Adaptation of Winter Wheat Cultivars to Different Environments: A Case Study in Poland
Abstract
:1. Introduction
2. Materials and Methods
2.1. Yield Dataset and Environmental Variables
2.2. Statistical Analyses
2.2.1. Calculation of Expected Productivity
2.2.2. Calculation of Relative Productivity of Cultivars (Genotype Specific Reaction)
2.2.3. Recommendation of Cultivars
3. Results
3.1. Calculation of Expected Productivity
3.2. The Impact of Environmental Variables (Covariates)
3.3. Recommendation of Cultivars
4. Discussion
4.1. Main Effects of Cultivars on Wheat Productivity
4.2. The Impact of Environmental Factors: Soil and Weather
4.2.1. Soil
4.2.2. Weather
4.3. Recommendation of Cultivars and Validation
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- FAO. World Food and Agriculture—Statistical Pocketbook 2018; FAO: Rome, Italy, 2018; 254p. [Google Scholar]
- GUS. Agriculture in 2017, Warsaw. 2018. Available online: http://stat.gov.pl/en/topics/agriculture-forestry/agriculture/agriculture-in-2017,4,14.html (accessed on 15 February 2019).
- Gozdowski, D.; Stępień, M.; Samborski, S.; Dobers, E.S.; Szatyłowicz, J.; Chormański, J. Determination of the most relevant soil properties for the delineation of management zones in production fields. Commun. Soil Sci. Plant Anal. 2014, 45, 2289–2304. [Google Scholar] [CrossRef]
- Mądry, W.; Iwańska, M. Measures of genotype wide adaptation level and their relationships in winter wheat. Cereal Res. Commun. 2012, 40, 592–601. [Google Scholar] [CrossRef]
- Mueller, L.; Schindler, U.; Mirschel, W.; Shepherd, T.G.; Ball, B.C.; Heliming, K.; Rogasik, J.; Eulenstein, F.; Wiggering, H. Assessing the productive function of soils. A review. Agron. Sustain. Dev. 2010, 30, 601–614. [Google Scholar] [CrossRef]
- Hanson, A.D.; Nelsen, C.E. Water: Adaptation of crops to drought–prone environments. In The Biology of Crop Productivity; Carlson, P.S., Ed.; Academic Press: New York, NY, USA, 1980; pp. 77–152. [Google Scholar]
- Wheeler, T.R.; Craufurd, P.Q.; Ellis, R.H.; Porter, J.R.; Prasad, P.V. Air temperature variability and the yield of annual crops. Agric. Ecosyst. Environ. 2000, 82, 159–167. [Google Scholar] [CrossRef]
- Podolska, G. Plon i jakość ziarna pszenicy ozimej uprawianej w warunkach wysokiej temperatury oraz stresu suszy (Yield and quality of winter wheat grain grown under high temperature and drought stress, in Polish). Studia Raporty IUNG–PIB 2018, 57, 9–21. [Google Scholar]
- Вabushkina, E.A.; Belokopytova, L.V.; Zhirnova, D.F.; Shah, S.K.; Kostyakova, T.V. Climatically driven yield variability of major crops in Khakassia (South Siberia). Int. J. Biometeorol. 2018, 62, 939–948. [Google Scholar] [CrossRef] [Green Version]
- Carew, R.; Smith, E.G.; Grant, C. Factors influencing wheat yield and variability: Evidence from Manitoba, Canada. J. Agric. Appl. Econ. 2009, 41, 625–639. [Google Scholar] [CrossRef] [Green Version]
- Wójcik–Gront, E. Variables influencing yield–scaled Global Warming Potential and yield of winter wheat production. Field Crops Res. 2018, 227, 19–29. [Google Scholar] [CrossRef]
- Iwańska, M.; Stępień, M. The effect of soil and course of weather conditions during the growth and maturation of winter wheat on yields in multi-environmental trials. In Proceedings of the XLIXth International Biometrical Colloquium, Siedlce, Poland, 8–12 September 2017. [Google Scholar]
- Iwańska, M.; Stępień, M. The effect of soil and weather conditions on yields of winter wheat in multi-environmental trials. Biom. Lett. 2019, 56, 263–279. [Google Scholar] [CrossRef] [Green Version]
- Rodrigues, P.C. Statistical methods to detect and understand genotype-by-environment interaction and QTL-by-environment interaction. Biom. Lett. 2018, 55, 123–138. [Google Scholar] [CrossRef] [Green Version]
- Paderewski, J.; Rodrigues, P.C. Constrained AMMI Model: Application to Polish Winter Wheat Post-Registration Data. Crop Sci. 2018, 58, 1458–1469. [Google Scholar] [CrossRef] [Green Version]
- Yates, F.; Cochran, W.G. The analysis of groups of experiments. J. Agric. Sci. 1938, 28, 556–580. [Google Scholar] [CrossRef]
- Finlay, K.W.; Wilkinson, G.N. The analysis of adaptation in a plant-breeding programme. Aust. J. Agric. Res. 1963, 14, 742–754. [Google Scholar] [CrossRef] [Green Version]
- Pereira, D.; Rodrigues, P.C.; Mejza, S.; Mexia, J.T. A comparison between Joint Regression Analysis and the AMMI model: A case study with barley. J. Stat. Comput. Simul. 2012, 82, 193–207. [Google Scholar] [CrossRef]
- COBORU. Available online: http://www.coboru.pl (accessed on 18 February 2019).
