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Tytuł artykułu

Statistical yielding models of some irrigated vegetable crops in dependence on water use and heat supply

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Statistical analysis is helpful for better understanding of the processes which take place in agricultural ecosystems. Particular attention should be paid to the processes of crops’ productivity formation under the influence of natural and anthropogenic factors. The goal of our study was to provide new theoretical knowledge about the dependence of vegetable crops’ productivity on water supply and heat income. The study was conducted in the irrigated conditions of the semi-arid cold Steppe zone on the fields of the Institute of Irrigated Agriculture of NAAS, Kherson, Ukraine. We studied the historical data of productivity of three most common in the region vegetable crops: potato, tomato, onion. The crops were cultivated by using the generally accepted in the region agrotechnology. Historical yielding and meteorological data of the period 1990–2016 were used to develop the models of the vegetable crops’ productivity. We used two approaches: development of pair linear models in three categories (“yield – water use”, “yield – sum of the effective air temperatures above 10°C”); development of complex linear regression models taking into account such factors as total water use, and temperature regime during the crops’ vegetation. Pair linear models of the crops’ productivity showed that the highest effect on the yields of potato and onion has the water use index (R2 of 0.9350 and 0.9689, respectively), and on the yield of tomato – temperature regime (R2 of 0.9573). The results of pair analysis were proved by the multiple regression analysis that revealed the same tendencies in the crop yield formation depending on the studied factors.
Wydawca
Rocznik
Tom
Strony
190--197
Opis fizyczny
Bibliogr. 31 poz., tab.
Twórcy
  • Institute of Irrigated Agriculture, Naddniprianske, 73483, Kherson, Ukraine
  • Institute of Irrigated Agriculture, Naddniprianske, 73483, Kherson, Ukraine
  • Institute of Irrigated Agriculture, Naddniprianske, 73483, Kherson, Ukraine
  • Institute of Irrigated Agriculture, Naddniprianske, 73483, Kherson, Ukraine
  • Institute of Irrigated Agriculture, Naddniprianske, 73483, Kherson, Ukraine
  • Institute of Irrigated Agriculture, Naddniprianske, 73483, Kherson, Ukraine
  • Kherson State Agrarian University, Faculty of Agronomy, Kherson, Ukraine
Bibliografia
  • AL-JAMAL M.S., SAMMIS T.W., BALL S., SMEAL D. 1999. Yield-based, irrigated onion crop coefficients. Applied Engineering in Agriculture. Vol. 15 (6) p. 659–668. DOI 10.13031/2013. 5835.
  • ALLEN R.G., PEREIRA L.S., RAES D., SMITH M. 1998. Crop evapotranspiration – guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56, 300(9), D05109. Rome. FAO.
  • BAIER W. 1979. Note on the terminology of crop – weather models. Agricultural Meteorology. Vol. 20 (2) p. 137–145.
  • BECK H.E., ZIMMERMANN N.E., MCVICAR T.R., VERGOPOLAN N., BERG A., WOOD E.F. 2018. Present and future Koppen-Geiger climate classification maps at 1-km resolution. Scientific Data. Vol. 5 p. 180–214. DOI 10.1038/sdata.2018.214.
  • BUTT D.J., ROYLE D.J. 1974. Multiple regression analysis in the epidemiology of plant diseases. In: Epidemics of plant diseases. Berlin. Springer p. 78–114.
  • CAMEJO D., RODRÍGUEZ P., MORALES M.A., DELL’AMICO J.M., TORRECILLAS A., ALARCÓN J.J. 2005. High temperature effects on photosynthetic activity of two tomato cultivars with different heat susceptibility. Journal of Plant Physiology. Vol. 162 (3) p. 281–289.
  • CHEN J., KANG S., DU T., GUO P., QIU R., CHEN R., GU F. 2014. Modeling relations of tomato yield and fruit quality with water deficit at different growth stages under greenhouse condition. Agricultural Water Management. Vol. 146 p. 131–148.
  • DE VISSER C.L.M. 1994a. ALCEPAS, an onion growth model based on SUCROS87. I. Development of the model. Journal of Horticultural Science. Vol. 69 (3) p. 501–518.
  • DE VISSER C.L.M. 1994b. ALCEPAS, an onion growth model based on SUCROS87. II. Validation of the model. Journal of Horticultural Science. Vol. 69 (3) p. 519–525.
  • DOMÍNGUEZ A., TARJUELO J.M., DE JUAN J.A., LÓPEZ-MATA E., BREIDY J., KARAM F. 2011. Deficit irrigation under water stress and salinity conditions: The MOPECO-Salt Model. Agricultural Water Management. Vol. 98 (9) p. 1451–1461.
  • ENGELSTAD O.P. 1968. Use of multiple regression in fertilizer evaluation. Agronomy Journal. Vol. 60 (3) p. 327–329.
