The Impact of Crop Year and Crop Density on the Production of Sunflower in Site-Specific Precision Farming in Hungary
Abstract
:1. Introduction
2. Materials and Methods
2.1. The Open Field Sowing Experiment
- -
- Low-productivity zone: the average of pixel vales was between 87% and 94%;
- -
- Average-productivity zone: the average of pixel values was between 97.6% and 102.5%;
- -
- High-productivity zone: the average of pixel values was between 105.1% and 113%.
- In the low-productivity zone, we applied the standard nominal number of seeds ha−1 (55,000), and the number of seeds was reduced by 20% (44,000) and 40% (33,000).
- In the average-productivity zone, the standard nominal number of seeds (55,000) was used, and the number of seeds increased by 20% (66,000) and reduced by 20% (44,000).
- In the high-productivity zone, the standard nominal number of seeds (55,000) was used, and the number of seeds increased by 20% (66,000) and 40% (77,000).
2.2. Weather Monitoring
2.3. Yield Monitoring
2.4. Statistical Analysis
3. Results
4. Discussion
4.1. Nominal and Germinated Seeds
4.2. Quantity and Quality of the Yield
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Zhu, Z.; Lu, L.; Zhang, W.; Liu, W. AR6 Climate Change 2021: The Physical Science Basis; IPCC: Geneva, Switzerland, 2021. [Google Scholar]
- Pachauri, R.K.; Allen, M.R.; Barros, V.R.; Broome, J.; Cramer, W.; Christ, R.; Church, J.A.; Clarke, L.; Dahe, Q.; Dasgupta, P.; et al. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II, III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; IPCC: Geneva, Switzerland, 2014. [Google Scholar]
- Boris, O.; Seneviartne, S.I. Global changes in extreme events: Regional and seasonal dimension. Clim. Chang. 2012, 110, 669–696. [Google Scholar]
- Dai, A. Drought under global warming: A review. Wiley Interdiscip. Rev. Clim. Chang. 2011, 1, 45–65. [Google Scholar] [CrossRef]
- Dai, A. Increasing drought under global warming in observation and models. Nat. Clim. Chang. 2012, 3, 52–58. [Google Scholar] [CrossRef]
- Sheffield, J.; Wood, E.; Roderick, M. Little change in global drought over the past 60 years. Nature 2012, 491, 435–438. [Google Scholar] [CrossRef] [PubMed]
- Jonathan, S.; Naumann, G.; Carrao, H.; Barbosa, P.; Vogt, J. World drought frequency, duration and severity for 1951–2010. Int. J. Climatol. 2014, 34, 2792–2804. [Google Scholar]
- Trenberth, K.E.; Dai, A.; Van Der Schrier, G.; Jones, P.; Barichivich, J.; Briffa, K.; Sheffield, J. Global warming and changes in drought. Nat. Clim. Chang. 2013, 4, 17–22. [Google Scholar] [CrossRef]
- Siegfried, D.S.; Stewart, R.E.; Wang, H.; Barlow, M.; Berbery, E.H.; Cai, W.; Hoerling, M.P.; Kanikicharla, K.K.; Koster, R.D.; Lyon, B.; et al. Global meteorological drought: A synthesis of current understanding with focus on SST drivers of precipitation deficits. J. Clim. 2016, 29, 3989–4019. [Google Scholar]
- Vicente-Serrano, S.M.; Lopez-Moreno, I.; Begueria, S.; Lorenzo-Lacruz, J.; Sanchez-Lorenzo, A.; García-Ruiz, J.M.; Azorin-Molina, C.; Morán-Tejeda, E.; Revuelto, J.; Trigo, R.; et al. Evidence of increasing drought severity caused by temperature rise in southern Europe. Environ. Res. Lett. 2014, 9, 044001. [Google Scholar] [CrossRef]