- Zarządzenie nr 3/2019 z dnia 31 stycznia 2019 roku Dyrektora Centralnego Ośrodka Badania Odmian Roślin Uprawnych w Słupi Wielkiej w sprawie Porejestrowego doświadczalnictwa odmianowego (Ordinance No. 3/2019 of 31 January 2019 of the Director of the Research Centre for Cultivar Testing (COBORU) in Słupia Wielka regarding nation-wide variety testing system, called post-registration variety testing (PDO), in Polish). Available online: http://www.coboru.pl/DR/Pliki/Zarzadzenie.pdf (accessed on 9 July 2019).
- Berg, J.E.; Bruckner, P.L.; Carr, P.; Chen, C.; Cook, C.; Eberly, J.; Fang, Z.T.; Kephart, K.D.; Kowatch–Carlson, K.; Lamb, P.; et al. Winter Wheat Variety Performance Summary in Montana. 2019. Available online: http://plantsciences.montana.edu/crops (accessed on 9 July 2019).
- AHDB. Recommended Lists. Crop Committee Handbook. 2018. Available online: https://projectblue.blob.core.wndows.net/media/Default/Imported%20Publication%20Docs/AHDB%20Cereals%20&%20Oilseeds/Varieties/RL%20Crop%20Committee%20Handbook.pdf (accessed on 19 February 2019).
- Rubel, F.; Kottek, M. Observed and projected climate shifts 1901–2100 depicted by world maps of the Köppen-Geiger climate classification. Meteorol. Z. 2010, 19, 135–141. [Google Scholar] [CrossRef] [Green Version]
- Witek, T.; Górski, T.; Kern, H.; Bartoszewski, Z.; Biesiadzki, A.; Budzyńska, K.; Demidowicz, G.; Deputat, T.; Flaczyk, Z.; Gałecki, Z.; et al. Waloryzacja rolniczej przestrzeni produkcyjnej Polski wg gmin (Valorization of Productive Agricultural Area of Poland in Districts, in Polish); IUNiG: Puławy, Poland, 1981; pp. 1–416. [Google Scholar]
- Mądry, W.; Paderewski, J.; Gozdowski, D.; Rozbicki, J.; Golba, J.; Piechociński, M.; Studnicki, M.; Derejko, A. Adaptation of winter wheat cultivars to crop managements and Polish agricultural environments. Turkish J. Field Crop. 2013, 18, 118–127. [Google Scholar]
- Studnicki, M.; Derejko, A.; Wójcik–Gront, E.; Kosma, M. Adaptation patterns of winter wheat cultivars in agro–ecological regions. Sci. Agric. 2019, 76, 148–156. [Google Scholar] [CrossRef]
- Selyaninov, G.T. Methods of climate description to agricultural purposes. In World Climate and Agriculture Handbook; Selyaninov, G.T., Ed.; Gidrometeoizdat: Leningrad, Russia, 1937; pp. 5–27. [Google Scholar]
- Radomski, C. Państwowe Wydawnictwo Rolnicze i Leśne; Agrometeorologia: Warsaw, Poland, 1987; pp. 1–544. [Google Scholar]
- Jadczyszyn, J.; Niedźwiecki, J.; Debaene, G. Analysis of agronomic categories in different soil texture classification systems. Pol. J. Soil Sci. 2016, 49, 61–72. [Google Scholar] [CrossRef] [Green Version]
- Meier, U. BBCH-Monograph. Growth Stages of Plants—Entwicklungsstadien von Pflanzen—Estadios de las plantas—Dévelopement des Plantes; Blackwell Wissenschaftsverlag: Berlin, Germany; Wien, NY, USA, 1997. [Google Scholar]
- Zadoks, J.C.; Chang, T.T.; Konzak, C.F. A decimal code for the growth stages of cereals. Weed Res. 1974, 14, 415–421. [Google Scholar] [CrossRef]
