Quick In Situ Evaluation of Herbicide Efficacy in Maize (Zea mays L.) Crop
Abstract
:1. Introduction
2. Materials and Methods
2.1. Site Description
2.2. Experimental Setup and Design
2.3. Data Collection
2.4. Statistical Analysis
3. Results and Discussion
3.1. Herbicide Efficacy
3.2. Maize Grain Yield
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Oerke, E.C. Crop losses to pests. J. Agric. Sci. 2006, 144, 31–43. [Google Scholar] [CrossRef]
- Beckie, H.J. Herbicide-resistant weeds: Management tactics and practices. Weed Technol. 2006, 3, 793–814. [Google Scholar] [CrossRef]
- Silva, V.; Yang, X.; Fleskens, L.; Ritsema, C.J.; Geissen, V. Environmental and human health at risk—Scenarios to achieve the Farm to Fork 50% pesticide reduction goals. Environ. Int. 2022, 165, 107296. [Google Scholar] [CrossRef] [PubMed]
- Wang, X.; Christensen, S.; Svensgaard, J.; Jensen, S.M.; Liu, F. The effects of cultivar, nitrogen supply and soil type on radiation use efficiency and harvest index in spring wheat. Agronomy 2020, 10, 1391. [Google Scholar] [CrossRef]
- Vannoppen, A.; Gobin, A.; Kotova, L.; Top, S.; De Cruz, L.; Vīksna, A.; Aniskevich, S.; Bobylev, L.; Buntemeyer, L.; Caluwaerts, S.; et al. Wheat Yield Estimation from NDVI and Regional Climate Models in Latvia. Remote Sens. 2020, 12, 2206. [Google Scholar] [CrossRef]
- Milesi, C.; Samanta, A.; Hashimoto, H.; Kumar, K.K.; Ganguly, S.; Thenkabail, P.S.; Srivastava, A.N.; Nemani, R.R.; Myneni, R.B. Decadal Variations in NDVI and Food Production in India. Remote Sens. 2010, 2, 758–776. [Google Scholar] [CrossRef]
- Romano, E.; Bergonzoli, S.; Pecorella, I.; Bisaglia, C.; De Vita, P. Methodology for the definition of durum wheat yield homogeneous zones by using satellite spectral indices. Remote Sens. 2021, 13, 2036. [Google Scholar] [CrossRef]
- Travlos, I.; Tsekoura, A.; Antonopoulos, N.; Kanatas, P.; Gazoulis, I. Novel sensor-based method (quick test) for the in-season rapid evaluation of herbicide efficacy under real field conditions in durum wheat. Weed Sci. 2021, 69, 147–160. [Google Scholar] [CrossRef]
- Nehurai, O.; Atsmon, G.; Kizel, F.; Kamber, E.; Bar, N.; Eizenberg, H.; Lati, R.N. Early detection of the herbicidal effect of glyphosate and glufosiate by using hyperspectral imaging. Agron. J. 2023, 115, 2558–2569. [Google Scholar] [CrossRef]
- Xia, F.; Quan, L.; Lou, Z.; Sun, D.; Li, H.; Lv, X. Identification and comprehensive evaluation of resistant weeds using unmaned aerial vehicle-based multispectral imagery. Front. Plant Sci. 2022, 13, 938604. [Google Scholar] [CrossRef]
- Pause, M.; Raasch, F.; Marrs, C.; Csaplovics, E. Monitoring glyphosate-based herbicide treatment using Sentinel-2 time series-a proof-of-principle. Remote Sens. 2019, 11, 2541. [Google Scholar] [CrossRef]