  • FAN X.R., KANG M.Z., HEUVELINK E., DE REFFYE P., HU B.G. 2015. A knowledge-and-data-driven modeling approach for simulating plant growth: A case study on tomato growth. Ecological Modelling. Vol. 312 p. 363–373.
  • GARCIA A.G., GUERRA L.C., HOOGENBOOM G. 2009. Water use and water use efficiency of sweet corn under different weather conditions and soil moisture regimes. Agricultural Water Management. Vol. 96 (10) p. 1369–1376.
  • GORNOTT C., WECHSUNG F. 2016. Statistical regression models for assessing climate impacts on crop yields: A validation study for winter wheat and silage maize in Germany. Agricultural and Forest Meteorology. Vol. 217 p. 89–100.
  • HAVERKORT A.J., HARRIS P.M. 1987. A model for potato growth and yield under tropical highland conditions. Agricultural and Forest Meteorology. Vol. 39(4) p. 271–282.
  • HOOGENBOOM G.J., WHITE J.W., MESSINA C.D. 2004. From genome to crop: Integration through simulation modelling. Field Crops Research. Vol. 90 p. 145–163.
  • JEFFERIES R.A., HEILBRONN T.D. 1991. Water stress as a constraint on growth in the potato crop. 1. Model development. Agricultural and Forest Meteorology, Vol. 53 (3) p. 185–196.
  • JONES J.W., DAYAN E., ALLEN L.H., vAN KEULEN H., CHALLA H. 1991. A dynamic tomato growth and yield model (TOMGRO). Transactions of the ASAE. Vol. 34 (2) p. 663–672.
  • JONES J.W., HOOGENBOOM G., PORTER C.H., BOOTE K.J., BATCHELOR W.D., HUNT L.A., WILKENS P.W., SINGH U., GIJSMAN A.J., RITCHIE J.T. 2003. The DSSAT cropping system model. European Journal of Agronomy. Vol. 18 (3–4) p. 235–265. DOI 10.1016/S1161-0301(02)00107-7.
  • KOOMAN P.L., HAVERKORT A.J. 1995. Modelling development and growth of the potato crop influenced by temperature and daylength: LINTUL-POTATO. In: Potato ecology and modelling of crops under conditions limiting growth. Dordrecht. Springer p. 41–59.
  • LETEY J., DINAR A., KNAPP K.C. 1985. Crop-water production function model for saline irrigation waters. Soil Science Society of America Journal. Vol. 49 p. 1005–1009. DOI 10.2136/ sssaj1985.03615995004900040043x
  • LOBELL D.B., CAHILL K.N., FIELD C.B. 2007. Historical effects of temperature and precipitation on California crop yields. Climatic Change. Vol. 81 (2) p. 187–203.
  • MISHRA P., SARKAR C., VISHWAJITH K.P., DHEKALE B.S., SAHU P.K. 2013. Instability and forecasting using ARIMA model in area, production and productivity of onion in India. Journal of Crop and Weed. Vol. 9 (2) p. 96–101.
  • MUKHERJEE J., SASTRI C.V. 2004. Fruit yield predicting model of tomato using spectral and hyperspectral indices. Journal of the Indian Society of Remote Sensing. Vol. 32 (3) p. 301–306.
  • NELSON W.L., DALE R.F. 1978. Effect of trend or technology variables and record period on prediction of corn yields with weather variables. Journal of Applied Meteorology. Vol. 17 (7) p. 926–933.
  • PASSIOURA J.B. 1996. Simulation models: Science, snake oil, education, or engineering? Agronomy Journal. Vol. 88 p. 690–694. DOI 10.2134/agronj1996.00021962008800050002x.
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  • SAMMIS T.W., AL-JAMMAL M.S., BALL S., SMEAL D. 2000. Crop water use of onion. In: The 6th International Microirrigation Congress (Micro 2000). South Africa, 22–27 October 2000. International Commission on Irrigation and Drainage (ICID). Cape Town p. 1–9.
  • TEI F., AIKMAN D.P., SCAIFE A. 1996. Growth of lettuce, onion and red beet. 2. Growth modelling. Annals of Botany. Vol. 78 (5) p. 645–652.
  • WHISLER F.D., ACOCK B., BAKER D.N., FYE R.E., HODGES H.F., LAMBERT J.R., LEMMON H.E., MC KINON J.M., REDDY V.R. 1986. Crop simulation models in agronomic systems. Advances in Agronomy. Vol. 40 p. 141–208. DOI 10.1016/ S0065-2113(08)60282-5.
  • ZOBEL R.W., WRIGHT M.J., GAUCH H.G. 1988. Statistical analysis of a yield trial. Agronomy Journal. Vol. 80 (3) p. 388–393.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7b8fab4e-dc7d-40bd-833f-5bad04de4600
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