- Skowera, B.; Puła, J. Skrajne warunki pluwiotermiczne w okresie wiosennym na obszarze Polski w latach 1971–2000 (Pluviometric extreme conditions in spring season in Poland in the years 1971–2000, in Polish). Acta Agrophys. 2004, 3, 171–177. [Google Scholar]
- Szewrański, S.; Kazak, J.; Żmuda, R.; Wawer, R. Indicator-based assessment for soil resource management in the Wrocław larger urban zone of Poland. Pol. J. Environ. Stud. 2017, 26, 2239–2248. [Google Scholar] [CrossRef] [Green Version]
- De Martonne, E. Traité de Géographie Physique; Colin: Paris, France, 1925. [Google Scholar]
- Baltas, E. Spatial distribution of climatic indices in northern Greece. Meteorol. Appl. 2007, 14, 69–78. [Google Scholar] [CrossRef]
- McSherry, M.E.; Ritchie, M.E. Effects of grazing on grassland soil carbon: A global review. Glob. Chang. Biol. 2013, 19, 1347–1357. [Google Scholar] [CrossRef]
- COBORU. Listy Odmian Zalecanych do Uprawy na Obszarze Województw 2019 (Lists of Varieties Recommended for Cultivation on the Territory of the Voivodeship in 2019, in Polish). Available online: http://www.coboru.pl/Publikacje_COBORU/LOZ/LOZ_2019.pdf (accessed on 15 November 2019).
- R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2017; Available online: https://www.R-project.org/ (accessed on 15 November 2019).
- Venables, W.N.; Ripley, B.D. Modern Applied Statistics with S, 4th ed.; Springer: Berlin/Heidelberg, Germany, 2002. [Google Scholar]
- Sabaghnia, N.; Sabaghpour, S.H.; Dehghani, H. The use of an AMMI model and its parameters to analyse yield stability in multi-environment trials. J. Agric. Sci. 2008, 146, 571–581. [Google Scholar] [CrossRef]
- Purchase, J.L.; Hatting, H.; van Deventer, C.S. Genotype × environment interaction of winter wheat (Triticum aestivum L.) in South Africa: II. Stability analysis of yield performance. S. Afr. J. Plant Soil 2000, 17, 101–107. [Google Scholar] [CrossRef]
- Speed, T.P.; Yu, B. Model selection and prediction: Normal regression. Ann. Inst. Stat. Math. 1993, 45, 35–54. [Google Scholar] [CrossRef]
- Babyak, M.A. What you see may not be what you get: A brief, nontechnical introduction to overfitting in regression-type models. Psychosom. Med. 2004, 66, 411–421. [Google Scholar] [CrossRef] [Green Version]
- Rynkiewicz, J. General bound of overfitting for MLP regression models. Neurocomputing 2012, 90, 106–110. [Google Scholar] [CrossRef] [Green Version]
- Mądry, W.; Derejko, A.; Studnicki, M.; Paderewski, J.; Gacek, E. Response of winter wheat cultivars to crop management and environment in post–registration trials. Czech J. Genet. Plant Breed. 2017, 53, 76–82. [Google Scholar] [CrossRef]
- Rozbicki, J.; Gozdowski, D.; Studnicki, M.; Mądry, W.; Golba, J.; Sobczyński, G.; Wijata, M. Management intensity effects on grain yield and its quality traits of winter wheat cultivars in different environments in Poland. Biotechnology 2019, 22, 2. [Google Scholar] [CrossRef]
- Praczyk, T. Regulatory wzrostu i rozwoju roślin (Plant Growth Regulators, in Polish). 2013. Available online: https://www.agro.basf.pl/Documents/broszury/regulatory-wzrostu-i-rozwoju-roslin-broszura.pdf (accessed on 30 January 2020).
