobre la producción de sorgo bajo un suministro variable de riego. IX Jornadas Te´cnicas de Riego, 37–45, Granada, Spain.
  • Gnansounou, E., Dauriat, A., & Wyman, C. E. (2005). Refining sweet sorghum to ethanol and sugar: economic trade-offs in the context of North China. Bioresource Technology, 96, 985-1002. [DOI: 10.1016/j.biortech.2004.06.025]
  • Habyarimana, E., Laureti, D., Ninno, M. D., & Lorenzoni, C. (2004). Performances of biomass sorghum [Sorghum bicolor (L.) Moench] under different water regimes in the Mediterranean region. Industrial Crops and Products, 20, 23–28. [DOI: 10.1016/j.indcrop.2004.03.004]
  • IBM Corp. (2017). IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY: IBM Corp.
  • Impa, S. M., Perumal, R., Bean, S. R., Sunoj, V. S. J., & Jagadish, S. V. K. (2019). Water deficit and heat stress-induced alterations in grain physico-chemical characteristics and micronutrient composition in field-grown grain sorghum. J. Cereal Sci., 86, 124–131. [DOI: 10.1016/j.jcs.2019.02.004]
  • Jane, S. A., Fernandes, F. A., Silva, E. M., Muniz, J. A., Fernandes, T. J., & Pimentel, G. V. (2020). Adjusting the growth curve of sugarcane varieties using nonlinear models. Ciência Rural, 50.
  • Karizaki, A. R., Rezvantalab, N., & Gholamalipour Alamdari, E. (2022). Quantification of grain dry matter accumulation trends in barley cultivars. Plant Physiology Reports, 1-12. [DOI: 10.1007/s40502-022-00647-5]
  • Lacasa, J., Hefley, T. J., Otegui, M. E., & Ciampitti, I. A. (2021). A practical guide to estimating the light extinction coefficient with nonlinear models—a case study on maize. Plant Methods, 17(1), 1-11. [DOI: 10.1186/s13007-020-00720-2]
  • Liu, X., Woodward, J. E., Kelly, B., Lewis, K. L., Byrd, S. A., & Chen, Y. (2021). Effects of production practices on temporal disease progress of Verticillium wilt of cotton (Gossypium hirsutum L.) in the Texas High Plains, USA. Crop Protection, 140, 105429. [DOI: 10.1016/j.cropro.2020.105429]
  • Lupi, T. M., Nogales, S., Leَn, J. M., Barba, C., & Delgado, J. V. (2015). Characterization of commercial and biological growth curves in the Segureٌna sheep breed. Animal, 9(8), 1-8. [DOI: 10.1017/S1751731115000855]
  • Mastrorilli, M., Katerji, N., & Rana, G. (1999). Productivity and water use efficiency of sweet sorghum as affected by soil water deficit occurring at different vegetative growth stages. European Journal of Agronomy, 11(3-4), 207-215. [DOI: 10.1016/S1161-0301(99)00032-4]
  • Mathur, S., Umakanth, A. V., Tonapi, V. A., Sharma, R., & Sharma, M. K. (2017). Sweet sorghum as biofuel feedstock: recent advances and available resources. Biotechnol Biofuels, 10, 146. [DOI: 10.1186/s13068-017-0829-y]
  • McCann, T., Krause, D., & Sanguansri, P. (2015). Sorghum—new gluten-free ingredient and applications. Food Aust, 67(6), 24–26. [DOI: 10.1016/j.biortech.2004.06.025]
  • McLaren, J. S., Lakey, N., & Osborne, J. (2003). Sorghum as a bioresources platform for future renewable resources. Proceedings 57th Corn and Sorghum Research Conference. CD ROM. American Seed Trade Association, Alexandria, VA, USA.