- Stahl, K.; Kohn, I.; Blauhut, V.; Urquijo, J.; De Stefano, L.; Acácio, V.; Dias, S.; Stagge, J.H.; Tallaksen, L.M.; Kampragou, E.; et al. Impacts of European drought events: Insights from an international database of text-based reports. Nat. Hazards Earth Syst. Sci. 2016, 16, 801–819. [Google Scholar] [CrossRef]
- Tigchelaar, M.; Battisti, D.S.; Naylor, R.L.; Ray, D. Future warming increases probability of globally synchronized maize production shocks. Proc. Natl. Acad. Sci. USA 2018, 115, 6644–6649. [Google Scholar] [CrossRef]
- Auerbach, R.; Piek, H.; Battersby, J.; Devereux, S.; Olivier, N. The Likely Impact of the 2015–2018 Drought in South Africa: Lessons from the 2008 Food Price Crisis and Future Implications; Cabi: Wallingford, UK, 2020; pp. 81–99. [Google Scholar]
- Bartholy, J.; Pongracz, R.; Hollósi, B. Analysis of projected drought hazards for Hungary. Adv. Geosci. 2013, 35, 61–66. [Google Scholar] [CrossRef]
- Mezősi, G.; Bata, T.; Meyer, B.C.; Blanka, V.; Ladányi, Z. Climate Change Impacts on Environmental Hazards on the Great Hungarian Plain, Carpathian Basin. Int. J. Disaster Risk Sci. 2014, 5, 136–146. [Google Scholar] [CrossRef]
- Ceglar, A.; Croitoru, A.E.; Cuxart, J.; Djurdjevic, V.; Güttler, I.; Ivancan-Picek, B.; Jug, D.; Lakatos, M.; Weidinger, T. PannEx: The Pannonian basin experiment. Clim. Serv. 2018, 11, 78–85. [Google Scholar] [CrossRef]
- Gálos, B.; Lorenz, P.; Jacob, D. Will dry events occur more often in Hungary in the future? Environ. Res. Lett. 2007, 2, 034006. [Google Scholar] [CrossRef]
- Mohammed, S.; Alsafadi, K.; Daher, H.; Gombos, B.; Mahmood, S.; Harsányi, E. Precipitation pattern changes and response of vegetation to drought variability in the eastern Hungary. Bull. Natl. Res. Cent. 2020, 44, 55. [Google Scholar] [CrossRef]
- Széles, A.; Kovács, K.; Ferencsik, S. The effect of crop years and nitrogen basal and top dressing on the yield of different maize genotypes and marginal revenue. Quart. J. Hung. Meteorol. Serv. 2019, 123, 65–278. [Google Scholar] [CrossRef]
- Nagy, J. The effect of tillage and fertilization on the yield of maize in drought and favourable years. In Soil, Plant and Environment Interactions III; Nagy, J., Pepó, P., Eds.; Debreceni Egyetem Agrártudományi Centrum: Debrecen, Hungary, 2000; pp. 97–119. [Google Scholar]
- Antal, J. The optimal climate conditions of sunflower. In Arable Crop Production; Bocz, E., Ed.; Mezőgazda Kiadó: Budapest, Hungary, 1992. [Google Scholar]
- Nagy, J. The interaction of plant density and tillage in maize production. Növénytermelés 1996, 45, 543–553. [Google Scholar]
- Szabó, A. The Effect of Plant Density on the Yield, Yield Safety and Quality of the Sunflower Hybrids. PhD Thesis, University of Debrecen, Debrecen, Hungary, 2007. [Google Scholar]
- Moore, I.D.; Gessler, P.E.; Nielsen, G.A.; Peterson, G.A. Soil attribute prediction using terrain analysis. Soil Sci. Soc. Am. J. 1993, 57, 443–452. [Google Scholar] [CrossRef]
- Monoki, S.; Csornai, G.; Zalai, M.; Nagy, J. Economics of site-specific crop density in precision sunflower (Helianthus annus L.) production. Acta Agrar. Debreceniensis 2023, 1, 91–96. [Google Scholar] [CrossRef]
