Crop Diversification for Improved Weed Management: A Review
Abstract
:1. Introduction
2. Crop Diversification Focused on Weed Management
2.1. Crop Rotation
2.2. Intercropping
2.3. Cover Crops
Weed Species = Palmer Amaranth (Amaranthus palmeri) | ||
---|---|---|
Diversification Strategy * | Details | Reference |
Crop Rotation in cotton with corn | Crop rotation of cotton and corn reduce the risk of developing glyphosate resistance in Palmer amaranth by ~50% | [74] |
Cover crops for corn | Hairy vetch and crimson clover provide early season suppression for glyphosate-resistant Palmer Amaranth in corn | [141] |
Cover crops for cotton | Austrian winterpea (Lathyrus hirsutus L.), cereal rye, crimson clover, hairy vetch, oats, (Avena sativa L.), rapeseed, (Brassica napus L.), and wheat can be used to reduce Palmer amaranth emergence in cotton | [147,148] |
Cover crop for glyphosate- and dicamba-tolerant (GDT) soybean | Hairy vetch and wheat were effective to control Palmer amaranth. However, cover crop termination and herbicide program should be taken into consideration for maximum yield and highest weed control | [149] |
Weed Species = Blackgrass (Alopecurus myosuroides) | ||
Cover crop for various crops | Ryegrass as a cover crop can help to reduce the emergence of blackgrass by 17%. It can be used as a cover crop in corn, soybean, and winter-wheat | [150] |
Crop rotation of winter-annual and spring crops | Five-year rotation with winter wheat, corn, summer barley, winter oilseed rape, and winter wheat reduce blackgrass densities by 50% as compared to winter wheat and winter oilseed rape rotation | [151] |
Weed Species = Rigid Ryegrass (Lolium rigidum) | ||
Crop rotation | Oaten hay (Avena sativa), filed pea, wheat, and barley crop rotation helps to deplete rigid ryegrass seedbank, reduce in-crop weed infestation, and higher profitability | [152] |
Cover crop in corn | Velvet bean (Mucuna pruriens (L.) DC. var. utilis) has allelopathic potential and can help to reduce rigid ryegrass biomass, height, and leaf number | [153] |
3. Major Constraints to Adoption of Crop Diversification in Modern Agriculture
4. Crop Diversification in the Precision Agriculture Era
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- WSSA GLOSSARY. Available online: https://wssa.net/wssa/wssa-glossary/ (accessed on 2 May 2021).
- Harlan, J.R.; de Wet, J.M.J. Some Thoughts about Weeds. Econ. Bot. 1965, 19, 16–24. [Google Scholar] [CrossRef]
- Cousens, R. A Simple Model Relating Yield Loss to Weed Density. Ann. Appl. Biol. 1985, 107, 239–252. [Google Scholar] [CrossRef]
- Chauhan, B.S. Grand Challenges in Weed Management. Front. Agron. 2020, 1. [Google Scholar] [CrossRef]
- Fahad, S.; Hussain, S.; Chauhan, B.S.; Saud, S.; Wu, C.; Hassan, S.; Tanveer, M.; Jan, A.; Huang, J. Weed Growth and Crop Yield Loss in Wheat as Influenced by Row Spacing and Weed Emergence Times. Crop Prot. 2015, 71, 101–108. [Google Scholar] [CrossRef]
- Oerke, E.-C. Crop Losses to Pests. J. Agric. Sci. 2006, 144, 31–43. [Google Scholar] [CrossRef]
- Loux, M.M.; Doohan, D.; Dobbels, A.F.; Johnson, W.G.; Young, B.G.; Legleiter, T.R.; Hager, A. Weed Control Guide for Ohio; University of Illinois: Champaign, IL, USA, 2017; pp. 1–2. [Google Scholar]
- Soltani, N.; Dille, J.A.; Burke, I.C.; Everman, W.J.; VanGessel, M.J.; Davis, V.M.; Sikkema, P.H. Perspectives on Potential Soybean Yield Losses from Weeds in North America. Weed Technol. 2017, 31, 148–154. [Google Scholar] [CrossRef] [Green Version]
- Soltani, N.; Dille, J.A.; Burke, I.C.; Everman, W.J.; VanGessel, M.J.; Davis, V.M.; Sikkema, P.H. Potential Corn Yield Losses from Weeds in North America. Weed Technol. 2016, 30, 979–984. [Google Scholar] [CrossRef]
- Llewellyn, R.S.; Ronning, D.; Ouzman, J.; Walker, S.; Mayfield, A.; Clarke, M. Impact of Weeds on Australian Grain Production: The Cost of Weeds to Australian Grain Growers and the Adoption of Weed Management and Tillage Practices; Report for Grains Research & Development Corporation: Canberra, Australia, 2016. [Google Scholar]
- Gharde, Y.; Singh, P.K.; Dubey, R.P.; Gupta, P.K. Assessment of Yield and Economic Losses in Agriculture Due to Weeds in India. Crop Prot. 2018, 107, 12–18. [Google Scholar] [CrossRef]
- Mesterházy, Á.; Oláh, J.; Popp, J. Losses in the Grain Supply Chain: Causes and Solutions. Sustainability 2020, 12, 2342. [Google Scholar] [CrossRef] [Green Version]
- Ramesh, K.; Matloob, A.; Aslam, F.; Florentine, S.K.; Chauhan, B.S. Weeds in a Changing Climate: Vulnerabilities, Consequences, and Implications for Future Weed Management. Front. Plant Sci. 2017, 8, 95. [Google Scholar] [CrossRef]
- Tirado, R.; Englande, A.J.; Promakasikorn, L.; Novotny, V. Use of Agrochemicals in Thailand and Its Consequences for the Environment. Available online: http://www.greenpeace.to/publications/GPSEA_agrochemical-use-in-thailand.pdf (accessed on 28 March 2021).
- Gianessi, L.P. The Increasing Importance of Herbicides in Worldwide Crop Production: The Increasing Importance of Herbicides. Pest Manag. Sci. 2013, 69, 1099–1105. [Google Scholar] [CrossRef] [PubMed]
- Pariona, A. Top Pesticide Using Countries. Available online: https://www.worldatlas.com/articles/top-pesticide-consuming-countries-of-the-world.html (accessed on 28 March 2021).
- Oca, A.M.; Arreola, L.; Flores, A.; Sanchez, J.; Flores, G. Low-Cost Multispectral Imaging System for Crop Monitoring; IEEE: Dallas, TX, USA, 2018. [Google Scholar]
- Available online: http://www.weedscience.org/Home.aspx (accessed on 28 March 2021).
