Quantifying the costs of soil constraints to Australian agriculture: a case study of wheat in north-eastern Australia
Y. P. Dang A C and P. W. Moody B
+ Author Affiliations
- Author Affiliations
A School of Agriculture and Food Sciences, University of Queensland, Toowoomba, Qld 4350, Australia.
B Department of Science, Information Technology and Innovation, Dutton Park, Qld 4102, Australia.
C Corresponding author. Email: y.dang@uq.edu.au
Soil Research 54(6) 700-707 https://doi.org/10.1071/SR15007
Submitted: 14 January 2015 Accepted: 11 January 2016 Published: 25 July 2016
Abstract
Soil salinity, sodicity, acidity and alkalinity, elemental toxicities, such as boron, chloride and aluminium, and compaction are important soil constraints to agricultural sustainability in many soils of Australia. There is considerable variation in the existing information on the costs of each of the soil constraints to Australian agriculture. Determination of the cost of soil constraints requires measuring the magnitude and causes of yield gap (Yg) between yield potential and actual yield. We propose a ‘hybrid approach’ consisting of determining the magnitude of Yg and the cause(s) of Yg for spatiotemporal representation of Yg that can be apportioned between management and soil constraint effects, thereby allowing a better estimate of the cost of mitigation of the constraints. The principles of this approach are demonstrated using a 2820-ha wheat-growing farm over a 10-year period to quantify the costs of the proportion of forfeited Yg due to soil constraints. Estimated Yg over the whole farm varied annually from 0.6 to 2.4 Mg ha–1, with an average of 1.4 Mg ha–1. A multiyear spatiotemporal analysis of remote sensing data identified that 44% of the farm was consistently poor performing, suggesting the potential presence of at least one soil constraint. The percentage decrease in productivity due to soil constraints varied annually from 5% to 24%, with an average estimated annual loss of wheat grain production of 182 Mg per year on 1069 ha. With the 2015 season’s average wheat grain price (A$0.29 kg–1), the estimated annual value of lost agricultural production due to soil constraints was estimated at A$52 780 per year. For successful upscaling of the hybrid approach to regional or national scale, Australia has reliable data on the magnitude of Yg. The multiyear spatiotemporal analysis of remote sensing data would identify stable, consistently poor performing areas at a similar scale to Yg. Soil maps could then be used to identify the most-limiting soil constraints in the consistently poor performing areas. The spatial distribution of soil constraint at similar scale could be used to obtain the cost of lost production using soil constraint–grain yield models.
Additional keywords: acidity, compaction, elemental toxicity, multiyear remote sensing, salinity, sodicity, yield gap.
References
Alston JM, Beddow JM, Pardey PG (2009) Agricultural research, productivity, and food prices in the long run. Science 325, 1209–1210.| Agricultural research, productivity, and food prices in the long run.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXhtFeisrnP&md5=c876a203d3b8bec5837155b29afaa99fCAS | 19729642PubMed |
Anwar MR, O’Leary GJ, Rab MA, Fisher PD, Armstrong RD (2009) Advances in precision agriculture in south-eastern Australia. V. Effect of seasonal conditions on wheat and barley yield response to applied nitrogen across management zones. Crop and Pasture Science 60, 901–911.
| Advances in precision agriculture in south-eastern Australia. V. Effect of seasonal conditions on wheat and barley yield response to applied nitrogen across management zones.Crossref | GoogleScholarGoogle Scholar |
Armstrong RD, Fitzpatrick J, Rab MA, Abuzar M, Fisher PD, O’Leary GJ (2009) Advances in precision agriculture in south-eastern Australia. III. Interactions between soil properties and water use help explain spatial variability of crop production in the Victorian Mallee. Crop and Pasture Science 60, 870–884.
| Advances in precision agriculture in south-eastern Australia. III. Interactions between soil properties and water use help explain spatial variability of crop production in the Victorian Mallee.Crossref | GoogleScholarGoogle Scholar |
Bot AJ, Nachtergaele F, Young A (2000) Land resource potential and constraints at regional and country levels. In ‘World soil resources reports 90’. (Land and Water Development Division, Food and Agriculture Organization: Rome)
Cambardella CA, Karlen DL (1999) Spatial analysis of soil fertility parameters. Precision Agriculture 1, 5–14.