- Milics, G. Application of GIS and Remote Sensing in Precision (Site-Spcific) Crop Production. PhD Thesis, University of Pécs, Pécs, Hungary, 2008. [Google Scholar]
- Tótin, A.; Pepó, P. The effect of drilling time and crop density on the emergence and growth dynamics of three maize hybrids. Agric. Sci. Bull. 2016, 68, 105–110. [Google Scholar]
- Kandil, A.A.; Ibrahim, A.F.; Marquard, R.; Taha, R.S. Response of some quality traits of sunflower seeds and oil to different environments. J. Agron. Crop Sci. 1990, 164, 224–230. [Google Scholar] [CrossRef]
- Kotevcka, G.; Egumenovski, P. Effect of ecological factors on yields and concentration of sunflowers in Pelagonija, Bitola. God. Zb. Na Zemjod. Fak. Univ. St. Kiril Is Metod. Skopje 1994, 39, 107–120. [Google Scholar]
- Chaurasia, R.; Dhaliwal, S.S.; Sharma, P.K.; Mahi, G.S. Growth and yield of sunflower in relation to canopy temperature. Punjab Agric. Univ. J. Res. 1997, 32, 397–403. [Google Scholar]
- Frank, J. The Biology and Cultivation of Sunflower; Mezőgazda Kiadó: Budapest, Hungary, 1999; pp. 159–188. [Google Scholar]
- Geleta, S.; Baltensperger, D.D.; Binford, G.G.; Miller, J.F. Sunflower response to nitrogen and phosphorus in wheat-fallow cropping systems. J. Prod. Agricul. 1997, 10, 466–472. [Google Scholar] [CrossRef]
- Németh, F.; Horváth, Z.; Csizmadia, J.; Döme, O.; Francsicskovics, L.; Lasancz, I. Key elements of sunflower production. In What You Need to Know about Sunflower and Canola; Sárközi, F., Ed.; Agroinform: Budapest, Hungary, 1996; pp. 9–76. (In Hungarian) [Google Scholar]
- Pepó, P.; Borbélyné Hunyadi, É.; Zsombik, L.; Szabó, A. Investigation of the biological basis of sunflower cultivation in Hajdúság. Agrofórum 2003, 14, 7–15. [Google Scholar]
- Kill, F. Influence of different nitrogen levels on productivity of oilseed and confection sunflowers (Helianthus annuus L.) under varying plant population. Int. J. Agric. Biol. 2004, 6, 594–598. [Google Scholar]
- Sanches, L.P.; Verano, H.B.; Liriano, G.R.; Lima, D.S.; Fabelo, P.M.; Loez, C.C. Influence of five sowing densities on some phenological parameters of a crop of sunflower. Cent. Agric. 2000, 27, 58–63. [Google Scholar]
- Allam, A.Y.; Galal, A.H. Effect of nitrogen fertilization and plant density on yield and quality of sunflower. Assuit J. Garicultural Sci. 1996, 27, 169–177. [Google Scholar]
- ISO 664; Oilseeds—Reduction of Laboratory Sample to Test Sample. European Committee for Standardization: Brussels, Belgium, 2008. Available online: https://www.iso.org/obp/ui/en/#iso:std:iso:664:ed-3:v1:en (accessed on 1 March 2024).
- ISO 665; Oilseeds—Determination of Moisture and Volatile Matter Content. European Committee for Standardization: Brussels, Belgium, 2020. Available online: https://www.iso.org/obp/ui/en/#iso:std:iso:665:ed-3:v1:en (accessed on 1 March 2024).
- ISO 659; Oilseeds—Determination of Oil Content (Reference Method). European Committee for Standardization: Brussels, Belgium, 2009. Available online: https://www.iso.org/obp/ui/en/#iso:std:iso:659:ed-4:v1:en (accessed on 1 March 2024).