- Egan, J.F.; Mortensen, D.A. Quantifying Vapor Drift of Dicamba Herbicides Applied to Soybean. Environ. Toxicol. Chem. 2012, 31, 1023–1031. [Google Scholar] [CrossRef] [PubMed]
- Perrino, E.V.; Calabrese, G. Endangered Segetal Species in Southern Italy: Distribution, Conservation Status, Trends, Actions and Ethnobotanical Notes. Genet. Resour. Crop Evol. 2018, 65, 2107–2134. [Google Scholar] [CrossRef]
- Shrestha, S.; Sharma, G.; Burgos, N.R.; Tseng, T.-M. Response of Weedy Rice (Oryza spp.) Germplasm from Arkansas to Glyphosate, Glufosinate, and Flumioxazin. Weed Sci. 2019, 67, 303–310. [Google Scholar] [CrossRef]
- Yu, Q.; Cairns, A.; Powles, S. Glyphosate, Paraquat and ACCase Multiple Herbicide Resistance Evolved in a Lolium Rigidum Biotype. Planta 2007, 225, 499–513. [Google Scholar] [CrossRef]
- Owen, M.J.; Walsh, M.J.; Llewellyn, R.S.; Powles, S.B. Widespread Occurrence of Multiple Herbicide Resistance in Western Australian Annual Ryegrass (Lolium Rigidum) Populations. Aust. J. Agric. Res. 2007, 58, 711. [Google Scholar] [CrossRef]
- Tseng, T.-M.; Shrestha, S.; McCurdy, J.D.; Wilson, E.; Sharma, G. Target-Site Mutation and Fitness Cost of Acetolactate Synthase Inhibitor-Resistant Annual Bluegrass. HortScience 2019, 54, 701–705. [Google Scholar] [CrossRef]
- Yuan, J.S.; Tranel, P.J.; Stewart, C.N., Jr. Non-Target-Site Herbicide Resistance: A Family Business. Trends Plant Sci. 2007, 12, 6–13. [Google Scholar] [CrossRef]
- Duke, S.O. Why Have No New Herbicide Modes of Action Appeared in Recent Years? Pest Manag. Sci. 2012, 68, 505–512. [Google Scholar] [CrossRef] [Green Version]
- Egan, J.F.; Barlow, K.M.; Mortensen, D.A. A Meta-Analysis on the Effects of 2,4-D and Dicamba Drift on Soybean and Cotton. Weed Sci. 2014, 62, 193–206. [Google Scholar] [CrossRef]
- Suarez Cadavid, L.A. Proximal and Remote Sensing for Early Detection and Assessment of Herbicide Drift Damage on Cotton Crops; University of Southern Queensland: Toowoomba, Australia, 2018. [Google Scholar]
- Zhang, J.; Huang, Y.; Reddy, K.N.; Wang, B. Assessing Crop Damage from Dicamba on Non-dicamba-tolerant Soybean by Hyperspectral Imaging through Machine Learning. Pest Manag. Sci. 2019, 75, 3260–3272. [Google Scholar] [CrossRef]
- Udeigwe, T.K.; Teboh, J.M.; Eze, P.N.; Stietiya, M.H.; Kumar, V.; Hendrix, J.; Mascagni, H.J., Jr.; Ying, T.; Kandakji, T. Implications of Leading Crop Production Practices on Environmental Quality and Human Health. J. Environ. Manag. 2015, 151, 267–279. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Gilliom, R.J. Pesticides in U.S. Streams and Groundwater. Environ. Sci. Technol. 2007, 41, 3408–3414. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Van Bruggen, A.H.C.; He, M.M.; Shin, K.; Mai, V.; Jeong, K.C.; Finckh, M.R.; Morris, J.G., Jr. Environmental and Health Effects of the Herbicide Glyphosate. Sci. Total Environ. 2018, 616–617, 255–268. [Google Scholar] [CrossRef]
- Sterling, T.D.; Arundel, A.V. Health Effects of Phenoxy Herbicides: A Review. Scand. J. Work Environ. Health 1986, 12, 161–173. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Liebman, M.; Staver, C.P. Crop Diversification for Weed Management. In Ecological Management of Agricultural Weeds; Cambridge University Press: Cambridge, UK, 2001; pp. 322–374. [Google Scholar]
- Smith, R.G.; Gross, K.L. Assembly of Weed Communities along a Crop Diversity Gradient. J. Appl. Ecol. 2007, 44, 1046–1056. [Google Scholar] [CrossRef]
- Kremen, C.; Miles, A. Ecosystem Services in Biologically Diversified versus Conventional Farming Systems: Benefits, Externalities, and Trade-Offs. Ecol. Soc. 2012, 1. [Google Scholar] [CrossRef]
- Liebman, M.; Dyck, E. Crop Rotation and Intercropping Strategies for Weed Management. Ecol. Appl. 1993, 3, 92–122. [Google Scholar] [CrossRef]
- Hufnagel, J.; Reckling, M.; Ewert, F. Diverse Approaches to Crop Diversification in Agricultural Research. A Review. Agron. Sustain. Dev. 2020, 40. [Google Scholar] [CrossRef]
- Bommarco, R.; Kleijn, D.; Potts, S.G. Ecological Intensification: Harnessing Ecosystem Services for Food Security. Trends Ecol. Evol. 2013, 28, 230–238. [Google Scholar] [CrossRef]
- Lin, B.B. Resilience in Agriculture through Crop Diversification: Adaptive Management for Environmental Change. Bioscience 2011, 61, 183–193. [Google Scholar] [CrossRef] [Green Version]
- Meynard, J.M.; Messéan, A.; Charlier, A.; Charrier, F.; Farès, M.; Le Bail, M.; Savini, I. Crop Diversification: Obstacles and Levers; INRA: Paris, France, 2013. [Google Scholar]
- Blaix, C.; Moonen, A.C.; Dostatny, D.F.; Izquierdo, J.; Le Corff, J.; Morrison, J.; Von Redwitz, C.; Schumacher, M.; Westerman, P.R. Quantification of Regulating Ecosystem Services Provided by Weeds in Annual Cropping Systems Using a Systematic Map Approach. Weed Res. 2018, 58, 151–164. [Google Scholar] [CrossRef]
- Capinera, J.L. Relationships between Insect Pests and Weeds: An Evolutionary Perspective. Weed Sci. 2005, 53, 892–901. [Google Scholar] [CrossRef]
- Bretagnolle, V.; Gaba, S. Weeds for Bees? A Review. Agron. Sustain. Dev. 2015, 35, 891–909. [Google Scholar] [CrossRef] [Green Version]