| Spatial analysis of soil fertility parameters.Crossref | GoogleScholarGoogle Scholar |
Dang YP, Dalal RC, Mayer DG, McDonald M, Routley R, Schwenke GD, Buck SR, Daniells IG, Singh DK, Manning W, Ferguson N (2008) High subsoil chloride concentrations reduce soil water extraction and crop yield on Vertosols in north-eastern Australia. Australian Journal of Agricultural Research 59, 321–330.
| High subsoil chloride concentrations reduce soil water extraction and crop yield on Vertosols in north-eastern Australia.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXktlehtL0%3D&md5=861c3bb4a324a85ca29f6ed584c19d8eCAS |
Dang YP, Dalal RC, Buck SR, Harms B, Kelly R, Hochman Z, Schwenke GD, Biggs AJW, Ferguson NJ, Norrish S, Routley R, McDonald M, Hall C, Singh DK, Daniells IG, Farquharson R, Manning W, Speirs S, Grewal HS, Cornish P, Bodapati N, Orange D (2010) Diagnosis, extent, impacts, and management of subsoil constraints in the northern grains cropping region of Australia. Australian Journal of Soil Research 48, 105–119.
| Diagnosis, extent, impacts, and management of subsoil constraints in the northern grains cropping region of Australia.Crossref | GoogleScholarGoogle Scholar |
Dang YP, Dalal RC, Pringle MJ, Biggs AJW, Darr S, Sauer B, Moss J, Payne J, Orange D (2011a) Electromagnetic induction sensing of soil identifies constraints to the crop yields of north-eastern Australia. Soil Research 49, 559–571.
| Electromagnetic induction sensing of soil identifies constraints to the crop yields of north-eastern Australia.Crossref | GoogleScholarGoogle Scholar |
Dang YP, Pringle MJ, Schmidt M, Dalal RC, Apan A (2011b) Identifying the spatial variability of soil constraints using multi-year remote sensing. Field Crops Research 123, 248–258.
| Identifying the spatial variability of soil constraints using multi-year remote sensing.Crossref | GoogleScholarGoogle Scholar |
Dawbin KW, Evans JC (1988) Large area crop classification in New South Wales, Australia, using landsat data. International Journal of Remote Sensing 9, 295–301.
| Large area crop classification in New South Wales, Australia, using landsat data.Crossref | GoogleScholarGoogle Scholar |
Dolling P, Moody P, Noble I, Helyar K, Hughes B, Reuter D, Sparrow L (2001) ‘Soil acidity and acidification in Australia.’ (National Land and Water Resources Audit Project Report: Canberra)
FAO (2013) ‘Wheat in the world.’ Available at http://www.fao.org/docrep/006/y4011e/y4011e04.htm [accessed 4 January 2016].
Fisher PD, Abuzar M, Rab MA, Best F, Chandra S (2009) Advances in precision agriculture in south-eastern Australia. I. A regression methodology to simulate spatial variation in cereal yields using farmers’ historical paddock yields and normalized difference vegetation index. Crop and Pasture Science 60, 844–858.
| Advances in precision agriculture in south-eastern Australia. I. A regression methodology to simulate spatial variation in cereal yields using farmers’ historical paddock yields and normalized difference vegetation index.Crossref | GoogleScholarGoogle Scholar |
Furby S, Caccetta P, Wallace J (2010) Salinity monitoring in Western Australia using remotely sensed and other spatial data. Journal of Environmental Quality 39, 16–25.
| Salinity monitoring in Western Australia using remotely sensed and other spatial data.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXpslKjtw%3D%3D&md5=9cbe25c11ba36025763f335fb49987efCAS | 20048290PubMed |
Hochman Z, Dang YP, Schwenke GD, Dalgliesh NP, Routley R, McDonald M, Daniells IG, Manning W, Poulton PL (2007) Simulating the effects of saline and sodic subsoils on wheat crops growing on Vertosols. Australian Journal of Agricultural Research 58, 802–810.
| Simulating the effects of saline and sodic subsoils on wheat crops growing on Vertosols.Crossref | GoogleScholarGoogle Scholar |
Hochman Z, Gobbett D, Holzworth D, McClelland T, van Rees H, Marinoni O, Garcia JN, Horan H (2012) Quantifying yield gaps in rainfed cropping systems: a case study of wheat in Australia. Field Crops Research 136, 85–96.
