Farmer Decision-Making in Rainfed Farming Systems

Abstract

This chapter reviews how farmers in Australia gain information and make decisions about their rainfed farming systems. It examines the roles of consultants, farmer groups and decision support systems (DSS) in assisting farmers as their systems adjust in response to changes in their external environment. A specific DSS, Yield Prophet^®, is discussed in terms of its development in conjunction with two farming systems groups and their consultants.

Appendices

Appendix

Yield Prophet^® Explanation and Report Excerpts

This appendix is extracted from a page created by the lead author on the YPASG website.

Farmers or consultants subscribe to the service in late summer and autumn and provide the Yield Prophet® team with their field names and locations. During autumn, the soil is sampled at different depths to the maximum rooting depth of their crop (e.g. 0–10, 10–40, 40–70, 70–100 cm). These samples are analysed for water content, nitrate concentration, organic carbon content, electrical conductivity, chloride concentration and pH. These data are entered by growers into the Yield Prophet® web interface, and are also used by the grower and Yield Prophet® team to select a suitable soil characterisation (an essential input to simulate crop growth, yield and protein accurately).

During the season, subscribers enter paddock management details (sowing date, crop type, variety, nitrogen fertiliser and irrigation) and rainfall. When growers wish to find out how much water and nitrogen is currently available to a crop, the likely yield of their crop, or what the likely impact of management events will be, they generate a report. Some of the types of information provided by reports are shown below.

Yield Prophet® simulates daily crop growth from planting up to the report date using the paddock specific rainfall and management data entered by the subscriber, and climate data (maximum and minimum temperature, radiation, evaporation and vapour pressure) from the nominated weather station. At every daily time step Yield Prophet® calculates the amount of water and nitrogen available to the crop, and the water and nitrogen demand of the crop. This is used to determine if the crop is suffering stress from lack of either of these resources, and any subsequent reduction in growth and yield potential. This information is then presented to subscribers in reports returned to the subscribers’ account (Fig. 37.4).

Fig. 37.4

Output from Yield Prophet® indicating the amounts of water and nitrogen available to the crop during the season. The stress graphs indicate loss of potential growth and carbon fixation, i.e. on a day when the graph is at 0.5, the crop is growing and photosynthesising at half its potential rate, 1.0 indicates severe stress with limited growth

In order to make predictions about crop yield, Yield Prophet® uses the last one hundred years of climate data taken from the nearest Bureau of Meteorology weather station to continue the simulation from the date of report generation to the end of the season. The model simulates one hundred different crop yields and protein contents, based on the current season up until the day the report is generated, and then on the season finishes of the past one hundred years. These yields are then plotted as a probability curve, showing the probability of yields being equal to or greater than shown by the curve (Fig. 37.5 solid line).

Fig. 37.5

Yield probability curve generated using season finishes for the last 100 years of climate data (solid line), and only those years in which the SOI phase was the same as the current phase at the time the report was generated. In the above example (dotted line), this is the years with negative SOI phase in June–July; the report was generated in early August 2004

This is the main output of Yield Prophet®, and its value is increased by incorporating seasonal forecasts, such as the Southern Oscillation Index (SOI) phase system. That is, instead of using season finishes for the last 100 years, Yield Prophet® selects the years in which the SOI phase was the same as in the current year, and runs the future part of the simulation using only the finishes from those years. This creates another probability curve which growers can use if the SOI phase is strongly indicating wet or dry conditions (Fig. 37.5 broken line).

Yield Prophet® also allows scenario predictions. The likely impact of different sowing dates, varieties, nitrogen applications and irrigation can then be determined by simulating different ‘scenarios’. Yield Prophet® calculates a probability curve for each scenario, and subscribers use this to determine the probability of achieving or exceeding a yield response from the addition of nitrogen (Fig. 37.6) (or water).

Fig. 37.6

Yield probability curves for three different nitrogen top-dressing scenarios generated for a rainfed wheat crop on 1 August 2005. Scenario 1 (broken line) is the yield probability if no further N added, Scenario 2 (black line) is the yield probability with 35 kg/ha N top-dressed on 15 August, Scenario 3 (grey line) is the yield probability with 70 kg/ha N top-dressed on 15 August 2005. There is an 80% chance of achieving a minor yield response with topdressing, and about a 40% chance of achieving a 1 t/ha yield response from 35 kg/ha N. There is a 20% chance of achieving a 2 t/ha yield response to 70 kg/ha N

Yield Prophet® also can indicate likely yield from different sowing dates (Fig. 37.7) based on climate records, including the probabilities of damage from frost and heat stress, for any time of planting from 1 April to 1 July. The best planting time (sowing date) for maximum grain yield is then seen to be about mid May.

Fig. 37.7

Yield Prophet® Sowing opportunity report. Likely median yield is highest from sowing in mid May when frost risk is low and there is less than 20% risk of heat shock