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Estimating the farm-level economic costs of spring cropping to manage Alopecurus myosuroides (black-grass) in UK agriculture

Published online by Cambridge University Press:  09 October 2019

K. Ahodo ,
D. Oglethorpe ,
H. L. Hicks  and
R. P. Freckleton Open the ORCID record for R. P. Freckleton [Opens in a new window]
K. Ahodo
Affiliation:
Department of Animal and Plant Sciences, The University of Sheffield, Western Bank, Sheffield S10 2TN, UK
D. Oglethorpe
Affiliation:
Cranfield School of Management, Cranfield University, Bedford MK43 0AL, UK
H. L. Hicks
Affiliation:
School of Animal, Rural & Environmental Sciences, Nottingham Trent University, Nottingham NG1 4FQ, UK
R. P. Freckleton*
Affiliation:
Department of Animal and Plant Sciences, The University of Sheffield, Western Bank, Sheffield S10 2TN, UK
*
Author for correspondence: R. P. Freckleton, E-mail: r.freckleton@sheffield.ac.uk
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Abstract

Crop rotation is a non-chemical strategy adopted by farmers to manage weeds. However, not all crops in a rotation are equally profitable. Thus, there is potentially a trade-off between the costs and benefits of this strategy. The objective of the current study is to quantify this trade-off for the rotational control of an important weed (Alopecurus myosuroides). Data from 745 farms were used to parameterize a farm-level mixed-integer goal-programming model of the economics of spring cropping for weed control in UK agriculture. On average, the short-term loss of profit from spring cropping is greater than the benefits in terms of reduced herbicide usage and yield increases. These costs are greater when weed densities are low, so that spring cropping is an expensive strategy in the early stages of an infestation. However, there is a great deal of farm-to-farm variation: factors such as soil type and farm size are important and the current study highlights that economic modelling at the farm level is important in enabling farmers to make informed decisions. In general, however, if spring cropping is to be a successful strategy then the benefits to farmers will be in terms of long-term reductions in weed densities, but this will be at the expense of short-term profitability.

Keywords

Black-grassgoal programmingspring croppingUK arable farming systemsweed management

Information

Type
Crops and Soils Research Paper
Information
The Journal of Agricultural Science , Volume 157 , Issue 4 , May 2019 , pp. 318 - 332
Copyright
Copyright © Cambridge University Press 2019 