- Smith, B.M.; Aebischer, N.J.; Ewald, J.; Moreby, S.; Potter, C.; Holland, J.M. The Potential of Arable Weeds to Reverse Invertebrate Declines and Associated Ecosystem Services in Cereal Crops. Front. Sustain. Food Syst. 2020, 3. [Google Scholar] [CrossRef]
- Mouritsen, O.G. Those Tasty Weeds. J. Appl. Phycol. 2017, 29, 2159–2164. [Google Scholar] [CrossRef]
- Amato-Lourenco, L.F.; Ranieri, G.R.; de Oliveira Souza, V.C.; Junior, F.B.; Saldiva, P.H.N.; Mauad, T. Edible Weeds: Are Urban Environments Fit for Foraging? Sci. Total Environ. 2020, 698, 133967. [Google Scholar] [CrossRef]
- Varvel, G.E. Crop Rotation and Nitrogen Effects on Normalized Grain Yields in a Long-term Study. Agron. J. 2000, 92, 938–941. [Google Scholar] [CrossRef] [Green Version]
- Bowles, T.M.; Mooshammer, M.; Socolar, Y.; Calderón, F.; Cavigelli, M.A.; Culman, S.W.; Deen, W.; Drury, C.F.; Garcia y Garcia, A.; Gaudin, A.C.M.; et al. Long-Term Evidence Shows That Crop-Rotation Diversification Increases Agricultural Resilience to Adverse Growing Conditions in North America. One Earth 2020, 2, 284–293. [Google Scholar] [CrossRef]
- Zhao, J.; Yang, Y.; Zhang, K.; Jeong, J.; Zeng, Z.; Zang, H. Does Crop Rotation Yield More in China? A Meta-Analysis. Field Crops Res. 2020, 24, 107659. [Google Scholar] [CrossRef]
- Weisberger, D.; Nichols, V.; Liebman, M. Does Diversifying Crop Rotations Suppress Weeds? A Meta-Analysis. PLoS ONE 2019, 14, e0219847. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Anderson, R.L. Managing Weeds with a Dualistic Approach of Prevention and Control. A Review. Agron. Sustain. Dev. 2007, 27, 13–18. [Google Scholar] [CrossRef] [Green Version]
- Liebman, M.; Miller, Z.J.; Williams, C.L.; Westerman, P.R.; Dixon, P.M.; Heggenstaller, A.; Sundberg, D.N. Fates of Setaria Faberi and Abutilon Theophrasti Seeds in Three Crop Rotation Systems. Weed Res. 2014, 54, 293–306. [Google Scholar] [CrossRef]
- Simić, M.; Spasojević, I.; Kovacević, D.; Brankov, M.; Dragicević, V. Crop Rotation Influence on Annual and Perennial Weed Control and Maize Productivity. Rom Agric Res. 2016, 33, 125–133. [Google Scholar]
- Satorre, E.H.; de la Fuente, E.B.; Mas, M.T.; Suárez, S.A.; Kruk, B.C.; Guglielmini, A.C.; Verdú, A.M.C. Crop Rotation Effects on Weed Communities of Soybean (Glycine max L. Merr.) Agricultural Fields of the Flat Inland Pampa. Crop Prot. 2020, 130, 105068. [Google Scholar] [CrossRef]
- Simić, M.S.; Dragičević, V.; Chachalis, D.; Dolijanović, Ž.; Brankov, M. Integrated Weed Management in Long-Term Maize Cultivation. Zemdirbyste 2020, 107, 33–40. [Google Scholar] [CrossRef] [Green Version]
- Mishra, J.S.; Kumar, R.; Kumar, R.; Rao, K.K.; Bhatt, B.P. Weed Density and Species Composition in Rice-Based Cropping Systems as Affected by Tillage and Crop Rotation. Ind. J. Weed Sci. 2019, 51, 116. [Google Scholar] [CrossRef]
- Liebman, M.; Nichols, V.A. Cropping System Redesign for Improved Weed Management: A Modeling Approach Illustrated with Giant Ragweed (Ambrosia Trifida). Agronomy 2020, 10, 262. [Google Scholar] [CrossRef] [Green Version]
- Schönhart, M.; Schmid, E.; Schneider, U.A. CropRota–A Crop Rotation Model to Support Integrated Land Use Assessments. Eur. J. Agron. 2011, 34, 263–277. [Google Scholar] [CrossRef]
- Dury, J.; Schaller, N.; Garcia, F.; Reynaud, A.; Bergez, J.E. Models to Support Cropping Plan and Crop Rotation Decisions. A Review. Agron. Sustain. Dev. 2012, 32, 567–580. [Google Scholar] [CrossRef] [Green Version]
- Dogliotti, S.; Rossing, W.A.H.; van Ittersum, M.K. Rotat, a Tool for Systematically Generating Crop Rotations. Eur. J. Agron. 2003, 19, 239–250. [Google Scholar] [CrossRef]
- Colbach, N.; Colas, F.; Pointurier, O.; Queyrel, W.; Villerd, J. A Methodology for Multi-Objective Cropping System Design Based on Simulations. Application to Weed Management. Eur. J. Agron. 2017, 87, 59–73. [Google Scholar] [CrossRef]
- Haring, S.C.; Flessner, M.L. Improving Soil Seed Bank Management: Improving Soil Seed Bank Management. Pest Manag. Sci. 2018, 74, 2412–2418. [Google Scholar] [CrossRef] [PubMed]
- Anderson, R. An Ecological Approach to Strengthen Weed Management in the Semiarid Great Plains. In Adv. Agron; Elsevier: Amsterdam, The Netherlands, 2003; pp. 33–62. [Google Scholar]
- Anderson, R.L. Sequencing Crops to Minimize Selection Pressure for Weeds in the Central Great Plains1. Weed Technol. 2004, 18, 157–164. [Google Scholar] [CrossRef]
- Kumar, A.; Choudhary, T.; Das, S.; Meena, S.K. Weed Seed Bank: Impacts and Management for Future Crop Production. In Agronomic Crops; Springer: Singapore, 2019; pp. 207–223. [Google Scholar]
- Leibman, M.; Davis, A.S. Managing Weed in Organic Farming Systems: An Ecological Approach; Francis, C., Ed.; American Society of Agronomy: Madison, WI, USA, 2009; pp. 173–196. [Google Scholar]
- Cardina, J.; Herms, C.P.; Doohan, D.J. Crop Rotation and Tillage System Effects on Weed Seedbanks. Weed Sci. 2002, 50, 448–460. [Google Scholar] [CrossRef]
- Westerman, P.R.; Liebman, M.; Menalled, F.D.; Heggenstaller, A.H.; Hartzler, R.G.; Dixon, P.M. Are Many Little Hammers Effective? Velvetleaf (Abutilon Theophrasti) Population Dynamics in Two-and Four-Year Crop Rotation Systems. Weed Sci. 2005, 53, 382–392. [Google Scholar] [CrossRef]