  • Murungweni, C., Van Wijk, M. T., Smaling, E. M. A., & Giller, K. E. (2016). Climate-smart crop production in semi-arid areas through increased knowledge of varieties, environment, and management factors. Nutrient Cycl. Agroecosyst, 105, 183-197. [DOI: 10.1007/s10705-016-9802-0]
  • Nelder, J. A. (1961). The fitting of a generalization of the logistic curve. Biometrics, 17, 89-110. [DOI: 10.2307/2527682]
  • Pannacci, E., & Bartolini, S. (2016). Evaluation of sorghum hybrids for biomass production in central Italy. Biomass and Bioenergy, 88, 135-141. [DOI: 10.1016/j.biombioe.2016.03.014]
  • Rahemi-Karizaki, A., Khaliliaghdam, N., & Biabani, A. (2021). Predicting the time trend of dry matter accumulation and leaf area index of winter cereals under nitrogen limitation by non-linear models. Plant Physiology Reports, 26(3), 443-456. [DOI: 10.1007/s40502-021-00647-5]
  • Rządkowski, G., Rządkowski, W., & Wójcicki, P. (2015). On some connections between the Gompertz function and special numbers. Journal of Nonlinear Mathematical Physics, 22, 374–380. [DOI: 10.1080/14029251.2014.911347]
  • Sari, B. G., Lúcio, A. D. C., Santana, C. S., Olivoto, T., Diel, M. I., & Krysczun, D. K. (2019). Nonlinear growth models: An alternative to ANOVA in tomato trials evaluation. European Journal of Agronomy, 104, 21-36. [DOI: 10.1016/j.eja.2019.01.002]
  • Sari, B. G., Lúcio, A. D. C., Santana, C. S., & Savian, T. V. (2019). Describing tomato plant production using growth models. Scientia Horticulturae, 246, 146-154. [DOI: 10.1016/j.scienta.2018.11.035]
  • Shi, P. J., Men, X. Y., Sandhu, H. S., Chakraborty, A., Li, B. L., Ou-Yang, F., Sun, Y. C., & Ge, F. (2013). The “general” ontogenetic growth model is inapplicable to crop growth. Ecological Modelling, 266, 1-9. [DOI: 10.1016/j.ecolmodel.2013.06.013]
  • Sousa, I. F., Neto, J. E. K., Muniz, J. A., Guimarães, R. M., Savian, T. V., & Muniz, F. R. (2014). Fitting nonlinear autoregressive models to describe coffee seed germination. Cienc. Rural., 44, 2016-2021. [DOI: 10.1590/0103-8478cr20130118]
  • Tariq, M. M., Iqbal, F., Eyduran, E., Bajwa, M. A., & Huma, Z. (2013). Comparison of non-linear functions to describe the growth in the Mengali sheep breed of Balochistan. Pakistan Journal of Zoology, 45, 661-665. [DOI: 10.17582/journal.pjz/2013.45.3.661.665]
  • Tesso, T. T., Claflin, L. E., & Tuinstra, M. R. (2005). Analysis of stalk rot resistance and genetic diversity among drought-tolerant sorghum genotypes. Crop Sci., 45, 645-652. [DOI: 10.2135/cropsci2004.0293]
  • Tyagi, R. C., Singh, D., & Hooda, I. S. (1998). Effect of plant population, irrigation, and nitrogen on yield and its attributes of spring maize (Zea mays). Indian Journal of Agronomy, 43(4), 672–676. [DOI: 10.1007/s10681-013-0885-1]
  • Thomas, M. S. C., Annaz, D., Ansari, D., Serif, G., Jarrold, C., & Karmiloff-Smith, A. (2009). Using developmental trajectories to understand developmental disorders. Journal of Speech, Language, and Hearing Research, 52(2), 336-358. [DOI: 10.1044/1092-4388(2009/07-0203)]
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Sorgumun Bitki Boyu, Taze Ağırlığı ve Kuru Ağırlığının Büyüme Eğrisi Modelleri ile Araştırılması