| Quantifying yield gaps in rainfed cropping systems: a case study of wheat in Australia.Crossref | GoogleScholarGoogle Scholar |
Holzworth DP, Huth NI, deVoil PG, Zurcher EJ, Herrmann NI, McLean G, Chenu K, van Oosterom EJ, Snow V, Murphy C, Moore AD, Brown H, Whish JPM, Verrall S, Fainges J, Bell LW, Peake AS, Poulton PL, Hochman Z, Thorburn PJ, Gaydon DS, Dalgliesh NP, Rodriguez D, Cox H, Chapman S, Doherty A, Teixeira E, Sharp J, Cichota R, Vogeler I, Li FY, Wang E, Hammer GL, Robertson MJ, Dimes JP, Whitbread AM, Hunt J, van Rees H, McClelland T, Carberry PS, Hargreaves JNG, MacLeod N, McDonald C, Harsdorf J, Wedgwood S, Keating BA (2014) APSIM: evolution towards a new generation of agricultural systems simulation. Environmental Modelling & Software 62, 327–350.
| APSIM: evolution towards a new generation of agricultural systems simulation.Crossref | GoogleScholarGoogle Scholar |
Jakubauskas ME, Legates DR, Kastens JH (2002) Crop identification using harmonic analysis of time-series AVHRR NDVI data. Computers and Electronics in Agriculture 37, 127–139.
| Crop identification using harmonic analysis of time-series AVHRR NDVI data.Crossref | GoogleScholarGoogle Scholar |
Jenny H (1941) ‘Factors of soil formation.’ (McGraw-Hill Book Company: New York, NY)
Kokic P, Davidson A, Rodriguez V (2006) Australia’s grains industry: factors influencing productivity growth. Australian Commodities 13, 705–712.
Lawes RA, Oliver YM, Robertson MJ (2009) Capturing the in-field spatial–temporal dynamic of yield variation. Crop and Pasture Science 60, 834–843.
| Capturing the in-field spatial–temporal dynamic of yield variation.Crossref | GoogleScholarGoogle Scholar |
Lawrence G, Richards C, Lyons K (2013) Food security in Australia in an era of neoliberalism, productivism and climate change. Journal of Rural Studies 29, 30–39.
| Food security in Australia in an era of neoliberalism, productivism and climate change.Crossref | GoogleScholarGoogle Scholar |
Lobell DB (2010) Remote sensing of soil degradation: introduction. Journal of Environmental Quality 39, 1–4.
| Remote sensing of soil degradation: introduction.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXpslKjsQ%3D%3D&md5=68ece307850a9ce86ba7517a476a5000CAS | 20048288PubMed |
Lobell DB (2013) The use of satellite data for crop yield gap analysis. Field Crops Research 143, 56–64.
| The use of satellite data for crop yield gap analysis.Crossref | GoogleScholarGoogle Scholar |
Lobell DB, Ortiz-Monasterio JI (2008) Satellite monitoring of yield responses to irrigation practices across thousands of fields. Agronomy Journal 100, 1005–1012.
| Satellite monitoring of yield responses to irrigation practices across thousands of fields.Crossref | GoogleScholarGoogle Scholar |
Lobell DB, Ortiz-Monasterio JI, Asner GP, Naylor RL, Falcon WP (2005) Combining field surveys, remote sensing, and regression trees to understand yield variations in an irrigated wheat landscape. Agronomy Journal 97, 241–249.
Lobell DB, Ortiz-Monasterio JI, Gurrola FC, Valenzuela L (2007) Identification of saline soils with multiyear remote sensing of crop yields. Soil Science Society of America Journal 71, 777–783.
| Identification of saline soils with multiyear remote sensing of crop yields.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXlvFGgt7Y%3D&md5=6aa03486ffd15093b276050890d06d11CAS |
Lobell DB, Cassman KG, Field CB (2009) Crop yield gaps: their importance, magnitudes, and causes. Annual Review of Environment and Resources 34, 179–204.
| Crop yield gaps: their importance, magnitudes, and causes.Crossref | GoogleScholarGoogle Scholar |
Maas E, Hoffman G (1977) Crop salt tolerance: current assessment. Journal of the Irrigation and Drainage Division 103, 115–134.
McBratney AB, Pringle MJ (1999) Estimating average and proportional variograms of soil properties and their potential use in precision agriculture. Precision Agriculture 1, 125–152.
| Estimating average and proportional variograms of soil properties and their potential use in precision agriculture.Crossref | GoogleScholarGoogle Scholar |
National Land and Water Resources Audit (NLWRA) (2001) ‘Australian agriculture assessment.’ (NLWRA: Canberra, ACT)
Northcote K, Skene J (1972) Australian soils with saline and sodic properties. CSIRO Soil Publication No. 27, CSIRO Divisions of Soils, Adelaide, SA.
