Value of seasonal forecasting for sugarcane farm irrigation planning

An-Vo, Duc-Anh and Mushtaq, Shahbaz and Reardon-Smith, Kathryn and Kouadio, Louis and Attard, Steve and Cobon, David and Stone, Roger (2019) Value of seasonal forecasting for sugarcane farm irrigation planning. European Journal of Agronomy, 104. pp. 37-48. ISSN 1161-0301

Abstract

Seasonal climate forecasts (SCFs) have potential to improve productivity and profitability in the sugar industry. However, they are often underutilised due to insufficient evidence of the economic value of the forecasts, especially when there is a level of uncertainty associated with SCFs. Here, we demonstrate the value of integrating SCFs at various forecast quality (skill) levels into seasonal irrigation planning for sugarcane farming. A seasonal forecast system based on ENSO (El Niño Southern Oscillation) phases was parameterised by forecast quality to predict seasonal precipitation tercile (i.e. wet, neutral and dry) categories. A bio-economic model was developed to determine water-yield-profit relationships. Sugarcane production under different climatic conditions and irrigation scheduling scenarios was simulated using the Agricultural Production Systems sIMulator (APSIM)-Sugar, calibrated using case study information from one of Australia’s major irrigated sugarcane growing regions. We then employed an expected profit approach to achieve an optimal profit, rather than the more conventional optimal yield, for plant and ratoon crops to quantify the potential value of using SCFs in sugarcane irrigation decision making.

The results show that using skilled SCF systems in sugarcane irrigation decision making can help growers improve their gross margin compared to that achieved in the absence of climate information (economic value). With a perfect forecast of moderate climatic conditions, an average economic value of up to AUD 27 ha^−1 per annum was achieved, while forecasts of moderate wet or dry conditions indicated gains of up to AUD 40 and 43 ha^−1 per annum, respectively, and forecasts of extreme wet or dry conditions delivered economic gains of up to AUD 150 and 260 ha^−1 per annum, respectively. With the current seasonal climate forecast skill of 60% (based on the ENSO phases forecasting system) in the case study region, an average gain of up to AUD 4.5 ha^−1 per annum was realised, with up to AUD 6.2 and 7.1 ha^−1 per annum, respectively, for moderate wet and dry forecasting and up to AUD 92 and 43 ha^−1 per annum, respectively, for extreme wet and dry forecasting. Improvements in the skill and reliability of SCFs will be important for achieving greater productivity and/or profitability and the wider uptake of climate forecasts in agricultural decision making.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version, in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment - Centre for Applied Climate Sciences
Date Deposited: 20 Mar 2019 02:10
Last Modified: 13 Jun 2019 04:31
Uncontrolled Keywords: seasonal climate forecast, sugarcane, crop modelling, APSIM, bio-economic modelling, irrigation planning, decision support, value of information
Fields of Research : 14 Economics > 1402 Applied Economics > 140201 Agricultural Economics
07 Agricultural and Veterinary Sciences > 0701 Agriculture, Land and Farm Management > 070103 Agricultural Production Systems Simulation
01 Mathematical Sciences > 0103 Numerical and Computational Mathematics > 010303 Optimisation
Socio-Economic Objective: B Economic Development > 82 Plant Production and Plant Primary Products > 8203 Industrial Crops > 820304 Sugar
Identification Number or DOI: 10.1016/j.eja.2019.01.005
URI: http://eprints.usq.edu.au/id/eprint/35545

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