Yıl 2024, Cilt: 27 Sayı: 4, 993 - 1004

Şenol Çelik Erdal Gönülal Halit Tutar

https://doi.org/10.18016/ksutarimdoga.vi.1310574

Öz

Bu çalışmada dünyanın en önemli bitkilerinden biri olan sorgum bitkisi materyal olarak kullanılmıştır. Bu bitki Türkiye'nin yarı kurak iklim koşullarına sahip Konya ilinde yetiştirilmiştir. Vejetasyon döneminde 11 hafta boyunca bitki boyu, yaş ağırlığı ve kuru ağırlığı ölçülmüştür. Bitkide büyümenin şeklini belirlemek amacıyla bazı büyüme modelleri kullanılmış ve modellerin parametreleri tanımlanmaya çalışılmıştır. Brody, Gompertz, Von Bertalanffy, Logistic ve Log-Logistic modellerinin karşılaştırılmasında belirleme katsayısı (R2), Pseudo R2, Hata Kareler Ortalaması ve Akaike Bilgi Kriteri istatistikleri dikkate alınmıştır. Bitki boyu için uygun bulunan Gompertz modelinin R2, Pseudo R2, Hata Kareler Ortalaması ve Akaike Bilgi Kriteri değerleri sırasıyla 0.998, 0.999, 23.162 ve 21.013 olarak bulunmuştur. Yaş ağırlık için uygun bulunan Von Bertalanffy modelinin R2, Pseudo R2, Hata Kareler Ortalaması ve Akaike Bilgi Kriteri değerleri sırasıyla 0.995, 0.998, 1817.141 ve 41.993 olarak elde edilmiştir. Kuru ağırlık için uygun bulunan Log-Logistik modelinin R2, Pseudo R2, Hata Kareler Ortalaması ve Akaike Bilgi Kriteri değerleri sırasıyla 0.998, 0.9993, 51.007 ve 24.784 olarak hesaplanmıştır. Bitki boyu, yaş ve kuru ağırlık gibi önemli bitki özelliklerinin tanımlanması, maksimum bitki boyu ve ağırlığının hesaplanması ve ortalama büyüme hızının belirlenmesi açısından doğrusal olmayan matematiksel büyüme modellerinin faydalı yöntemler olduğu önerilebilir. Sonuç olarak sorgum bitkisinde bitki boyu, bitkinin yaş ve kuru ağırlığı gibi farklı özelliklerinde büyüme eğrisi modelleri farklı sonuçlar göstermiştir.

Anahtar Kelimeler

Büyüme eğrisi modelleri Olgunlaşma indeksi Büküm noktası Sorgum Bitki karakteristikleri

