Elsevier

Biosystems Engineering

Volume 105, Issue 2, February 2010, Pages 198-204
Biosystems Engineering

Research Paper
Farm-scale zoning of extreme temperatures in Southern Mallee, Victoria, Australia

https://doi.org/10.1016/j.biosystemseng.2009.10.008Get rights and content

Extreme temperatures around the flowering of wheat have the potential to reduce grain yield and at farm scale their impact can be spatially variable. In this study, the zoning of extreme temperatures, using data collected over two years, was carried out for a 164 ha farm in the Southern Mallee, Victoria, Australia in order to identify areas at high risk of extreme temperatures around the time of the flowering of wheat. Twenty-five data loggers were installed at 0.8 m height across the farm to spatially record the daily course of temperatures around the average date of flowering for the region. After applying the zoning algorithms, the maps of different temperature zones were produced by spatial interpolation in ArcView 3.2. It was found that in 2003, about 58% of the farm area was prone to exposure to higher temperatures and about 73% to the lower temperatures whereas in 2004 about 46% of the farm area was prone to exposure to higher temperatures and about 39% to the lower temperatures.

Introduction

Farm-scale spatial variability in microclimate, notably extreme temperatures, is the major factor responsible for reduction in grain yield of wheat crop in Southern Mallee, Victoria, Australia (Cawood, 1996) and in most of Australia (Potgieter et al., 2002). Extreme temperatures can have severe consequences for crops and significantly reduce yields (Porter and Gawith, 1999). Each year considerable yield losses in wheat occur globally due to untimely frosts at flowering time (Maes et al., 2001). Yield losses in Victoria due to frost can vary from 5% to 50% depending on timing and temperature reached (Cawood and McDonald, 1996). Both high and low temperatures decrease the rate of dry matter production and, in the extreme, can cause production to cease (Grace, 1988).

The time of flowering of wheat (Single, 1961) and many crop plants (Wheeler et al., 2000) is sensitive to extremes of temperature and for maximum yield, flowering should occur after the last damaging frost (Fischer, 1979). Planting of crops at a time when the risk of frost during flowering has diminished to an acceptable level is the best approach for the growers in Australia (Martin, 2002). Exposure to low temperatures during flowering of wheat can reduce grain yields through the production of infertile florets and frost damage. Reproductive tissues of the developing wheat ear are extremely susceptible to damage by freezing (Single and Marcellos, 1974) and damage from severe frosts during the critical flowering and grain filling stages causes severe economic loss to winter crop producers (Kelleher et al., 2001). Temperatures as high as 9.5 °C, for a few days around flowering, can produce infertile florets (Slafer and Slavin, 1991, Russell and Wilson, 1994). Similarly, temperatures above 31 °C immediately before flowering have the potential to reduce grain yield by inducing pollen sterility, thereby reducing grain numbers (Wheeler et al., 1996). Brief episodes of hot temperatures at the time of flowering can reduce the potential number of seeds or grains that subsequently contribute to the crop yield (Wheeler et al., 2000). Wheat is vulnerable to high temperature during its most reproductive stages (Nicolas et al., 1984, Wardlaw et al., 1989, Tashiro and Wardlaw, 1990a, Tashiro and Wardlaw, 1990b), and kernel number, kernel weight, or both can be diminished (Gibson and Paulsen, 1999).

At farm scale, the combination of local variations in elevation, aspect and slope cause variations in temperature and frost incidence in the landscape (Kelleher et al., 2001) even with little variation in topographic relief (Kalma, 1984). Differences in elevation of only 1 m can allow cold-air drainage down slopes and the formation of the frost pockets. The coldest temperatures are generally associated with the low-lying areas and studies show that temperature variations have a distinct relation to the atmospheric circulation (Tveito, 2002). Aspect is associated with differences in relative radiation load, while relative slope position is associated with airflow effects such as cold-air drainage (Lookingbill and Urban, 2003). Hocevar and Martsolf (1971) related the occurrence of minimum temperatures to elevation in the landscape during frosty nights. They reported that the minimum temperatures in complex terrain are influenced by the temperature of the well mixed air stream, measured on an exposed hilltop, and effects controlled by the terrain such as katabatic flows and stagnation of cold air. In many cases elevation alone can explain up to 85% of the spatial variation in minimum temperatures in the landscape on a particular day (Fitzpatrick and Laughlin, 1981). Air temperature close to the ground is a factor in plant growth (Hudson and Wackernagel, 1994) and is extremely variable in space and time, depending on numerous environmental factors such as solar radiation, elevation, aspect, distance from the sea, shape of the valley and presence of water bodies (Petkov et al., 1996).

Thus, the variation in microclimate within the farm can explain variation in grain yield (Cawood, 1996, Tveito, 2002). This study aims to identify different zones of extreme temperatures within the farm which may be treated as separate units to practice different management in order to minimise losses due to crop exposure to extreme temperatures around the time of flowering.

Section snippets

Study area

The study was conducted in a 164 ha farm (35.78°S, 142.98°E), 25 km north of Birchip in Southern Mallee, Victoria, Australia. The farm has approximately 10 m variation in elevation. Soils are Epihypersodic Hypercalic Calcarosols (Isbell, 1996). A digital elevation map (Fig. 1) of the farm was developed at 10 m × 10 m resolution using ArcView 3.2 with Spatial Analyst (ESRI, 1996) and shows the variation in elevation across the farm.

Experimental setup

Tinytag temperature data loggers (TG-0050, Gemini Data Loggers (UK)

Results and discussion

The difference between threshold values and the average maximum and minimum temperatures and standard deviation were calculated by subtracting the average value in each zone (based on the number of data loggers in that zone) from the threshold value for that zone for both maximum and minimum temperatures and the standard deviation (Table 2). In 2003, no values were obtained for the variable high temperature zone in the case of minimum temperature; hence there is little of this zone in Fig. 4a.

Conclusions

Maps of zones of consistently high and low temperatures, such as found in this study, may be used in precision agriculture to create management zones according to their risk of frost and heat stress. The zoning exercise proposed a simple and efficient way to identify different zones of extreme temperatures across the farm by observing the consistency of temperature variation over the time and space. Although the temperatures during the period of observation were not extreme, the information is

Acknowledgement

The financial support provided by the University of Melbourne in the form of Melbourne International Research Fee Remission Scholarship and Melbourne International Research Scholarship is greatly acknowledged. The help extended by the Department of Primary Industries, Horsham and Birchip Cropping Group is duly acknowledged. In particular, the author is very grateful to Dr. Daniel Rodriguez and Dr. John Angus for their constructive suggestions.

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