- Gerhards, R.; Wyse-Pester, D.Y.; Johnson, G.A. Characterizing spatial stability of weed populations using interpolated maps. Weed Sci. 1997, 45, 108–119. [Google Scholar] [CrossRef]
- Tremblay, N.; Wang, Z.; Ma, B.; Belec, C. Comparison of crop data measured by two commercial sensors for variable-rate nitrogen application. Precis. Agric. 2009, 10, 145–161. [Google Scholar] [CrossRef]
- Kong, L.; Si, J.; Feng, B.; Li, S.; Wang, F.; Sayre, K. Differential responses of two types of winter wheat (Triticum aestivum L.) to autumn- and spring-applied mesosulfuron-methyl-methyl. Crop Prot. 2009, 28, 387–392. [Google Scholar] [CrossRef]
- Kanatas, P.; Travlos, I.; Gazoulis, J.; Antonopoulos, N.; Tsekoura, A.; Tataridas, A.; Zannopoulos, S. The combined effects of false seedbed technique, post-emergence chemical control and cultivar on weed management and yield of barley in Greece. Phytoparasitica 2020, 48, 131–143. [Google Scholar] [CrossRef]
- Berger, W.; Parker, F.L. Diversity of planktonic Forminifera in deep-sea sediments. Science 1970, 168, 1345–1347. [Google Scholar] [CrossRef] [PubMed]
- Ashenafi, M.; Gebre Selassie, Y.; Alemayehu, G.; Berhani, Z. Growth, yield components, and yield parameters of maize (Zea mays L.) as influenced by unified use of NPSZnB blended fertilizer and farmyard manure. Int. J. Agron. 2023, 2023, 1311521. [Google Scholar] [CrossRef]
- Shapiro, S.S.; Wilk, M.B. An analysis of variance test for normality (complete samples). Biometrika 1965, 52, 591–611. [Google Scholar] [CrossRef]
- Levene, H. Robust tests of equality of variances. In Contributions to Probability and Statistics, Essays in Honor of Harold Hoteling; Olkin, I., Ghurye, S.G., Hoeffding, W., Madow, W.G., Mann, H.B., Eds.; Stanford University Press: Stanford, CA, USA, 1960; pp. 278–292. [Google Scholar]
- Kanatas, P.; Gazoulis, I.; Antonopoulos, N.; Tataridas, A.; Travlos, I. The Potential of a Precision Agriculture (PA) Practice for In Situ Evaluation of Herbicide Efficacy and Selectivity in Durum Wheat (Triticum durum Desf.). Agronomy 2023, 13, 732. [Google Scholar] [CrossRef]
- Alvarenga, C.B.; Mundim, G.S.M.; Santos, E.A.; Gallis, R.B.A.; Zampiroli, R.; Rinaldi, P.C.N.; Prado, J.R. Normalized difference vegetation index for desiccation evaluation with glyphosate + 2,4-D in magnetized spray solution. Braz. J. Biol. 2023, 83, e246579. [Google Scholar] [CrossRef]
- Mink, R.; Linn, A.I.; Santel, H.-J.; Gerhards, R. Sensor-based evaluation of maize (Zea mays) and weed response to post-emergence herbicide applications of isoxaflutole and cyprosulfamide applied as crop seed treatment or herbicide mixing partner. Pest Manag. Sci. 2019, 76, 1856–1865. [Google Scholar] [CrossRef] [PubMed]