Kaynakça

  • Abreha, K. B., Enyew, M. A., Carlsson, S., Vetukuri, R. R., Feyissa, T., Motlhaodi, T., Ng’uni, D., & Geleta, M. (2022). Sorghum in dryland: Morphological, physiological, and molecular responses of sorghum under drought stress. Planta, 255. [DOI: 10.1007/s00425-022-03808-0]
  • Alam, T., Suryanto, P., Susyanto, N., Kurniasih, B., Basunanda, P., Putra, E. T. S., Kastono, D., Respatite, D. W., Widyawan, M. H., Nurmansyah, N., Ansari, A., & Taryono, T. (2022). Performance of 45 non-linear models for determining the critical period of weed control and acceptable yield loss in soybean agroforestry systems. Sustainability, 14(13), 7636. [DOI: 10.3390/su14137636]
  • Almodares, A., Taheri, R., & Adeli, S. (2007). Inter-relationship between growth analysis and carbohydrate contents of sweet sorghum cultivars and lines. Journal of Environmental Biology, 28, 527-531.
  • Bahreini Behzadi, M. R., Aslaminejad, A. A., Sharifi, A. R., & Simianer, H. (2014). Comparison of mathematical models for describing the growth of Baluchi Sheep. J. Agr. Sci. Tech., 14, 57-68.
  • Bem, C. M., Filho, A. C., Facco, G., Schabarum, D. E., Silveira, D. L., Simões F. M., & Uliana, D. B. (2017). Growth models for morphological traits of sunn hemp. Seminar: Ciênc. Agrár., 38, 2933-2944.
  • Bem, C. M., Filho, A. C., Chaves, G. G., Kleinpaul, J. A., Pezzini, R. V., & Lavezo, A. (2018). Gompertz and logistic models for the productive traits of Sunn hemp. J. Agric. Sci., 10, 225-238.
  • Bertalanffy, L. (1957). Quantitative laws for metabolism and growth. The Quarterly Review of Biology, 32, 217-231.
  • Blasco, A., Piles, M., & Varona, L. (2003). A Bayesian analysis of the effect of selection for growth rate on growth curves in rabbits. Genet. Sel. Evol., 35, 21-41.
  • Brody, S. (1945). Bioenergetics and growth. Rheinhold Pub. Corp. N.Y.
  • Brown, J. E., Fitzhugh Jr, H. A., & Cartwright., T. C. (1976). A Comparison of nonlinear models for describing weight-age relationship in cattle. J. Anim. Sci., 42, 810-818.
  • Chrisostomo, P. H. B., Camilo, M. G., Baffa, D. F., Process, E. F., Gloria, L. S., Fernandes, A. M., & Oliveria, T. S. (2022). Biometric evaluation and nutrients of the corn, pearl millet, and sorghum crops. Pesqulsa Agropecuaria Brasilleira, Brasília, 57.
  • Echeverri, A. M. L., Bergmann, J. A. G., Toral, F. L. B., Osorio, J. P., Carmo, A. S., Mendonça, L. F., Moustacas, V. S., & Henry, M. (2013). Use of nonlinear models for describing scrotal circumference growth in Guzerat bulls raised under grazing conditions. Theriogenology, 79(5), 751-759. [DOI: 10.1016/j.theriogenology.2012.11.021]
  • FAO. (2022). Food and Agriculture Organization of the United Nations: Home. Retrieved from http://apps.fas.usda.gov/faostat/en/#data/QCL
  • Faci, J. M. (1991). Efecto de la densidad de plantas sobre la producción de sorgo bajo un suministro variable de riego. IX Jornadas Te´cnicas de Riego, 37–45, Granada, Spain.
  • Gnansounou, E., Dauriat, A., & Wyman, C. E. (2005). Refining sweet sorghum to ethanol and sugar: economic trade-offs in the context of North China. Bioresource Technology, 96, 985-1002. [DOI: 10.1016/j.biortech.2004.06.025]
  • Habyarimana, E., Laureti, D., Ninno, M. D., & Lorenzoni, C. (2004). Performances of biomass sorghum [Sorghum bicolor (L.) Moench] under different water regimes in the Mediterranean region. Industrial Crops and Products, 20, 23–28. [DOI: 10.1016/j.indcrop.2004.03.004]
  • IBM Corp. (2017). IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY: IBM Corp.