- Witek, T. Wpływ jakości gleby na plonowanie roślin uprawnych (Influence of the quality of soils on yielding of crops, in Polish). Zesz. Probl. Postęp. Nauk Rol. 1979, 224, 35–47. [Google Scholar]
- Fotyma, M.; Zięba, S. Przyrodnicze i Gospodarcze Podstawy Wapnowania gleb (Natural and Economic Base of Soil Liming, in Polish); PWRiL: Warszawa, Poland, 1988; pp. 1–251. [Google Scholar]
- Farhoodi, A.; Coventry, D.R. Field crop responses to lime in mid–north region of South Australia. Field Crops Res. 2008, 108, 45–53. [Google Scholar] [CrossRef]
- Miller, L. Summary of Yield Responses from Liming. 2017. Available online: http://www.sfs.org.au/Liming%20and%20soil%20acidity%20article/2017%20Results%20Book%20Summary%20Yield%20Responses%20from%20liming.pdf (accessed on 30 October 2019).
- ADMS: Agricultural Drought Monitoring System in Poland. Available online: http://www.susza.iung.pulawy.pl (accessed on 15 February 2019).
- Rane, J.; Pannu, R.K.; Sohu, V.S.; Saini, R.S.; Mishra, B.; Shoran, J.; Joshi, A.K. Performance of yield and stability of advanced wheat genotypes under heat stress environments of the Indo–Gangetic plains. Crop Sci. 2007, 47, 1561–1573. [Google Scholar] [CrossRef]
- Senapati, N.; Stratonovitch, P.; Paul, M.J.; Semenov, M.A. Drought Tolerance during Reproductive Development is Important for Increasing wheat yield Potential under Climate change in Europe. J. Exp. Bot. 2018, 70, 2549–2560. [Google Scholar] [CrossRef] [Green Version]
Location | Longitude/Latitude/Altitude | Province * | The Points Attributed to Arable Land Suitability Group (Designation) in 2018 | Average Arable Land Suitability in Points [26] | Soil pH | Drought | Mean Yield (t/ha) |
---|---|---|---|---|---|---|---|
Cicibór Duży | 23.117/52.083/114 | Lubelskie | 70 (4. Very good for rye) | 36.5 | 5.9 | 4 | 5.70 |
Czesławice | 22.267/51.317/206 | Lubelskie | 94 (1. Very good for wheat) | 82.9 | 6.2 | No drought | 9.88 |
Głębokie | 18.438/52.645/85 | Kujawsko-Pomorskie | 80 (2. Good for wheat) | 70.0 | 6.3 | 2 | 5.69 |
Głubczyce | 17.833/50.183/280 | Opolskie | 94 (1. Very good for wheat) | 82.4 | 5.7 | No drought | 10.19 |
Krościna Mała | 16.950/51.367/106 | Dolnośląskie | 70 (4. Very good for rye) | 48.9 | 6.9 | 3 | 9.56 |
Marianowo | 22.117/53.217/140 | Podlaskie | 70 (4. Very good for rye) | 76.7 | 5.7 | 2 | 7.45 |
Masłowice | 18.633/51.250/174 | Łódzkie | 70 (4. Very good for rye) | 50.9 | 6.5 | 2 | 7.32 |
Nowa Wieś Ujska | 16.750/53.033/105 | Wielkopolskie | 70 (4. Very good for rye) | 43.5 | 5.4 | 5 | 6.14 |
Pawłowice | 18.483/50.467/240 | Śląskie | 80 (2. Good for wheat) | 53.1 | 6.7 | No drought | 4.94 |
Radostowo | 18.750/53.983/40 | Pomorskie | 94 (1. Very good for wheat) | 76.7 | 7.0 | No drought | 10.02 |
Rarwino | 14.833/53.933/10 | Zachodnio-Pomorskie | 70 (4. Very good for rye) | 57.3 | 5.9 | 2 | 6.66 |
Rychliki | 19.533/53.983/80 | Warmińsko–Mazurskie | 80 (2. Good for wheat) | 72.3 | 6.4 | 2 | 9.14 |
Seroczyn | 21.933/52.000/150 | Mazowieckie | 70 (4. Very good for rye) | 39.6 | 6.8 | 4 | 5.58 |
Skołoszów | 22.733/49.883/230 | Podkarpackie | (2. Good for wheat) | 70.6 | 5.8 | No drought | 8.66 |
