• Hochman, Zvi
• Mcclelland, Tim
• Holzworth, Dean
• Horan, Heidi
• Navarro Garcia, Javier
• Marinoni, Oswald
• Van Rees, Harm

Abstract

To feed a growing world population in the coming decades, agriculture must strive to reduce the gap between the yields that are currently achieved by farmers (Ya) and those potentially attainable in rainfed farming systems (Yw).The first step towards reducing yield gaps (Yg) is to obtain realistic estimates of their magnitude and their spatial and temporal variability. In this paper we describe a new yield gap assessment framework. The framework uses statistical yield and cropping area data, remotely sensed data, cropping system simulation and GIS mapping to calculate wheat yield gaps at scales from 1.1 km cells to regional. The framework includes ad hoc on-ground testing of the calculated yield gaps. The framework was applied to wheat in the Wimmera region of Victoria, Australia, a region with considerable spatial and temporal variability. The estimated yield gap over the whole Wimmera region varied annually from 0.63 to 4.12 Mg ha-1with an average of 2.00 Mg ha-1. Expressed as a relative yield (Y%) the range was 26.3 % to 77.9 % with an average gap of 52.7 %. Similarly large spatial variability in the Wimmera was described in yield gap maps. Such maps can be used to show where efforts to bridge the yield gap are likely to have the biggest impacts. Bridging the exploitable yield gap by increasing average Y% to 75% would increase average annual wheat production in the Wimmera region from 1.09 M tonnes to 1.55 M tonnes. The proposed framework provided a robust and widely applicable method of determining yield gaps at a regional scale. Its successful implementation requires that a number of conditions be satisfied: 1. the area and geospatial distribution of wheat cropping is well defined; 2. there is good coverage throughout the area of daily weather data and of soil properties data (such as PAWC) required by crop models; 3. local agronomic best practice is well defined; and 4. There is a crop model with proven performance in the local agro-ecological zone.

Topics

• impedance spectroscopy
• simulation