- Li, F.; Miao, Y.; Feng, G.; Yuan, F.; Yue, S.; Gao, X.; Liu, Y.; Liu, B.; Ustin, S.L.; Chen, X. Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices. Field Crops Res. 2014, 157, 111–123. [Google Scholar] [CrossRef]
- Gitelson, A.A.; Kaufman, Y.J.; Merzlyak, M.N. Use of green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 1996, 58, 289–298. [Google Scholar] [CrossRef]
Field | Geographic Position | Type of Soil | pH | Organic Matter % |
---|---|---|---|---|
Pyrgos1 | 37.667872° N, 21.477450° E | Clay | 7.15 | 3.1 |
Pyrgos2 | 37.661711° N, 21469247° E | Clay | 7.41 | 2.65 |
Pyrgos3 | 37.5543099° N, 21.5860696° E | Clay | 7.29 | 3.02 |
Pyrgos4 | 37.6512367° N, 21.4501098° E | Clay | 7.24 | 4.05 |
Field | 1st Experimental Year | 2nd Experimental Year |
---|---|---|
Pyrgos1 | 8 April | 5 April |
Pyrgos2 | 10 April | 6 April |
Pyrgos3 | 6 April | 9 April |
Pyrgos4 | 10 April | 8 April |
Treatment | Active Ingredient | Mechanism of Action | Rate (g·a.i.·ha−1) | Trade Name | Manufacturer |
---|---|---|---|---|---|
T1 1 | - | - | - | - | - |
T2 | Nicosulfuron + rimsulfuron + mesotrione | ALS + ALS + 4-HPPD inhibitors | 39.6 + 99 + 118.8 + 1080 | Arigo 51 WG + Codacide EC | Corteva Agriscience Hellas, Athens, Greece |
T3 | Nicosulfuron + rimsulfuron + dicamba | ALS + ALS inhibitors + natural auxins | 400.4 + 422.4 + 374 + 1080 | Hector max WG + Codacide EC | Corteva Agriscience Hellas, Athens, Greece |
T4 | Nicosulfuron + rimsulfuron | ALS + ALS inhibitors | 38.61 + 9.63 | Principal | Corteva Agriscience Hellas, Athens, Greece |
T5 | Florasulam + mesotrione | ALS + 4-HPPD inhibitors | 7.515 + 120.15 | Cabatex extra | Corteva Agriscience Hellas, Athens, Greece |
T6 | Mesotrione + nicosulfuron | 4-HPPD + ALS inhibitors | 112.5 + 45 | Elumis 105 OD | Syngenta Hellas, Athens, Greece |
T7 2 | 2,4 D ester | Natural auxins | 600 | Crossbow 600 EC | Corteva Agriscience Hellas, Athens, Greece |
Plots of Treatments | Pyrgos1 | Pyrgos2 | Pyrgos3 | Pyrgos4 |
---|---|---|---|---|
1st Experimental Year | ||||
T1 | 0.37Aab | 0.69 Abc | 0.86 Aa | 0.43 Aab |
T2 | 0.40 Aab | 0.74 Ab | 0.90 Aa | 0.44 Aa |
T3 | 0.40 Aab | 0.7 Aabc | 0.87 Aa | 0.46 Aab |
T4 | 0.41 Aa | 0.75 Aa | 0.87 Aa | 0.43 Aab |
T5 | 0.39 Ab | 0.75 Aa | 0.84 Aa | 0.41 Ab |
T6 | 0.37 Aab | 0.70 Aabc | 0.81 Aa | 0.44 Aab |
T7 | 0.33 Aab | 0.73 Ac | 0.82 Aa | 0.41 Aab |
2nd Experimental year | ||||
T1 | 0.39 Aab | 0.70 Abc | 0.88 Aa | 0.44 Aab |
T2 | 0.41 Aab | 0.72 Ab | 0.84 Aa | 0.45 Aa |
T3 | 0.37 Aab | 0.68 Abc | 0.91 Aa | 0.44 Aab |
T4 | 0.41 Aa | 0.74 Aa | 0.88 Aa | 0.41 Aab |
T5 | 0.34 Ab | 0.73 Aa | 0.89 Aa | 0.42 Ab |
T6 | 0.41 Aab | 0.74 Aabc | 0.90 Aa | 0.40 Aab |
T7 | 0.42 Aab | 0.71 Ac | 0.83 Aa | 0.44 Aab |