- Jonge, L.W.; Jajobsen, O.H.; Moldrup, D. Soil water repellency; effects of water content, temperature, and particle size. Soil Sci. Soc. Am. J. 1999, 63, 437–441. [Google Scholar] [CrossRef]
- Long, M.; Fril, B.; Diepenbrock, W. Effects of plant density, row spacing and row orientation on yield and achene quality in rainfed sunflower. Acta Agron. Hung. 2001, 49, 397–407. [Google Scholar] [CrossRef]
- Novák, A.; Máriás, K. Effects of sowing time on yield and oil content of different sunflower genotypes in years with different water supply. Int. Sch. Sci. Res. Innov. 2013, 7, 1007–1010. [Google Scholar]
- Ion, V.; Dicu, G.; Basa, A.G.; Dumbrava, M.; Temocicot, G.; Epure, L.I.; State, D. Sunflower yield and yield components under different sowing conditions. Agric. Agric. Sci. Procedia 2015, 6, 44–51. [Google Scholar] [CrossRef]
- Mijic, A.; Liovicl, I.; Sudaric, A.; Gadzo, D.; Duvnjak, T.; Simic, B.; Jug, D.; Markulj Kulundzic, A. Influence of Plant Density and Hybrid on Grain Yield, Oil Content and Oil Yield of Sunflower. Agric. Conspec. Sci. 2021, 86, 27–33. [Google Scholar]
- Kushwaha, C.P.; Tripathi, S.K.; Singh, K.D. Soil organic matter and water-stable aggregates under different tillage and residue conditions in a tropical dryland agroecosystem. Appl. Soil Ecol. 2001, 16, 229–241. [Google Scholar] [CrossRef]
- Önemli, F. The effects of soil organic matter on seedling emergence in sunflower (Helianthus annuus L.). Plant Soil Environ. 2004, 50, 494–499. [Google Scholar] [CrossRef]
- Jocić, S. Inheritance of Yield Components in Sunflower (Helianthus annuus L.). Ph.D. Thesis, University of Novi Sad, Novi Sad, Serbia, 2003. [Google Scholar]
- Gunasekera, C.P.; Martin, L.D.; Siddique, K.H.M.; Walton, G.H. Genotype by environment interactions of Indian mustard (Brassica juncea L.) and canola (B. napus L.) in Mediterranean-type environments. II. Oil Protein Conc. Seed. Eur. J. Agron. 2006, 25, 13–21. [Google Scholar] [CrossRef]
- Mrdja, J.; Crnobarac, J.; Radić, V.; Miklič, V. Sunflower seed quality and yield in relation to environmental conditions of production region. Helia 2012, 35, 123–134. [Google Scholar] [CrossRef]
- Villegas, D.; Casadesus, J.; Atienza, S.G.; Martos, V.; Maalouf, F.; Karam, F.; Aranjuelo, I.; Nogues, S. Tritordeum, wheat and triticale yield components under multi-local mediterranean drought conditions. Field Crops Res. 2010, 116, 68–74. [Google Scholar] [CrossRef]
- Marjanović-Jeromela, A.; Nagl, N.; Gvozdanović-Varga, J.; Hristov, N.; Kondić-Špika, A.; Vasić, M.; Marinković, R. Genotype by environment interaction for seed yield per plant in rapeseed using AMMI model. Pesqui. Agropecu. Bras. 2011, 46, 174–181. [Google Scholar] [CrossRef]