- Oswald, A.; Ransom, J.K. Striga Control and Improved Farm Productivity Using Crop Rotation. Crop Prot. 2001, 20, 113–120. [Google Scholar] [CrossRef]
- Samaké, O.; Stomph, T.J.; Kropff, M.J.; Smaling, E.M.A. Integrated Pearl Millet Management in the Sahel: Effects of Legume Rotation and Fallow Management on Productivity and Striga Hermonthica Infestation. Plant Soil 2006, 286, 245–257. [Google Scholar] [CrossRef]
- Hayat, S.; Wang, K.; Liu, B.; Wang, Y.; Chen, F.; Li, P.; Hayat, K.; Ma, Y. A Two-Year Simulated Crop Rotation Confirmed the Differential Infestation of Broomrape Species in China Is Associated with Crop-Based Biostimulants. Agronomy 2019, 10, 18. [Google Scholar] [CrossRef] [Green Version]
- Norsworthy, J.K.; Ward, S.M.; Shaw, D.R.; Llewellyn, R.S.; Nichols, R.L.; Webster, T.M.; Bradley, K.W.; Frisvold, G.; Powles, S.B.; Burgos, N.R.; et al. Reducing the Risks of Herbicide Resistance: Best Management Practices and Recommendations. Weed Sci. 2012, 60, 31–62. [Google Scholar] [CrossRef] [Green Version]
- Neve, P.; Norsworthy, J.K.; Smith, K.L.; Zelaya, I.A. Modeling Glyphosate Resistance Management Strategies for Palmer Amaranth (Amaranthus palmeri) in Cotton. Weed Technol. 2011, 25, 335–343. [Google Scholar] [CrossRef]
- Lutman, P.J.W.; Moss, S.R.; Cook, S.; Welham, S.J. A Review of the Effects of Crop Agronomy on the Management of ALopecurus Myosuroides. Weed Res. 2013, 53, 299–313. [Google Scholar] [CrossRef]
- Moss, S.R.; Hull, R. Quantifying the Benefits of Spring Cropping for Control of Alopecurus Myosuroides Black-Grass. Asp. Appl. Biol. 2012, 117, 1–6. [Google Scholar]
- Ulber, L.; Rissel, D. Farmers’ Perspective on Herbicide-Resistant Weeds and Application of Resistance Management Strategies: Results from a German Survey. Pest Manag. Sci. 2018, 74, 2335–2345. [Google Scholar] [CrossRef]
- Beckie, H.J.; Harker, K.N. Our Top 10 Herbicide-Resistant Weed Management Practices. Pest Manag. Sci. 2017, 73, 1045–1052. [Google Scholar] [CrossRef]
- Goplen, J.J.; Coulter, J.A.; Sheaffer, C.C.; Becker, R.L.; Breitenbach, F.R.; Behnken, L.M.; Gunsolus, J.L. Economic Performance of Crop Rotations in the Presence of Herbicide-Resistant Giant Ragweed. Agron. J. 2018, 110, 260–268. [Google Scholar] [CrossRef] [Green Version]
- Goplen, J.J.; Sheaffer, C.C.; Becker, R.L.; Coulter, J.A.; Breitenbach, F.R.; Behnken, L.M.; Johnson, G.A.; Gunsolus, J.L. Seedbank Depletion and Emergence Patterns of Giant Ragweed (Ambrosia trifida) in Minnesota Cropping Systems. Weed Sci. 2017, 65, 52–60. [Google Scholar] [CrossRef] [Green Version]
- Simmonds, N.W.; Vandermeer, J. The Ecology of Intercropping. J. Appl. Ecol. 1989, 26, 1107. [Google Scholar] [CrossRef]
- Ngwira, A.R.; Aune, J.B.; Mkwinda, S. On-Farm Evaluation of Yield and Economic Benefit of Short-Term Maize Legume Intercropping Systems under Conservation Agriculture in Malawi. Field Crops Res. 2012, 132, 149–157. [Google Scholar] [CrossRef]
- Brooker, R.W.; Bennett, A.E.; Cong, W.-F.; Daniell, T.J.; George, T.S.; Hallett, P.D.; Hawes, C.; Iannetta, P.P.M.; Jones, H.G.; Karley, A.J.; et al. Improving Intercropping: A Synthesis of Research in Agronomy, Plant Physiology and Ecology. New Phytol. 2015, 206, 107–117. [Google Scholar] [CrossRef]
- Lithourgidis, A.S.; Dordas, C.A.; Damalas, C.A.; Vlachostergios, D. Annual Intercrops: An Alternative Pathway for Sustainable Agriculture. Aust. J. Crop Sci. 2011, 5, 396. [Google Scholar]
- Smith, J.; Pearce, B.D.; Wolfe, M.S. Reconciling Productivity with Protection of the Environment: Is Temperate Agroforestry the Answer? Renew. Agric. Food Syst. 2013, 28, 80–92. [Google Scholar] [CrossRef]
- Pakeman, R.J.; Brooker, R.W.; Karley, A.J.; Newton, A.C.; Mitchell, C.; Hewison, R.L.; Schöb, C. 473 Increased Crop Diversity Reduces the Functional Space Available for Weeds. Weed Res. 2019, 60, 121–131. [Google Scholar] [CrossRef]
- Marschner, P. (Ed.) Mineral Nutrition of Higher Plants, 3rd ed.; Academic Press: Waltham, MA, USA, 2012. [Google Scholar]
- Bybee-Finley, K.A.; Mirsky, S.B.; Ryan, M.R. Crop Biomass Not Species Richness Drives Weed Suppression in Warm-Season Annual Grass–Legume Intercrops in the Northeast. Weed Sci. 2017, 65, 669–680. [Google Scholar] [CrossRef]
- Stefan, L.; Engbersen, N.; Schöb, C. Crop-Weed Relationships Are Context-Dependent and Cannot Fully Explain the Positive Effects of Intercropping on Yield. Ecol. Appl. 2021, e2311. [Google Scholar] [CrossRef]
- Verret, V.; Gardarin, A.; Pelzer, E.; Médiène, S.; Makowski, D.; Valantin-Morison, M. Can Legume Companion Plants Control Weeds without Decreasing Crop Yield? A Meta-Analysis. Field Crops Res. 2017, 204, 158–168. [Google Scholar] [CrossRef]
- Rodriguez, C.; Carlsson, G.; Englund, J.-E.; Flöhr, A.; Pelzer, E.; Jeuffroy, M.-H.; Makowski, D.; Jensen, E.S. Grain Legume-Cereal Intercropping Enhances the Use of Soil-Derived and Biologically Fixed Nitrogen in Temperate Agroecosystems. A Meta-Analysis. Eur. J. Agron. 2020, 118, 126077. [Google Scholar] [CrossRef]
- Weed 427 Suppression Greatly Increased by Plant Diversity in Intensively Managed Grasslands: A Continental-Scale 428 Experiment. J. Appl. Ecol. 2017, 55, 852–862.