LSDT | 0.8574 | 0.0413 | 0.0506 | 0.0318 |
LSDY | 0.025061 | 0.023864 | 0.0292 | 0.018381 |
T | ns | * | ns | ns |
Y | * | ns | ns | ns |
NDVI Values | Total Weed Biomass (kg·ha−1) | |||||||
---|---|---|---|---|---|---|---|---|
1st Experimental Year | ||||||||
Treatments | Pyrgos1 | Pyrgos2 | Pyrgos3 | Pyrgos4 | Pyrgos1 | Pyrgos2 | Pyrgos3 | Pyrgos4 |
T1 | 0.76 Aa | 0.78 Aa | 0.72 Aa | 0.66 Aa | 1604 Aa | 1358 Aa | 1361.7 Aa | 1320.3 Aa |
T2 | 0.55 Ad | 0.62 Abc | 0.57 Aab | 0.55 Aab | 497.6 Ac | 543 Ac | 603.7 Ac | 626 Ab |
T3 | 0.48 Acd | 0.51 Ac | 0.69 Ab | 0.48 Ac | 643 Ac | 498 Ac | 753 Ab | 392 Ac |
T4 | 0.65 Abc | 0.61 Abc | 0.71 Aab | 0.62 Aab | 696 Ab | 696 Ab | 838 Ab | 647 Ab |
T5 | 0.67 Aab | 0.70 Aab | 0.45 Ac | 0.60 Aab | 680.6 Ab | 653 Ab | 488 Ac | 601 Ab |
T6 | 0.54 Ad | 0.57 Ac | 0.61 Aab | 0.55 Ab | 529.3 Acd | 558 Acd | 753 Ab | 552 Ab |
T7 | 0.59 Abcd | 0.68 Abc | 0.42 Ac | 0.51 Aab | 671.7 Abc | 705 Abc | 492.7 Ac | 599 Ab |
2nd Experimental year | ||||||||
Pyrgos1 | Pyrgos2 | Pyrgos3 | Pyrgos4 | Pyrgos1 | Pyrgos2 | Pyrgos3 | Pyrgos4 | |
T1 | 0.71 Aa | 0.69 Ba | 0.71 Aa | 0.69 Aa | 1642 Aa | 1463 Aa | 1113 Aa | 1437 Aa |
T2 | 0.42 Ad | 0.51 Bbc | 0.68 Aab | 0.59 aab | 411.6 Ac | 514 Ac | 583.7 Ac | 611.7 Ab |
T3 | 0.58 Acd | 0.54 Bc | 0.55 Ab | 0.41 Ac | 626.7 Ac | 411.7 Ac | 809.7 Ab | 418.7 Ac |
T4 | 0.6 Abc | 0.53 Abc | 0.65 Aab | 0.60 Aab | 722 Ab | 722 Ab | 827.7 Ab | 543 Ab |
T5 | 0.62 Aab | 0.63 Aab | 0.54 Ac | 0.56 Aab | 756 Ab | 756 Ab | 5147 Ac | 679 Ab |
T6 | 0.47 Ad | 0.52 Bc | 0.68 Aab | 0.63 Ab | 590.4 Acd | 590.3 Acd | 723 Ab | 572.7 Ab |
T7 | 0.54 Abcd | 0.53 Abc | 0.58 Ac | 0.54 Aab | 680.6 Abc | 68.07 Abc | 57.27 Ac | 63.7 Ab |
LSDT | 0.0985 | 0.0985 | 0.0943 | 0.0856 | 129.44 | 128.56 | 127.457 | 132.435 |
LSDY | 0.0342 | 0.0509 | 0.0504 | 0.0645 | 69.188 | 69.188 | 68.128 | 70.79 |
T | *** | *** | *** | *** | *** | *** | *** | *** |
Y | ns | *** | ns | ns | ns | ** | ns | ns |
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Tsekoura, A.; Gazoulis, I.; Antonopoulos, N.; Kousta, A.; Kanatas, P.; Travlos, I. Quick In Situ Evaluation of Herbicide Efficacy in Maize (Zea mays L.) Crop. Agrochemicals 2024, 3, 12-21. https://doi.org/10.3390/agrochemicals3010002
Tsekoura A, Gazoulis I, Antonopoulos N, Kousta A, Kanatas P, Travlos I. Quick In Situ Evaluation of Herbicide Efficacy in Maize (Zea mays L.) Crop. Agrochemicals. 2024; 3(1):12-21. https://doi.org/10.3390/agrochemicals3010002
Chicago/Turabian StyleTsekoura, Anastasia, Ioannis Gazoulis, Nikolaos Antonopoulos, Angeliki Kousta, Panagiotis Kanatas, and Ilias Travlos. 2024. "Quick In Situ Evaluation of Herbicide Efficacy in Maize (Zea mays L.) Crop" Agrochemicals 3, no. 1: 12-21. https://doi.org/10.3390/agrochemicals3010002