  • Impa, S. M., Perumal, R., Bean, S. R., Sunoj, V. S. J., & Jagadish, S. V. K. (2019). Water deficit and heat stress-induced alterations in grain physico-chemical characteristics and micronutrient composition in field-grown grain sorghum. J. Cereal Sci., 86, 124–131. [DOI: 10.1016/j.jcs.2019.02.004]
  • Jane, S. A., Fernandes, F. A., Silva, E. M., Muniz, J. A., Fernandes, T. J., & Pimentel, G. V. (2020). Adjusting the growth curve of sugarcane varieties using nonlinear models. Ciência Rural, 50.
  • Karizaki, A. R., Rezvantalab, N., & Gholamalipour Alamdari, E. (2022). Quantification of grain dry matter accumulation trends in barley cultivars. Plant Physiology Reports, 1-12. [DOI: 10.1007/s40502-022-00647-5]
  • Lacasa, J., Hefley, T. J., Otegui, M. E., & Ciampitti, I. A. (2021). A practical guide to estimating the light extinction coefficient with nonlinear models—a case study on maize. Plant Methods, 17(1), 1-11. [DOI: 10.1186/s13007-020-00720-2]
  • Liu, X., Woodward, J. E., Kelly, B., Lewis, K. L., Byrd, S. A., & Chen, Y. (2021). Effects of production practices on temporal disease progress of Verticillium wilt of cotton (Gossypium hirsutum L.) in the Texas High Plains, USA. Crop Protection, 140, 105429. [DOI: 10.1016/j.cropro.2020.105429]
  • Lupi, T. M., Nogales, S., Leَn, J. M., Barba, C., & Delgado, J. V. (2015). Characterization of commercial and biological growth curves in the Segureٌna sheep breed. Animal, 9(8), 1-8. [DOI: 10.1017/S1751731115000855]
  • Mastrorilli, M., Katerji, N., & Rana, G. (1999). Productivity and water use efficiency of sweet sorghum as affected by soil water deficit occurring at different vegetative growth stages. European Journal of Agronomy, 11(3-4), 207-215. [DOI: 10.1016/S1161-0301(99)00032-4]
  • Mathur, S., Umakanth, A. V., Tonapi, V. A., Sharma, R., & Sharma, M. K. (2017). Sweet sorghum as biofuel feedstock: recent advances and available resources. Biotechnol Biofuels, 10, 146. [DOI: 10.1186/s13068-017-0829-y]
  • McCann, T., Krause, D., & Sanguansri, P. (2015). Sorghum—new gluten-free ingredient and applications. Food Aust, 67(6), 24–26. [DOI: 10.1016/j.biortech.2004.06.025]
  • McLaren, J. S., Lakey, N., & Osborne, J. (2003). Sorghum as a bioresources platform for future renewable resources. Proceedings 57th Corn and Sorghum Research Conference. CD ROM. American Seed Trade Association, Alexandria, VA, USA.
  • Murungweni, C., Van Wijk, M. T., Smaling, E. M. A., & Giller, K. E. (2016). Climate-smart crop production in semi-arid areas through increased knowledge of varieties, environment, and management factors. Nutrient Cycl. Agroecosyst, 105, 183-197. [DOI: 10.1007/s10705-016-9802-0]
  • Nelder, J. A. (1961). The fitting of a generalization of the logistic curve. Biometrics, 17, 89-110. [DOI: 10.2307/2527682]
  • Pannacci, E., & Bartolini, S. (2016). Evaluation of sorghum hybrids for biomass production in central Italy. Biomass and Bioenergy, 88, 135-141. [DOI: 10.1016/j.biombioe.2016.03.014]
  • Rahemi-Karizaki, A., Khaliliaghdam, N., & Biabani, A. (2021). Predicting the time trend of dry matter accumulation and leaf area index of winter cereals under nitrogen limitation by non-linear models. Plant Physiology Reports, 26(3), 443-456. [DOI: 10.1007/s40502-021-00647-5]
  • Rządkowski, G., Rządkowski, W., & Wójcicki, P. (2015). On some connections between the Gompertz function and special numbers. Journal of Nonlinear Mathematical Physics, 22, 374–380. [DOI: 10.1080/14029251.2014.911347]