- Corre-Hellou, G.; Dibet, A.; Hauggaard-Nielsen, H.; Crozat, Y.; Gooding, M.; Ambus, P.; Dahlmann, C.; von Fragstein, P.; Pristeri, A.; Monti, M.; et al. The Competitive Ability of Pea–Barley Intercrops against Weeds and the Interactions with Crop Productivity and Soil N Availability. Field Crops Res. 2011, 122, 264–272. [Google Scholar] [CrossRef] [Green Version]
- Saucke, H.; Ackermann, K. Weed Suppression in Mixed Cropped Grain Peas and False Flax (Camelina sativa). Weed Res. 2006, 6, 453–461. [Google Scholar] [CrossRef]
- Mathukia, R.K.; Mathukia, P.R.; Polara, A.M. Intercropping and Weed Management in Pearlmillet (Pennisetum glaucum) under Rainfed Condition. Agric. Sci. Dig. Res. J. 2015, 35, 138. [Google Scholar] [CrossRef]
- Cheriere, T.; Lorin, M.; Corre-Hellou, G. Species Choice and Spatial Arrangement in Soybean-Based Intercropping: Levers That Drive Yield and Weed Control. Field Crops Res. 2020, 256, 107923. [Google Scholar] [CrossRef]
- Jamshidi, K.; Yousefi, A.R.; Oveisi, M. Effect of Cowpea (Vigna unguiculata) Intercropping on Weed Biomass and Maize (Zea Mays) Yield. N. Z. J. Crop Hortic. Sci. 2013, 41, 180–188. [Google Scholar] [CrossRef] [Green Version]
- Farooq, M.; Jabran, K.; Cheema, Z.A.; Wahid, A.; Siddique, K.H.M. The Role of Allelopathy in Agricultural Pest Management. Pest Manag. Sci. 2011, 67, 493–506. [Google Scholar] [CrossRef] [PubMed]
- Tesio, F.; Ferrero, A. Allelopathy, a Chance for Sustainable Weed Management. Int. J. Sustain. Dev. World Ecol. 2010, 17, 377–389. [Google Scholar] [CrossRef]
- Makoi, J.H.; Ndakidemi, P.A. Allelopathy as Protectant, Defence and Growth Stimulants in Legume Cereal Mixed Culture Systems. N. Z. J. Crop Hortic. Sci. 2012, 40, 161–186. [Google Scholar] [CrossRef]
- Arowosegbe, S.; Afolayan, A.J. Assessment of Allelopathic Properties of Aloe Ferox Mill. on Turnip, Beetroot and Carrot. Biol. Res. 2012, 45, 363–368. [Google Scholar] [CrossRef] [Green Version]
- Głąb, L.; Sowiński, J.; Bough, R.; Dayan, F.E. Allelopathic Potential of Sorghum (Sorghum Bicolor (L.) Moench) in Weed Control: A Comprehensive Review. Adv. Agron 2017, 145, 43–95. [Google Scholar]
- Sowiński, J.; Dayan, F.E.; Głąb, L.; Adamczewska-Sowińska, K. Sorghum Allelopathy for Sustainable Weed Management. In Progress in Biological Control; Springer International Publishing: Cham, Switzerland, 2020; pp. 263–288. [Google Scholar]
- Kandhro, M.N.; Tunio, S.; Rajpar, I.; Chachar, Q. Allelopathic Impact of Sorghum and Sunflower Intercropping on Weed Management and Yield Enhancement in Cotton. Sarhad J. Agric. 2014, 30, 312–318. [Google Scholar]
- Mahmood, A.; Cheema, Z.A.; Mushtaq, M.N.; Farooq, M. Maize–Sorghum Intercropping Systems for Purple Nutsedge Management. Arch. Acker Pflanzenbau Bodenkd. 2013, 59, 1279–1288. [Google Scholar] [CrossRef]
- dos Santos, R.C.; de Morais Guerra Ferraz, G.; de Albuquerque, M.B.; de Lima, L.M.; de Albuquerque Melo Filho, P.; de Rezende Ramos, A. Temporal Expression of the Sor1 Gene and Inhibitory Effects of Sorghum Bicolor L. Moench on Three Weed Species. Acta Bot. Brasilica 2014, 28, 361–366. [Google Scholar] [CrossRef] [Green Version]
- Dhungana, S.K.; Kim, I.-D.; Adhikari, B.; Kim, J.-H.; Shin, D.-H. Reduced Germination and Seedling Vigor of Weeds with Root Extracts of Maize and Soybean, and the Mechanism Defined as Allelopathic. J. Crop Sci. Biotechnol. 2019, 22, 11–16. [Google Scholar] [CrossRef]
- Jabran, K. Sorghum Allelopathy for Weed Control. In Manipulation of Allelopathic Crops for Weed Control; Springer International Publishing: Cham, Switzerland, 2017; pp. 65–75. [Google Scholar]
- Blaise, D.; Manikandan, A.; Verma, P.; Nalayini, P.; Chakraborty, M.; Kranthi, K.R. Allelopathic Intercrops and Its Mulch as an Integrated Weed Management Strategy for Rainfed Bt-Transgenic Cotton Hybrids. Crop Prot. 2020, 135, 105214. [Google Scholar] [CrossRef]
- Iqbal, J.; Cheema, Z.A.; An, M. Intercropping of Field Crops in Cotton for the Management of Purple Nutsedge (Cyperus rotundus L.). Plant Soil 2007, 300, 163–171. [Google Scholar] [CrossRef]
- Oswald, A.; Ransom, J.K.; Kroschel, J.; Sauerborn, J. Intercropping Controls Striga in Maize Based Farming Systems. Crop Prot. 2002, 21, 367–374. [Google Scholar] [CrossRef]
- Fernández-Aparicio, M.; Emeran, A.A.; Rubiales, D. Inter-Cropping with Berseem Clover (Trifolium alexandrinum) Reduces Infection by Orobanche Crenata in Legumes. Crop Prot. 2010, 29, 867–871. [Google Scholar] [CrossRef] [Green Version]
- Khan, Z.R.; Hassanali, A.; Overholt, W.; Khamis, T.M.; Hooper, A.M.; Pickett, J.A.; Wadhams, L.J.; Woodcock, C.M. Control of Witchweed Striga Hermonthica by Intercropping with Desmodium Spp., and the Mechanism Defined as Allelopathic. J. Chem. Ecol. 2002, 28, 1871–1885. [Google Scholar] [CrossRef]
- Cimmino, A.; Fernández-Aparicio, M.; Avolio, F.; Yoneyama, K.; Rubiales, D.; Evidente, A. Ryecyanatines A and B and Ryecarbonitrilines A and B, Substituted Cyanatophenol, Cyanatobenzo [1, 3] Dioxole, and Benzo [1, 3] Dioxolecarbonitriles from Rye (Secale cereale L.) Root Exudates: Novel Metabolites with Allelopathic Activity on Orobanche Seed Germination and Radicle Growth. Phytochemistry 2015, 109, 57–65. [Google Scholar]
- Blanco-Canqui, H.; Shaver, T.M.; Lindquist, J.L.; Shapiro, C.A.; Elmore, R.W.; Francis, C.A.; Hergert, G.W. Cover Crops and Ecosystem Services: Insights from Studies in Temperate Soils. Agron. J. 2015, 107, 2449–2474. [Google Scholar] [CrossRef] [Green Version]
- Baraibar, B.; Hunter, M.C.; Schipanski, M.E.; Hamilton, A.; Mortensen, D.A. Weed Suppression in Cover Crop Monocultures and Mixtures. Weed Sci. 2018, 66, 121–133. [Google Scholar] [CrossRef]
- Thorup-Kristensen, K.; Magid, J.; Jensen, L.S. Catch Crops and Green Manures as Biological Tools in Nitrogen Management in Temperate Zones. In Adv. Agron; Elsevier: Amsterdam, The Netherlands, 2003; pp. 227–302. [Google Scholar]