- Marin, V.; Ion, V. Sunflower grain yield at different conditions of row spacing and plant density. Sci. Pap. Ser. A Agron. 2022, LXV, 395–402. [Google Scholar]
Zone ID. | C14 low | C14 Average | C14 High | C10 low | C10 Average | C10 High | |
Soil Type | High-Plasticity Meadow | ||||||
Parameter | Unit | Measured Values | |||||
pH (KCl) | 6.06 | 5.87 | 6.1 | 5.41 | 6.7 | 6.62 | |
Arany’s plasticity index | 42 | >60 | >60 | 59 | >60 | >60 | |
Total salt | m/m% | 0.09 | 0.13 | 0.12 | 0.14 | 0.16 | 0.14 |
CaCO3 | m/m% | 0 | 0 | 0 | 0 | 0.9 | 0.1 |
Organic matter | m/m% | 3.01 | 2.66 | 2.68 | 3.32 | 4.31 | 3.18 |
(NO2¯+NO3¯)-N (KCl) | mg kg−1 | 25.3 | 12.7 | 16.3 | 27.7 | 39.8 | 42.4 |
P2O2 (AL) | mg kg−1 | 1060 | 258 | 209 | 150 | 461 | 73.3 |
K2O (AL) | mg kg−1 | 1230 | 484 | 445 | 849 | 1060 | 622 |
Operation | Tool | Input | Doze |
---|---|---|---|
discing | John Deere 9620RX (Deere and Company, Moline, IL, USA), Vaderstad Carrier 1225 (Vaderstad AB, Vaderstad, Sweden) | ||
subsoiling | John Deere 9620RX (Deere and Company, Moline, IL, USA) Maschio Artiglio (Maschio Gaspardo, Campodarsego, Italy) | ||
spraying | Agrifac Condor 4000 (Agrifac Maschinery B.V., Steenwijk, Netherlands) | glyphosate 480 gr/L | 3l haֿ1 |
seedbed preparation | John Deere 9620RX (Deere and Company, Moline, IL, USA) Vaderstad NZ Agressive 10000 (Vaderstad AB, Vaderstad, Sweden) | ||
rolling | John Deere 6195R (Deere and Company, Moline, IL, USA) Vaderstad Rexius 1230 (Vaderstad AB, Vaderstad, Sweden) | ||
spraying | Agrifac Condor 4000 (Agrifac Maschinery B.V., Steenwijk, Netherlands) | glyphosate 480 gr/L | 2l ha−1 |
drilling | John Deere 8335R (Deere and Company, Moline, IL, USA) Horsch Maestro 12.75 SW (Horsch Maschinen Gmbh, Schwandorf, Germany) applied with Precision Planting Sowing Unit (Precision Planting, Tremont, IL, USA) | SY Bacardi CLP | according to sowing plan |
cypermethrin 8g/kg | 10 kg ha−1 | ||
urea N47% | 30 kg ha−1 | ||
spraying | Agrifac Condor 4000 (Agrifac Maschinery B.V., Steenwijk, Netherlands) | dimethenamid-P 212.5 g/L, pendimethalin 250 g/L | 4l ha−1 |
fertilising | John Deere 6195R (Deere and Company, Moline, IL, USA) Amazone ZA TS (AMAZONEN-Werke H. Dreyer GmbH & Co. KG, Hasbergen, Germany) | monoammonium phosphate N12-P52% | 50 kg ha−1 |
spraying | Agrifac Condor 4000 (Agrifac Maschinery B.V., Steenwijk, Netherlands) | imazamox 25 g/L | 2l ha−1 |
fertilising | John Deere 6195R (Deere and Company, Moline, IL, USA) Amazone ZA TS (AMAZONEN-Werke H. Dreyer GmbH & Co. KG, Hasbergen, Germany) | ammonium sulphate N21-S24% | 50 kg ha−1 |
fertilising | John Deere 6195R (Deere and Company, Moline, IL, USA) Amazone ZA TS (AMAZONEN-Werke H. Dreyer GmbH & Co. KG, Hasbergen, Germany) | urea N47% | 200 kg ha−1 |