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References

ABC (2014) The Agricultural Budgeting & Costing Book, 78th Edn. Melton Mowbray, UK: Agro Business Consultants.Google Scholar
ADAS (2007) The Potential for Increasing the Area of Spring Cropping to Enhance Biodiversity (IFO130). London, UK: Defra. Available at http://randd.defra.gov.uk/Document.aspx?Document=IF0130_7250_FRA.pdf (Accessed 4 September 2019).Google Scholar
Ahuja, DB, Ahuja, UR, Singh, SK and Singh, N (2015) Comparison of Integrated Pest Management approaches and conventional (non-IPM) practices in late-winter-season cauliflower in Northern India. Crop Protection 78, 232–238.Google Scholar
Annetts, JE and Audsley, E (2002) Multiple objective linear programming for environmental farm planning. Journal of Operational Research Society 53, 933–943.Google Scholar
Barnard, CS and Nix, JS (1973) Farm Planning and Control. Cambridge, UK: Cambridge University Press.Google Scholar
Bayer (2015) Bayer Expert Guide: Black-Grass Management in Winter Cereals. Leverkusen, Germany: Bayer AG.Google Scholar
Beltran, JC, Pannell, DJ, Doole, GJ and White, B (2012) A bioeconomic model for analysis of integrated weed management strategies for annual barnyardgrass (Echinochloa crus-galli complex) in Philippine rice farming systems. Agricultural Systems 112, 1–10.Google Scholar
Bottrell, DG and Schoenly, KG (2018) Integrated pest management for resource-limited farmers: challenges for achieving ecological, social and economic sustainability. The Journal of Agricultural Science 156, 408–426.Google Scholar
Buckwell, AE and Hazell, PBR (1972) Implications of aggregation bias for the construction of static and dynamic linear programming supply models. Journal of Agricultural Economics 23, 119–134.Google Scholar
Chamen, WCT and Audsley, E (1993) A study of the comparative economics of conventional and zero traffic systems for arable crops. Soil and Tillage Research 25, 369–396.Google Scholar
Chauvel, B, Guillemin, JP, Colbach, N and Gasquez, J (2001) Evaluation of cropping systems for management of herbicide-resistant populations of blackgrass (Alopecurus myosuroides Huds.). Crop Protection 20, 127–137.Google Scholar
Chauvel, B, Guillemin, JP and Letouze, A (2005) Effect of intra-specific competition on development and growth of Alopecurus myosuroides Hudson. European Journal of Agronomy 22, 301–308.Google Scholar
Chikowo, R, Faloya, V, Petit, S and Munier-Jolain, NM (2009) Integrated Weed Management systems allow reduced reliance on herbicides and long-term weed control. Agriculture, Ecosystems & Environment 132, 237–242.Google Scholar
Colbach, N, Roger-Estrade, J, Chauvel, B and Caneill, J (2000) Modelling vertical and lateral seed bank movements during mouldboard ploughing. European Journal of Agronomy 13, 111–124.Google Scholar
Colbach, N, Kurstjens, DAG, Munier-Jolain, NM, Dalbiès, A and Doré, T (2010) Assessing non-chemical weeding strategies through mechanistic modelling of black-grass (Alopecurus myosuroides Huds.) dynamics. European Journal of Agronomy 32, 205–218.Google Scholar
Cooke, IR, Mattison, EHA, Audsley, E, Bailey, AP, Freckleton, RP, Graves, AR, Morris, J, Queenborough, SA, Sandars, DL, Siriwardena, GM, Trawick, P, Watkinson, AR and Sutherland, WJ (2013) Empirical test of an agricultural landscape model: the importance of farmer preference for risk aversion and crop complexity. SAGE Open 3, 1–16. https://doi.org/10.1177/2158244013486491.Google Scholar
Defra (2010) Fertilizer Manual (RB209). London, UK: The Stationery Office.Google Scholar
Dury, J, Schaller, N, Garcia, F, Reynaud, A and Bergez, JE (2012) Models to support cropping plan and crop rotation decisions: a review. Agronomy for Sustainable Development 32, 567–580.Google Scholar
Freckleton, RP, Hicks, HL, Comont, D, Crook, L, Hull, R, Neve, P and Childs, DZ (2018) Measuring the effectiveness of management interventions at regional scales by integrating ecological monitoring and modelling. Pest Management Science 74, 2287–2295.Google Scholar
Hanley, N and Oglethorpe, D (1999) Emerging policies on externalities from agriculture: an analysis for the European Union. American Journal of Agricultural Economics 81, 1222–1227.Google Scholar
Hanley, N, Kirkpatrick, H, Simpson, I and Oglethorpe, D (1998) Principles for the provision of public goods from agriculture: modeling moorland conservation in Scotland. Land Economics 74, 102–113.Google Scholar
Hazell, PBR (1971) A linear alternative to quadratic and semivariance programming for farm planning under uncertainty. American Journal of Agricultural Economics 53, 53–62.Google Scholar
HGCA (2008) Herbicide-resistant Black-grass: Managing Risk with Fewer Options. Information Sheet no. 3. London, UK: HGCA. Available at http://cereals.ahdb.org.uk/media/185447/is03-herbicide-resistant-black-grass-managing-risk-with-fewer-options.pdf (Accessed 5 August 2019).Google Scholar
HGCA (2014 a) Managing Weeds in Arable Rotations – A Guide. London, UK: HGCA. Available at https://cereals.ahdb.org.uk/media/433546/g61-managing-weeds-in-arable-rotations-a-guide.pdf (Accessed 5 August 2019).Google Scholar
HGCA (2014 b) Black-grass: Solutions to the Problem. Information Sheet no. 30. London, UK: HGCA. Available at https://cereals.ahdb.org.uk/media/433525/is30-black-grass-solutions-to-the-problem.pdf (Accessed 5 August 2019).Google Scholar