- Teasdale, J.R.; Abdul-Baki, A.A.; Bong Park, Y. Sweet Corn Production and Efficiency of Nitrogen Use in High Cover Crop Residue. Agron. Sustain. Dev. 2008, 28, 559–565. [Google Scholar] [CrossRef] [Green Version]
- Peachey, R.E.; William, R.D.; Mallory-Smith, C. Effect of No-till or Conventional Planting and Cover Crops Residues on Weed Emergence in Vegetable Row Crop. Weed Technol. 2004, 18, 1023–1030. [Google Scholar] [CrossRef]
- Wallace, J.; Williams, A.; Liebert, J.; Ackroyd, V.; Vann, R.; Curran, W.; Keene, C.; VanGessel, M.; Ryan, M.; Mirsky, S. Cover Crop-Based, Organic Rotational No-till Corn and Soybean Production Systems in the Mid-Atlantic United States. Agriculture 2017, 7, 34. [Google Scholar] [CrossRef] [Green Version]
- Singh, H.P.; Batish, D.R.; Kohli, R.K. Allelopathic Interactions and Allelochemicals: New Possibilities for Sustainable Weed Management. CRC Crit. Rev. Plant Sci. 2003, 22, 239–311. [Google Scholar] [CrossRef]
- Nichols, V.; Martinez-Feria, R.; Weisberger, D.; Carlson, S.; Basso, B.; Basche, A. Cover Crops and Weed Suppression in the US Midwest: A Meta-analysis and Modeling Study. Agric. Environ. Lett. 2020, 5, 20022. [Google Scholar] [CrossRef]
- Brust, J.; Claupein, W.; Gerhards, R. Growth and Weed Suppression Ability of Common and New Cover Crops in Germany. Crop Prot. 2014, 63, 1–8. [Google Scholar] [CrossRef]
- Myers, R.; Watts, C. Progress and Perspectives with Cover Crops: Interpreting Three Years of Farmer Surveys on Cover Crops. J. Soil Water Conserv. 2015, 70, 125A–129A. [Google Scholar] [CrossRef]
- Osipitan, O.A.; Dille, J.A.; Assefa, Y.; Radicetti, E.; Ayeni, A.; Knezevic, S.Z. Impact of Cover Crop Management on Level of Weed Suppression: A Meta-Analysis. Crop Sci. 2019, 59, 833–842. [Google Scholar] [CrossRef]
- Mennan, H.; Jabran, K.; Zandstra, B.H.; Pala, F. Non-Chemical Weed Management in Vegetables by Using Cover Crops: A Review. Agronomy 2020, 10, 257. [Google Scholar] [CrossRef] [Green Version]
- DeVore, J.D.; Norsworthy, J.K.; Brye, K.R. Influence of Deep Tillage, a Rye Cover Crop, and Various Soybean Production Systems on Palmer Amaranth Emergence in Soybean. Weed Technol. 2013, 27, 263–270. [Google Scholar] [CrossRef]
- Kadziene, G.; Suproniene, S.; Auskalniene, O.; Pranaitiene, S.; Svegzda, P.; Versuliene, A.; Ceseviciene, J.; Janusauskaite, D.; Feiza, V. Tillage and Cover Crop Influence on Weed Pressure and Fusarium Infection in Spring Cereals. Crop Prot. 2020, 127, 104966. [Google Scholar] [CrossRef]
- Weber, J.; Kunz, C.; Peteinatos, G.; Zikeli, S.; Gerhards, R. Weed Control Using Conventional Tillage, Reduced Tillage, No-Tillage, and Cover Crops in Organic Soybean. Agriculture 2017, 7, 43. [Google Scholar] [CrossRef] [Green Version]
- Brooker, A.P.; Renner, K.A.; Basso, B. Interseeding Cover Crops in Corn: Establishment, Biomass, and Competitiveness in On-farm Trials. Agron. J. 2020, 112, 3733–3743. [Google Scholar] [CrossRef]
- Büchi, L.; Wendling, M.; Amossé, C.; Jeangros, B.; Charles, R. Cover Crops to Secure Weed Control Strategies in a Maize Crop with Reduced Tillage. Field Crops Res. 2020, 247, 107583. [Google Scholar] [CrossRef]
- Finney, D.M.; Murrell, E.G.; White, C.M.; Baraibar, B.; Barbercheck, M.E.; Bradley, B.A.; Cornelisse, S.; Hunter, M.C.; Kaye, J.P.; Mortensen, D.A.; et al. Ecosystem Services and Disservices Are Bundled in Simple and Diverse Cover Cropping Systems. Agric. Environ. Lett. 2017, 2, 170033. [Google Scholar] [CrossRef]
- Finney, D.M.; White, C.M.; Kaye, J.P. Biomass Production and Carbon/Nitrogen Ratio Influence Ecosystem Services from Cover Crop Mixtures. Agron. J. 2016, 108, 39–52. [Google Scholar] [CrossRef] [Green Version]
- Smith, R.G.; Atwood, L.W.; Pollnac, F.W.; Warren, N.D. Cover-Crop Species as Distinct Biotic Filters in Weed Community Assembly. Weed Sci. 2015, 63, 282–295. [Google Scholar] [CrossRef]
- MacLaren, C.; Swanepoel, P.; Bennett, J.; Wright, J.; Dehnen-Schmutz, K. Cover Crop Biomass Production Is More Important than Diversity for Weed Suppression. Crop Sci. 2019, 59, 733–748. [Google Scholar] [CrossRef] [Green Version]
- Schappert, A.; Schumacher, M.; Gerhards, R. Weed Control Ability of Single Sown Cover Crops Compared to Species Mixtures. Agronomy 2019, 9, 294. [Google Scholar] [CrossRef] [Green Version]
- Florence, A.M.; McGuire, A.M. Do Diverse Cover Crop Mixtures Perform Better than Monocultures? A Systematic Review. Agron. J. 2020, 112, 3513–3534. [Google Scholar] [CrossRef]
- Rosario-Lebron, A.; Leslie, A.W.; Yurchak, V.L.; Chen, G.; Hooks, C.R.R. Can Winter Cover Crop Termination Practices Impact Weed Suppression, Soil Moisture, and Yield in No-till Soybean [Glycine max (L.) Merr.]? Crop Prot. 2019, 116, 132–141. [Google Scholar] [CrossRef]
- Wortman, S.E.; Francis, C.A.; Bernards, M.A.; Blankenship, E.E.; Lindquist, J.L. Mechanical Termination of Diverse Cover Crop Mixtures for Improved Weed Suppression in Organic Cropping Systems. Weed Sci. 2013, 61, 162–170. [Google Scholar] [CrossRef]
- Cholette, T.B.; Soltani, N.; Hooker, D.C.; Robinson, D.E.; Sikkema, P.H. Suppression of Glyphosate-Resistant Canada Fleabane (Conyza canadensis) in Corn with Cover Crops Seeded after Wheat Harvest the Previous Year. Weed Technol. 2018, 32, 244–250. [Google Scholar] [CrossRef]
- Wiggins, M.S.; McClure, M.A.; Hayes, R.M.; Steckel, L.E. Integrating Cover Crops and POST Herbicides for Glyphosate-Resistant Palmer Amaranth (Amaranthus palmeri) Control in Corn. Weed Technol. 2015, 29, 412–418. [Google Scholar] [CrossRef] [Green Version]
- Bunchek, J.M.; Wallace, J.M.; Curran, W.S.; Mortensen, D.A.; VanGessel, M.J.; Scott, B.A. Alternative Performance Targets for Integrating Cover Crops as a Proactive Herbicide-Resistance Management Tool. Weed Sci. 2020, 68, 534–544. [Google Scholar] [CrossRef]