Słupia | 19.967/50.633/290 | Świętokrzyskie | 80 (2. Good for wheat) | 57.9 | 5.5 | No drought | 10.45 |
Świebodzin | 15.583/52.233/90 | Lubuskie | 70 (4. Very good for rye) | 48.6 | 4.9 | 4 | 6.85 |
Tomaszów Bolesławie-cki | 15.683/51.283/200 | Dolnośląskie | 52 (4. Good for rye) | 70.3 | 6.0 | No drought | 3.27 |
Węgrzce | 19.983/50.117/285 | Małopolskie | 94 (1. Very good for wheat) | 79.8 | 6.1 | No drought | 8.87 |
Zybiszów | 16.917/51.067/130 | Dolnośląskie | 80 (2. Good for wheat) | 76.5 | 6.4 | no Drought | 9.93 |
Variable Name | Unit | Description and Interpretation | Number Per Location | Source | |
---|---|---|---|---|---|
Air temperature (T) | °C | Mean air temperature in 10-day period from the second period in April to the second period in July | 10 | COBORU | |
Precipitation (P) | mm | Sum of rainfall in 10-day period from the second period in April to the second period in July | 10 | ||
Selyaninov Hydrothermal coefficient (HTC) | 10 mm/°C | HTC = 10 × ƩP/ƩT | 10 | Skowera and Puła [32], simplified (calculation based on COBORU data) | |
Climatic water balance (CWB) | mm | The difference between the precipitations and the potential evapotranspiration for a total period of sixty days, reported every ten days | 5 | ADMS for the district in which the experiment is located | |
Drought length (DL) | 10-day period | The number of ADMS reports indicating the threat of drought between April 10 and July 10 as according to the ADMS web site adjusted to agronomic category | 1 | ||
Arable land suitability group (LS) | points | Arable land suitability for each trial location. The full scale ranges from 18 to 94 points, with higher values for better, more wheat suitable soils [33] | 1 | COBORU | |
Soil pH | unit less | Measured in 1M KCl extract | 1 |
Cultivar Name | ai | abij | m * | bj | AMMI Stability Measure (ASV) | Finlay–Wilkinson Reression Coeficient (FW) | ||
---|---|---|---|---|---|---|---|---|
MIM | HIM | MIM | HIM | |||||
Arkadia | −0.214 | −0.129 | 0.129 | 7.70 | −0.504 | 0.504 | 4.015 | 0.875 |
Artist | 0.337 | −0.063 | 0.063 | 0.978 | 0.978 | |||
Belissa | 0.085 | −0.038 | 0.038 | 2.857 | 1.054 | |||
Bonanza | 0.325 | −0.108 | 0.108 | 1.499 | 0.987 | |||
Delawar | −0.067 | 0.121 | −0.121 | 1.351 | 0.919 | |||
Dolores | 0.057 | 0.071 | −0.071 | 1.459 | 0.887 | |||
Formacja | −0.019 | 0.111 | −0.111 | 0.433 | 0.972 | |||
Frisky | 0.215 | 0.075 | −0.075 | 1.870 | 0.884 | |||
Hondia | −0.087 | 0.007 | −0.007 | 0.799 | 1.004 | |||
Hybery | 0.123 | 0.146 | −0.146 | 3.391 | 0.867 | |||
KWS Firebird | −0.065 | −0.138 | 0.138 | 2.323 | 0.956 | |||
KWS Kiran | 0.135 | 0.037 | −0.037 | 2.332 | 0.905 | |||
KWS Ozon | −0.265 | −0.186 | 0.186 | 0.883 | 0.950 | |||
KWS Spencer | −0.106 | 0.028 | −0.028 | 1.508 | 0.970 | |||
LG Jutta | 0.161 | −0.080 | 0.080 | 2.810 | 1.010 | |||
Linus | −0.106 | −0.090 | 0.090 | 0.927 | 0.912 | |||
Medalistka | −0.172 | 0.006 | −0.006 | 3.294 | 0.951 | |||
Opcja | 0.064 | −0.016 | 0.016 | 1.371 | 0.927 | |||
Ostroga | −0.472 | 0.028 | −0.028 | 1.458 | 0.961 | |||
Owacja | −0.253 | 0.154 | −0.154 | 1.992 | 0.951 | |||