Nuttall JG, Armstrong RD (2010) Impact of subsoil physicochemical constraints on crops grown in the Wimmera and Mallee is reduced during dry seasonal conditions. Australian Journal of Soil Research 48, 125–139.
| Impact of subsoil physicochemical constraints on crops grown in the Wimmera and Mallee is reduced during dry seasonal conditions.Crossref | GoogleScholarGoogle Scholar |
Nuttall JG, Armstrong RD, Connor DJ (2003) Evaluating physicochemical constraints of Calcarosols on wheat yield in the Victorian southern Mallee. Australian Journal of Agricultural Research 54, 487–497.
| Evaluating physicochemical constraints of Calcarosols on wheat yield in the Victorian southern Mallee.Crossref | GoogleScholarGoogle Scholar |
Ortiz-Monasterio JI, Lobell DB (2007) Remote sensing assessment of regional yield losses due to sub-optimal planting dates and fallow period weed management. Field Crops Research 101, 80–87.
| Remote sensing assessment of regional yield losses due to sub-optimal planting dates and fallow period weed management.Crossref | GoogleScholarGoogle Scholar |
Portmann FT, Siebert S, Doll P (2010) MIRCA2000: global monthly irrigated and rainfed crop areas around the year 2000: a new high-resolution data set for agricultural and hydrological modelling. Global Biogeochemistry Cycles 24, GB1011.
| MIRCA2000: global monthly irrigated and rainfed crop areas around the year 2000: a new high-resolution data set for agricultural and hydrological modelling.Crossref | GoogleScholarGoogle Scholar |
Potgieter AB, Apan A, Dunn P, Hammer G (2007) Estimating crop area using seasonal time series of enhanced vegetation index from MODIS satellite imagery. Australian Journal of Agricultural Research 58, 316–325.
| Estimating crop area using seasonal time series of enhanced vegetation index from MODIS satellite imagery.Crossref | GoogleScholarGoogle Scholar |
Rayment GE, Lyons DJ (2011) ‘Soil chemical methods: Australasia.’ (CSIRO Publishing: Melbourne)
Rengasamy P (2002) Transient salinity and subsoil constraints to dryland farming in Australian sodic soils: an overview. Australian Journal of Experimental Agriculture 42, 351–361.
| Transient salinity and subsoil constraints to dryland farming in Australian sodic soils: an overview.Crossref | GoogleScholarGoogle Scholar |
Rengasamy P, McKenzie D, Armstrong R, Barrett-Lenard G, Bennett J, Dalal R, Dang Y, Davenport D, Fisher P, Hall D, Kessel D, Kopittke P, Marchuk A, Menzies N, Naidu N, Rab A, Raine S, Rao R, Riethmuller G, Sauer B, Tavakkoli E, Wehr B, Wilhelm N (2015) ‘Dispersive (sodic and magnesic) soils in Australia: the mechanisms, distribution and management. GRDC Project: DAQ00200 soil constraints initiative: management of sodic and magnesic or dispersive soils.’ (Queensland Department of Science, Information Technology and Innovation: Brisbane, Qld)
Robertson M, Isbister B, Maling I, Oliver Y, Wong M, Adams M, Bowden B, Tozer P (2007) Opportunities and constraints for managing within-field spatial variability in Western Australian grain production. Field Crops Research 104, 60–67.
| Opportunities and constraints for managing within-field spatial variability in Western Australian grain production.Crossref | GoogleScholarGoogle Scholar |
Seifert C, Ortiz-Monasterio JI, Lobell DB (2011) Satellite-based detection of salinity and sodicity impacts on wheat production in the Mexicali Valley. Soil Science Society of America Journal 75, 699–707.
| Satellite-based detection of salinity and sodicity impacts on wheat production in the Mexicali Valley.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXksFOksL8%3D&md5=34062c3a890852d12affb6f19747cb8eCAS |
Tang C, Rengel Z, Diatloff E, Gazey C (2003) Responses of wheat and barley to liming on a sandy soil with subsoil acidity. Field Crops Research 80, 235–244.
| Responses of wheat and barley to liming on a sandy soil with subsoil acidity.Crossref | GoogleScholarGoogle Scholar |
van Ittersum MK, Cassman KG, Grassini P, Wolf J, Tittonell P, Hochman Z (2013) Yield gap analysis with local to global relevance: a review. Field Crops Research 143, 4–17.
| Yield gap analysis with local to global relevance: a review.Crossref | GoogleScholarGoogle Scholar |