inter-row cultivation | John Deere 8335R (Deere and Company, Moline, IL, USA) Orthman 8315 (Unverferth Manufacturing Co., Kalida, OH, USA) | ||
spraying | Agrifac Condor 4000 (Agrifac Maschinery B.V., Steenwijk, The Netherlands) | deltamethrin 50 g/L | 0.15l ha−1 |
fluopiram 125 g/L, protiokonazol 125 g/L | 0.8l ha−1 | ||
foliar B 150 gr/L, Mo 7.5 gr/L | 1.5l ha−1 | ||
foliar Zn 700 gr/L | 1l ha−1 | ||
foliar MgSO4 MgO 160 g/kg, SO3 325 g/kg | 5 kg ha−1 | ||
spraying | Agrifac Condor 4000 (Agrifac Maschinery B.V., Steenwijk, The Netherlands) | boskalid 200 g/L, dimixistrobin 200 g/L | 0.5l ha−1 |
foliar B 150 g/L, Mo 7.5 g/L | 0.7l ha−1 | ||
foliar Zn 700 g/L | 0.5l ha−1 | ||
foliar MgSO4 MgO 160 g/kg, SO3 325 g/kg | 5 kg ha−1 | ||
harvest | John Deere S690 (Deere and Company, Moline, IL, USA) |
Month | Monthly Total Rain (mm) | Rain Diff. (mm) | Monthly Avg. Temp. (°C) | Temp. Diff. (°C) | No. of Days over 5 mm of Rain | |||
---|---|---|---|---|---|---|---|---|
2021 | 2022 | 2021 | 2022 | 2021 | 2022 | |||
January | 26.8 | 4.5 | −22.3 | 0.05 | −0.32 | −0.37 | 0 | 0 |
February | 47.8 | 8.6 | −39.2 | 1.57 | 4.11 | 2.54 | 3 | 0 |
March | 10.3 | 32.0 | 21.7 | 5.3 | 5.4 | 0.10 | 1 | 1 |
April | 59.3 | 40.1 | −19.2 | 8.47 | 9.56 | 1.09 | 5 | 3 |
May | 73.7 | 11.4 | −62.3 | 14.21 | 17.58 | 3.37 | 4 | 0 |
June | 41.4 | 26.9 | −14.5 | 22.66 | 22.98 | 0.32 | 1 | 2 |
July | 49.5 | 20.1 | −29.4 | 24.32 | 24.06 | −0.26 | 4 | 1 |
August | 65.7 | 81.6 | 15.9 | 20.71 | 24.48 | 3.77 | 6 | 3 |
September | 11.7 | 51.3 | 39.6 | 17.37 | 15.76 | −1.61 | 1 | 3 |
October | 17.5 | 6.4 | −11.1 | 10.05 | 12.31 | 2.26 | 1 | 0 |
November | 62.0 | 38.2 | −23.8 | 4.95 | 6.17 | 1.22 | 4 | 2 |
December | 45.5 | 81.5 | 36.0 | 0.82 | 1.97 | 1.15 | 1 | 4 |
Ʃ | 511.2 | 402.6 | −108.6 | 10.87 | 12.01 | 1.13 | 31 | 19 |
Tested Variable | Test Results | ||||||
---|---|---|---|---|---|---|---|
Seed moisture | MANOVA | t-test/Tukey Comparison | |||||
Variable | d.f. | F | p-Value | Group | Avg. Value (%) | Sign.* | |
Year | 1 | 12,521 | <0.001 | 2021 | 6.05 | b | |
2022 | 5.83 | a | |||||
Zone | 2 | 5928 | <0.001 | Low | 6.16 | c | |
Average | 5.89 | b | |||||
High | 5.82 | a | |||||
Nominal crop density | 2 | 236 | <0.001 | Low | 5.92 | a | |
Average | 5.97 | b | |||||
High | 5.92 | a | |||||
Seed oil content | Variable | d.f. | F | p-Value | Group | Avg. Value (%) | Sign.* |
Year | 1 | 4500 | <0.001 | 2021 | 47.61 | b | |
2022 | 46.18 | a | |||||
Zone | 2 | 405 | <0.001 | Low | 47.30 | c | |
Average | 46.84 | b | |||||
High | 46.61 | a | |||||
Nominal crop density | 2 | 556 | <0.001 | Low | 47.35 | c | |
Average | 46.66 | a | |||||
High | 46.75 | b | |||||
Thousand-seed weight | Variable | d.f. | F | p-Value | Group | Avg. Value (g) | Sign.* |