Patras | 0.105 | 0.014 | −0.014 | 1.462 | 1.012 | |||
Pokusa | −0.211 | 0.003 | −0.003 | 1.446 | 0.915 | |||
RGT Bilanz | 0.360 | −0.089 | 0.089 | 2.142 | 0.850 | |||
RGT Kicker | −0.067 | 0.003 | −0.003 | 2.240 | 0.910 | |||
RGT Kilimanjaro | 0.031 | 0.052 | −0.052 | 1.558 | 0.876 | |||
RGT Metronom | 0.049 | −0.074 | 0.074 | 1.351 | 0.970 | |||
Rivero | 0.113 | 0.022 | −0.022 | 0.639 | 0.968 | |||
Rotax | 0.140 | 0.093 | −0.093 | 1.439 | 0.999 | |||
Tytanika | −0.197 | 0.039 | −0.039 | 3.552 | 0.926 |
Variable | AIC a | Coefficients b | Sum of Squares b |
---|---|---|---|
HTC_June_1_dec | 3732 | 1.69 | 1008 |
HTC_May_3_dec | 3572 | 2.82 | 781 |
DL | 3314 | −1.04 | 478 |
LS | 3278 | 0.093 | 441 |
HTC_July_2_dec | 3234 | −0.754 | 398 |
HTC_May_1_dec | 3171 | −2.58 | 339 |
HTC_June_3_dec | 3082 | −0.699 | 261 |
HTC_July_1_dec | 3048 | −2.68 | 232 |
HTC_April_2_dec | 2850 | −0.878 | 84 |
Soil pH | 2848 | −0.939 | 82 |
HTC_May_2_dec | 2818 | 0.314 | 63 |
HTC_June_2_dec | 2763 | −0.415 | 27 |
HTC_April_3_dec | 2729 | 0.457 | 6 |
All | 2722 |
Ranking of Cultivar | GSR-Based Recommendation in Environments of Expected Yield | COBORU Recommendation (No of Provinces) | ||
---|---|---|---|---|
Low (4 t/ha) | Medium (7 t/ha) | High (10 t/ha) | ||
Artist | 4 * | 1 * | 6 | 16 |
Rotax | 5 * | 5 * | 15 | 8 |
Belissa | 1 * | 6 | 20 | 7 |
Bonanza | 2 * | 2 * | 8 | 5 |
LG Jutta | 3 * | 4 * | 14 | 5 |
Hybery | 15 | 11 | 4 * | 4 |
KWS Kiran | 13 | 10 | 5 * | 3 |
RGT Bilanz | 14 | 3 * | 1 * | 3 |
Frisky | 17 | 7 | 2 * | 2 |
Dolores | 22 | 12 | 3 * | |
RGT Kilimanjaro | 23 | 14 | 7 | 14 |
Linus | 19 | 19 | 13 | 12 |
Hondia | 8 | 16 | 19 | 11 |
Arkadia | 16 | 22 | 22 | 7 |
Patras | 6 | 8 | 11 | 7 |
Ostroga | 25 | 25 | 25 | 6 |
Delawar | 21 | 18 | 10 | 5 |
KWS Ozon | 20 | 24 | 24 | 5 |
Formacja | 11 | 15 | 12 | 2 |
Pokusa | 24 | 23 | 17 | 2 |
Rivero | 7 | 9 | 9 | 2 |
KWS Firebird | 10 | 14 | 16 | 1 |
KWS Spencer | 12 | 17 | 18 | 1 |
Medalistka | 9 | 20 | 23 | 1 |
Tytanika | 18 | 21 | 21 | 1 |
Proposed | AMMI ASV | Finlay–Wilkinson | |
---|---|---|---|
Proposed | - | 0.40 * | 0.25 ns |
AMMI ASV | 0.40 * | - | 0.42 * |
Finlay–Wilkinson | 0.25 ns | 0.42 * | - |
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Iwańska, M.; Paderewski, J.; Stępień, M.; Rodrigues, P.C. Adaptation of Winter Wheat Cultivars to Different Environments: A Case Study in Poland. Agronomy 2020, 10, 632. https://doi.org/10.3390/agronomy10050632
Iwańska M, Paderewski J, Stępień M, Rodrigues PC. Adaptation of Winter Wheat Cultivars to Different Environments: A Case Study in Poland. Agronomy. 2020; 10(5):632. https://doi.org/10.3390/agronomy10050632
Chicago/Turabian StyleIwańska, Marzena, Jakub Paderewski, Michał Stępień, and Paulo Canas Rodrigues. 2020. "Adaptation of Winter Wheat Cultivars to Different Environments: A Case Study in Poland" Agronomy 10, no. 5: 632. https://doi.org/10.3390/agronomy10050632
APA StyleIwańska, M., Paderewski, J., Stępień, M., & Rodrigues, P. C. (2020). Adaptation of Winter Wheat Cultivars to Different Environments: A Case Study in Poland. Agronomy, 10(5), 632. https://doi.org/10.3390/agronomy10050632