- Gallandt, E.R. How Can We Target the Weed Seedbank? Weed Sci. 2006, 54, 588–596. [Google Scholar] [CrossRef]
- Moonen, A.C.; Barberi, P. Size and Composition of the Weed Seedbank after 7 Years of Different Cover-Crop-Maize Management Systems. Weed Res. 2004, 44, 163–177. [Google Scholar] [CrossRef]
- Buchanan, A.L.; Kolb, L.N.; Hooks, C.R.R. Can Winter Cover Crops Influence Weed Density and Diversity in a Reduced Tillage Vegetable System? Crop Prot. 2016, 90, 9–16. [Google Scholar] [CrossRef] [Green Version]
- Alonso-Ayuso, M.; Gabriel, J.L.; García-González, I.; Del Monte, J.P.; Quemada, M. Weed Density and Diversity in a Long-Term Cover Crop Experiment Background. Crop Prot. 2018, 112, 103–111. [Google Scholar] [CrossRef]
- Palhano, M.G.; Norsworthy, J.K.; Barber, T. Cover Crops Suppression of Palmer Amaranth (Amaranthus palmeri) in Cotton. Weed Technol. 2018, 32, 60–65. [Google Scholar] [CrossRef]
- Wiggins, M.S.; Hayes, R.M.; Steckel, L.E. Evaluating Cover Crops and Herbicides for Glyphosate-Resistant Palmer Amaranth (Amaranthus palmeri) Control in Cotton. Weed Technol. 2016, 30, 415–422. [Google Scholar] [CrossRef]
- Montgomery, G.B.; McClure, A.T.; Hayes, R.M.; Walker, F.R.; Senseman, S.A.; Steckel, L.E. Dicamba-Tolerant Soybean Combined Cover Crop to Control Palmer Amaranth. Weed Technol. 2018, 32, 109–115. [Google Scholar] [CrossRef]
- Cordeau, S.; Wayman, S.; Reibel, C.; Strbik, F.; Chauvel, B.; Guillemin, J.-P. Effects of Drought on Weed Emergence and Growth Vary with the Seed Burial Depth and Presence of a Cover Crop: Weed Emergence in No-till Systems. Weed Biol. Manag. 2018, 18, 12–25. [Google Scholar] [CrossRef]
- Zeller, A.; Kaiser, Y.; Gerhards, R. Suppressing Alopecurus Myosuroides Huds. In Rotations of Winter-Annual and Spring Crops. Agriculture 2018, 8, 91. [Google Scholar] [CrossRef] [Green Version]
- Kleemann, S.G.L.; Preston, C.; Gill, G.S. Influence of Management on Long-Term Seedbank Dynamics of Rigid Ryegrass (Lolium rigidum) in Cropping Systems of Southern Australia. Weed Sci. 2016, 64, 303–311. [Google Scholar] [CrossRef]
- Travlos, I.; Roussis, I.; Roditis, C.; Semini, C.; Rouvali, L.; Stasinopoulou, P.; Chimona, N.; Vlassopoulou, C.; Bilalis, D. Allelopathic Potential of Velvet Bean against Rigid Ryegrass. Not. Bot. Horti Agrobot. Cluj Napoca 2018, 46, 173–176. [Google Scholar] [CrossRef] [Green Version]
- IIes, A.; Marsh, R. Nurturing Diversified Farming Systems in Industrialized Countries: How Public Policy Can Contribute. Ecol. Soc. 2012, 17, 42. [Google Scholar]
- Kremen, C.; Iles, A.; Bacon, C. Diversified Farming Systems: An Agroecological, Systems-Based Alternative to Modern Industrial Agriculture. Ecol. Soc. 2012, 17. [Google Scholar] [CrossRef]
- Aare, A.K.; Egmose, J.; Lund, S.; Hauggaard-Nielsen, H. Opportunities and Barriers in Diversified Farming and the Use of Agroecological Principles in the Global North–The Experiences of Danish Biodynamic Farmers. Agroecol. Sustain. Food Syst. 2020, 45, 1–27. [Google Scholar] [CrossRef]
- Heal, G.; Walker, B.; Levin, S.; Arrow, K.; Dasgupta, P.; Daily, G.; Ehrlich, P.; Maler, K.-G.; Kautsky, N.; Lubchenco, J.; et al. Genetic Diversity and Interdependent Crop Choices in Agriculture. Res. Energy Econ. 2004, 26, 175–184. [Google Scholar] [CrossRef] [Green Version]
- Hendrickson, M.; Heffernan, W. Concentration of Agricultural Markets; Department of Rural Sociology, University of Missouri: Columbia, MO, USA, 2007. [Google Scholar]
- Buttel, F.H. Sustaining the Unsustainable: Agro-Food Systems and Environment in the Modern World; In Handbook of Rural Studies; Sage Pub.: London, UK, 2006; pp. 213–229. [Google Scholar]
- Boody, G.; Vondracek, B.; Andow, D.A.; Krinke, M.; Westra, J.; Zimmerman, J.; Welle, P. Multifunctional Agriculture in the United States. Bioscience 2005, 55, 27. [Google Scholar] [CrossRef] [Green Version]
- Thurston, H.D. Slash/Mulch Systems: Sustainable Methods for Tropical Agriculture; Westview: Boulder, CO, USA, 1997. [Google Scholar]
- Maxted, N.; Kell, S. Establishment of a Global Network for the in-Situ Conservation of Crop Wild Relatives: Status and Needs; FAO Commission on Genetic Resources for Food and Agriculture: Rome, Italy, 2009. [Google Scholar]
- Hajjar, R.; Hodgkin, T. The Use of Wild Relatives in Crop Improvement: A Survey of Developments over the Last 20 Years. Euphytica 2007, 156, 1–13. [Google Scholar] [CrossRef]
- Perrino, E.V.; Wagensommer, R.P.; Medagli, P. The Genus Aegilops (Poaceae) in Italy: Taxonomy, Geographical Distribution, Ecology, Vulnerability and Conservation. SYST BIODIVERS 2014, 12, 331–349. [Google Scholar] [CrossRef]
- Du, Q.; Chang, N.-B.; Yang, C.; Srilakshmi, K.R. Combination of Multispectral Remote Sensing, Variable Rate Technology and Environmental Modeling for Citrus Pest Management. J. Environ. Manag. 2008, 86, 14–26. [Google Scholar] [CrossRef] [PubMed]
- Ampatzidis, Y.; Partel, V.; Meyering, B.; Albrecht, U. Citrus Rootstock Evaluation Utilizing UAV-Based Remote Sensing and Artificial Intelligence. Comput. Electron. Agric. 2019, 164, 104900. [Google Scholar] [CrossRef]
- Kent Shannon, D.; Clay, D.E.; Sudduth, K.A. An Introduction to Precision Agriculture. In Precision Agriculture Basics; American Society of Agronomy and Soil Science Society of America: Madison, WI, USA, 2018; pp. 1–12. [Google Scholar]
- Brase, T. Basics of a Geographic Information System. In Precision Agriculture Basics; American Society of Agronomy and Soil Science Society of America: Madison, WI, USA, 2018; pp. 37–62. [Google Scholar]
- Mulla, D.J. Twenty-Five Years of Remote Sensing in Precision Agriculture: Key Advances and Remaining Knowledge Gaps. Biosyst. Eng. 2013, 114, 358–371. [Google Scholar] [CrossRef]