Year | 1 | 1,859,382 | <0.001 | 2021 | 55.82 | b | |
2022 | 20.18 | a | |||||
Zone | 2 | 64,264 | <0.001 | Low | 30.53 | a | |
Average | 39.16 | b | |||||
High | 41.46 | c | |||||
Nominal crop density | 2 | 16,982 | <0.001 | Low | 41.15 | c | |
Average | 37.85 | b | |||||
High | 35.44 | a | |||||
Yield | Variable | d.f. | F | p-Value | Group | Avg. Value (kg ha−1) | Sign.* |
Year | 1 | 309,665 | <0.001 | 2021 | 4557 | b | |
2022 | 2681 | a | |||||
Zone | 2 | 106,832 | <0.001 | Low | 2637 | a | |
Average | 3465 | b | |||||
High | 4376 | c | |||||
Nominal crop density | 2 | 911 | <0.001 | Low | 3519 | a | |
Average | 3599 | b | |||||
High | 3675 | c |
Tested Variable | Test Results | ||||||
---|---|---|---|---|---|---|---|
Deviation of crop density | MANOVA | t-Test/Tukey Comparison | |||||
Variable | d.f. | F | p-Value | Group | Avg. Value (Plant per Hectare) | Sign.* | |
Year | 1 | 5.30 | 0.021 | 2021 | 12,747 | a | |
2022 | 13,407 | b | |||||
Zone | 2 | 257.16 | <0.001 | Low | 8656 | a | |
Average | 12,429 | b | |||||
High | 16,714 | c | |||||
Nominal crop density | 2 | 143.20 | <0.001 | Low | 9852 | a | |
Average | 12,615 | b | |||||
High | 15,761 | c | |||||
Double seeding | Variable | d.f. | F | p-Value | Group | Avg. Value (%) | Sign.* |
Year | 1 | 35.41 | <0.001 | 2021 | 1.35 | b | |
2022 | 0.39 | a | |||||
Zone | 2 | 16.34 | <0.001 | Low | 0.23 | a | |
Average | 0.88 | b | |||||
High | 1.28 | b | |||||
Nominal crop density | 2 | 6.81 | 0.001 | Low | 0.56 | a | |
Average | 0.73 | a | |||||
High | 1.19 | b | |||||
Missing seeding | Variable | d.f. | F | p-Value | Group | Avg. Value (%) | Sign.* |
Year | 1 | 3.46 | 0.063 | 2021 | 10.47 | - | |
2022 | 9.12 | - | |||||
Zone | 2 | 8.38 | <0.001 | Low | 9.06 | a | |
Average | 8.96 | a | |||||
High | 10.96 | b | |||||
Nominal crop density | 2 | 8.80 | <0.001 | Low | 8.26 | a | |
Average | 9.85 | b | |||||
High | 10.76 | b |
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Nagy, J.; Zalai, M.; Illés, Á.; Monoki, S. The Impact of Crop Year and Crop Density on the Production of Sunflower in Site-Specific Precision Farming in Hungary. Agriculture 2024, 14, 1515. https://doi.org/10.3390/agriculture14091515
Nagy J, Zalai M, Illés Á, Monoki S. The Impact of Crop Year and Crop Density on the Production of Sunflower in Site-Specific Precision Farming in Hungary. Agriculture. 2024; 14(9):1515. https://doi.org/10.3390/agriculture14091515
Chicago/Turabian StyleNagy, János, Mihály Zalai, Árpád Illés, and Szabolcs Monoki. 2024. "The Impact of Crop Year and Crop Density on the Production of Sunflower in Site-Specific Precision Farming in Hungary" Agriculture 14, no. 9: 1515. https://doi.org/10.3390/agriculture14091515