  • Sari, B. G., Lúcio, A. D. C., Santana, C. S., Olivoto, T., Diel, M. I., & Krysczun, D. K. (2019). Nonlinear growth models: An alternative to ANOVA in tomato trials evaluation. European Journal of Agronomy, 104, 21-36. [DOI: 10.1016/j.eja.2019.01.002]
  • Sari, B. G., Lúcio, A. D. C., Santana, C. S., & Savian, T. V. (2019). Describing tomato plant production using growth models. Scientia Horticulturae, 246, 146-154. [DOI: 10.1016/j.scienta.2018.11.035]
  • Shi, P. J., Men, X. Y., Sandhu, H. S., Chakraborty, A., Li, B. L., Ou-Yang, F., Sun, Y. C., & Ge, F. (2013). The “general” ontogenetic growth model is inapplicable to crop growth. Ecological Modelling, 266, 1-9. [DOI: 10.1016/j.ecolmodel.2013.06.013]
  • Sousa, I. F., Neto, J. E. K., Muniz, J. A., Guimarães, R. M., Savian, T. V., & Muniz, F. R. (2014). Fitting nonlinear autoregressive models to describe coffee seed germination. Cienc. Rural., 44, 2016-2021. [DOI: 10.1590/0103-8478cr20130118]
  • Tariq, M. M., Iqbal, F., Eyduran, E., Bajwa, M. A., & Huma, Z. (2013). Comparison of non-linear functions to describe the growth in the Mengali sheep breed of Balochistan. Pakistan Journal of Zoology, 45, 661-665. [DOI: 10.17582/journal.pjz/2013.45.3.661.665]
  • Tesso, T. T., Claflin, L. E., & Tuinstra, M. R. (2005). Analysis of stalk rot resistance and genetic diversity among drought-tolerant sorghum genotypes. Crop Sci., 45, 645-652. [DOI: 10.2135/cropsci2004.0293]
  • Tyagi, R. C., Singh, D., & Hooda, I. S. (1998). Effect of plant population, irrigation, and nitrogen on yield and its attributes of spring maize (Zea mays). Indian Journal of Agronomy, 43(4), 672–676. [DOI: 10.1007/s10681-013-0885-1]
  • Thomas, M. S. C., Annaz, D., Ansari, D., Serif, G., Jarrold, C., & Karmiloff-Smith, A. (2009). Using developmental trajectories to understand developmental disorders. Journal of Speech, Language, and Hearing Research, 52(2), 336-358. [DOI: 10.1044/1092-4388(2009/07-0203)]
  • USDA-FAS. (2018). World Agricultural Production. Circular Series WAP10-18 [DOI: 10.1021/jf508703f]
  • Üçkardeş, F., Korkmaz, M., & Ocal, P. (2013). Comparison of models and estimation of missing parameters of some mathematical models related to in situ dry matter degradation. Journal of Animal and Plant Sciences, 23, 999–1007. [DOI: 10.11648/j.ajae.20130103.12]
  • Winsor, C. P. (1932). The Gompertz curve as a growth curve. Proc. National Academy of Science, 18(1), 1-8. [DOI: 10.1073/pnas.18.1.1]
  • Yavuz, E., Önem, A. B., Kaya, F., Çanga, D., & Şahin, M. (2019). Modeling of individual growth curves in Japanese quails. Black Sea Journal of Engineering and Science, 2(1), 11-15. [DOI: 10.32569/bsengineering.615738]
Toplam 44 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Çayır-Mera ve Yem Bitkileri
Bölüm ARAŞTIRMA MAKALESİ (Research Article)
Yazarlar

Şenol Çelik 0000-0001-5894-8986

Erdal Gönülal 0000-0002-1621-0892

Halit Tutar 0000-0002-9341-3503

Erken Görünüm Tarihi 23 Nisan 2024
Yayımlanma Tarihi
Gönderilme Tarihi 7 Haziran 2023
Kabul Tarihi 19 Ekim 2023
Yayımlandığı Sayı Yıl 2024Cilt: 27 Sayı: 4

Kaynak Göster

APA Çelik, Ş., Gönülal, E., & Tutar, H. (2024). Investigation of Plant Height, Fresh Weight and Dry Weight of Sorghum with Growth Curve Models. Kahramanmaraş Sütçü İmam Üniversitesi Tarım Ve Doğa Dergisi, 27(4), 993-1004. https://doi.org/10.18016/ksutarimdoga.vi.1310574
 Kapak Resmi İndir

21082



2022-JIF = 0.500

2022-JCI = 0.170

Uluslararası Hakemli Dergi (International Peer Reviewed Journal)

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      Yılda 6 sayı yayınlanır. (Published 6 times a year)


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