Hicks, HL, Comont, D, Coutts, SR, Crook, L, Hull, R, Norris, K, Neve, P, Childs, DZ and Freckleton, RP (2018) The factors driving evolved herbicide resistance at a national scale. Nature Ecology & Evolution 2, 529–536.Google Scholar
Hillocks, RJ (2012) Farming with fewer pesticides: EU pesticide review and resulting challenges for UK agriculture. Crop Protection 31, 85–93.Google Scholar
Keshtkar, E, Mathiassen, SK, Moss, SR and Kudsk, P (2015) Resistance profile of herbicide-resistant Alopecurus myosuroides (black-grass) populations in Denmark. Crop Protection 69, 83–89.Google Scholar
Lechenet, M, Deytieux, V, Antichi, D, Aubertot, J, Bàrberi, P, Bertrand, M, Cellier, V, Charles, R, Colnenne-David, C, Dachbrodt-Saaydeh, S, Debaeke, P, Doré, T, Farcy, P, Fernandez-Quintanilla, C, Grandeau, G, Hawes, C, Jouy, L, Justes, E, Kierzek, R, Kudsk, P, Lamichhane, JR, Lescourret, F, Mazzoncini, M, Melander, B, Messéan, A, Moonen, A, Newton, AC, Nolot, J, Panozzo, S, Retaureau, P, Sattin, M, Schwarz, J, Toqué, C, Vasileiadis, VP and Munier-Jolain, N (2017) Diversity of methodologies to experiment Integrated Pest Management in arable cropping systems: analysis and reflections based on a European network. European Journal of Agronomy 83, 86–99.Google Scholar
Lucas, JA (2011) Advances in plant disease and pest management. Journal of Agricultural Science, Cambridge 149(Suppl. 1), 91–114.Google Scholar
Lutman, PW, Moss, SR, Cook, S and Welham, SJ (2013) A review of the effects of crop agronomy on the management of Alopecurus myosuroides. Weed Research 53, 299–313.Google Scholar
McCarl, BA and Spreen, TH (1997) Applied Mathematical Programming Using Algebraic Systems. College Station, TX, USA: McCarl and Spreen. Available at http://agecon2.tamu.edu/people/faculty/mccarl-bruce/books.htm (Accessed 5 August 2019).Google Scholar
Metcalfe, H, Milne, AE, Webster, R, Lark, RM, Murdoch, AJ, Kanelo, L and Storkey, J (2018) Defining the habitat niche of Alopecurus myosuroides at the field scale. Weed Research 58, 165–176.Google Scholar
Met Office (2016) Climate and Climate Change: UK and Regional Series. Exeter, UK: The Met Office. Available at http://www.metoffice.gov.uk/climate/uk/summaries/datasets (Accessed 5 August 2019).Google Scholar
Moss, SR and Hull, R (2012) Quantifying the benefits of spring cropping for control of Alopecurus myosuroides (black-grass). Aspects of Applied Biology 117, 1–6.Google Scholar
Moss, S and Lutman, P (2013) Black-grass: The Potential of Non-chemical Control. Rothamsted Technical Publication, 2013. Herpenden, Herts, UK: Rothamsted Research. Available at https://www.agricology.co.uk/sites/default/files/Blackgrass%20non-chemical%20control%2028May13.pdf (Accessed 5 August 2019).Google Scholar
Moss, SR, Perryman, SAM and Tatnell, LV (2007) Managing herbicide-resistant blackgrass (Alopecurus myosuroides): theory and practice. Weed Technology 21, 300–309.Google Scholar
Nix, J (2014) Farm Management Pocketbook, 44 Edn. Melton Mowbray, England: Agro Business Consultants Ltd.Google Scholar
Önal, H and McCarl, BA (1989) Aggregation of heterogeneous firms in mathematical programming models. European Review of Agricultural Economics 16, 499–513.Google Scholar
Pannell, DJ, Stewart, V, Bennett, A, Monjardino, M, Schmidt, C and Powles, SB (2004) RIM: a bioeconomic model for integrated weed management of Lolium rigidum in Western Australia. Agricultural Systems 79, 305–325.Google Scholar
Pardo, G, Riravololona, M and Munier-Jolain, NM (2010) Using a farming system model to evaluate cropping system prototypes: are labour constraints and economic performances hampering the adoption of Integrated Weed Management? European Journal of Agronomy 33, 24–32.Google Scholar
Queenborough, SA, Burnet, KM, Sutherland, WJ, Watkinson, AR and Freckleton, RP (2011) From meso- to macroscale population dynamics: a new density-structured approach. Methods in Ecology and Evolution 2, 289–302.Google Scholar
R Core Team (2015) R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
Rounsevell, MDA, Annetts, JE, Audsley, E, Mayr, T and Reginster, I (2003) Modelling the spatial distribution of agricultural land use at the regional scale. Agriculture, Ecosystem & Environment 95, 465–479.Google Scholar
Schönhart, M, Schmid, E and Schneider, UA (2011) Croprota – a crop rotation model to support integrated land use assessments. European Journal of Agronomy 34, 263–277.Google Scholar
Sells, JE (1995) Optimising weed management using stochastic dynamic programming to take account of uncertain herbicide performance. Agricultural Systems 48, 271–296.Google Scholar
Soilscapes (2016) Soilscapes Map. Cranfield, UK: Cranfield Soil and AgriFood Institute. Available at http://www.landis.org.uk/soilscapes/ (Accessed 5 August 2019).Google Scholar
Swinton, SM and King, RP (1994) The value of pest information in a dynamic setting: the case of weed control. American Journal of Agricultural Economics 76, 36–46.Google Scholar
Tillett, ND and Audsley, E (1987) The potential economic benefits of gantries for leaf vegetable production. Journal of Agricultural Engineering Research 36, 31–44.Google Scholar
Toosey, RD (1988) Arable crops. In Halley, RJ and Soffe, RJ (eds.), The Agricultural Notebook, 18 Edn. London, England: Butterworths, pp. 90–176.Google Scholar