- Rokhmana, C.A. The Potential of UAV-Based Remote Sensing for Supporting Precision Agriculture in Indonesia. Procedia Environ. Sci. 2015, 24, 245–253. [Google Scholar] [CrossRef] [Green Version]
- Sapkota, B.; Singh, V.; Cope, D.; Valasek, J.; Bagavathiannan, M. Mapping and Estimating Weeds in Cotton Using Unmanned Aerial Systems-Borne Imagery. Agri. Eng. 2020, 2, 350–366. [Google Scholar] [CrossRef]
- Reynolds, D.; Baret, F.; Welcker, C.; Bostrom, A.; Ball, J.; Cellini, F.; Lorence, A.; Chawade, A.; Khafif, M.; Noshita, K.; et al. What Is Cost-Efficient Phenotyping? Optimizing Costs for Different Scenarios. Plant Sci. 2019, 282, 14–22. [Google Scholar] [CrossRef] [Green Version]
- Narvaez, F.Y.; Reina, G.; Torres-Torriti, M.; Kantor, G.; Cheein, F.A. A Survey of Ranging and Imaging Techniques for Precision Agriculture Phenotyping. IEEE/ASME Trans. Mechatron 2017, 22, 2428–2439. [Google Scholar] [CrossRef]
- Osco, L.P.; Ramos, A.P.M.; Faita Pinheiro, M.M.; Moriya, É.A.S.; Imai, N.N.; Estrabis, N.; Ianczyk, F.; de Araújo, F.F.; Liesenberg, V.; Jorge, L.A.d.C.; et al. A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements. Remote Sens. 2020, 12, 906. [Google Scholar] [CrossRef] [Green Version]
- Xu, R.; Li, C.; Paterson, A.H. Multispectral Imaging and Unmanned Aerial Systems for Cotton Plant Phenotyping. PLoS ONE 2019, 14, e0205083. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hassan, M.A.; Yang, M.; Rasheed, A.; Yang, G.; Reynolds, M.; Xia, X.; Xiao, Y.; He, Z. A Rapid Monitoring of NDVI across the Wheat Growth Cycle for Grain Yield Prediction Using a Multi-Spectral UAV Platform. Plant Sci. 2019, 282, 95–103. [Google Scholar] [CrossRef] [PubMed]
- Mirasi, A.; Mahmoudi, A.; Navid, H.; Valizadeh Kamran, K.; Asoodar, M.A. Evaluation of Sum-NDVI Values to Estimate Wheat Grain Yields Using Multi-Temporal Landsat OLI Data. Geocarto Int. 2019, 1–16. [Google Scholar] [CrossRef]
- Matese, A.; Di Gennaro, S.F.; Berton, A. Assessment of a Canopy Height Model (CHM) in a Vineyard Using UAV-Based Multispectral Imaging. Int. J. Remote Sens. 2017, 38, 2150–2160. [Google Scholar] [CrossRef]
- Costa, L.; Kunwar, S.; Ampatzidis, Y.; Albrecht, U. Estimating Leaf Nutrient Concentrations in Citrus Trees Using UAV Imagery and Gradient Boosting Decision Tree Regression. 2021; Unpublished Work. [Google Scholar]
- Shendryk, Y.; Sofonia, J.; Garrard, R.; Rist, Y.; Skocaj, D.; Thorburn, P. Fine-Scale Prediction of Biomass and Leaf Nitrogen Content in Sugarcane Using UAV LiDAR and Multispectral Imaging. ITC J. 2020, 92, 102177. [Google Scholar] [CrossRef]
- Cui, D.; Zhang, Q.; Li, M.; Hartman, G.L.; Zhao, Y. Image Processing Methods for Quantitatively Detecting Soybean Rust from Multispectral Images. Biosyst. Eng. 2010, 107, 186–193. [Google Scholar] [CrossRef]
- Garcia-Ruiz, F.; Sankaran, S.; Maja, J.M.; Lee, W.S.; Rasmussen, J.; Ehsani, R. Comparison of Two Aerial Imaging Platforms for Identification of Huanglongbing-Infected Citrus Trees. Comput. Electron. Agric. 2013, 91, 106–115. [Google Scholar] [CrossRef]
- Qin, Z.; Zhang, M. Detection of Rice Sheath Blight for In-Season Disease Management Using Multispectral Remote Sensing. ITC J. 2005, 7, 115–128. [Google Scholar] [CrossRef]
- Ondimu, S.; Murase, H. Water Stress Detection in Sunagoke Moss (Rhacomitrium canescens) Using Combined Thermal Infrared and Visible Light Imaging Techniques. Biosyst. Eng. 2008, 100, 4–13. [Google Scholar] [CrossRef]
- Partel, V.; Nunes, L.; Stansly, P.; Ampatzidis, Y. Automated Vision-Based System for Monitoring Asian Citrus Psyllid in Orchards Utilizing Artificial Intelligence. Comput. Electron. Agric. 2019, 162, 328–336. [Google Scholar] [CrossRef]
- Li, L.; Fan, Y.; Huang, X.; Tian, L. Real-Time UAV Weed Scout for Selective Weed Control by Adaptive Robust Control and Machine Learning Algorithm; American Society of Agricultural and Biological Engineers: St. Joseph, MI, USA, 2016. [Google Scholar]
- Laursen, M.; Jørgensen, R.; Midtiby, H.; Jensen, K.; Christiansen, M.; Giselsson, T.; Mortensen, A.; Jensen, P. Dicotyledon Weed Quantification Algorithm for Selective Herbicide Application in Maize Crops. Sensors 2016, 16, 1848. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ferguson, R.B.; Luck, J.D.; Stevens, R. Developing Prescriptive Soil Nutrient Maps. In Practical Mathematics for Precision Farming; American Society of Agronomy and Soil Science Society of America: Madison, WI, USA, 2018; pp. 149–166. [Google Scholar]
- Finger, R.; Swinton, S.M.; El Benni, N.; Walter, A. Precision Farming at the Nexus of Agricultural Production and the Environment. Annu. Rev. Resour. Econ. 2019, 11, 313–335. [Google Scholar] [CrossRef] [Green Version]
- Weersink, A.; Fraser, E.; Pannell, D.; Duncan, E.; Rotz, S. Opportunities and Challenges for Big Data in Agricultural and Environmental Analysis. Annu. Rev. Resour. Econ. 2018, 10, 19–37. [Google Scholar] [CrossRef]
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Sharma, G.; Shrestha, S.; Kunwar, S.; Tseng, T.-M. Crop Diversification for Improved Weed Management: A Review. Agriculture 2021, 11, 461. https://doi.org/10.3390/agriculture11050461
Sharma G, Shrestha S, Kunwar S, Tseng T-M. Crop Diversification for Improved Weed Management: A Review. Agriculture. 2021; 11(5):461. https://doi.org/10.3390/agriculture11050461
Chicago/Turabian StyleSharma, Gourav, Swati Shrestha, Sudip Kunwar, and Te-Ming Tseng. 2021. "Crop Diversification for Improved Weed Management: A Review" Agriculture 11, no. 5: 461. https://doi.org/10.3390/agriculture11050461
APA StyleSharma, G., Shrestha, S., Kunwar, S., & Tseng, T. -M. (2021). Crop Diversification for Improved Weed Management: A Review. Agriculture, 11(5), 461. https://doi.org/10.3390/agriculture11050461