There are few plants that are unaffected by the weather. In humankind’s quest for an adequate supply of food, it is particularly important that the climatic factors affecting yield and quality are understood. Although growers can implement good management practices for pollination (Abu-Zahra and Al-Abbadi, 2007) and irrigation (Ak and Agackesen, 2005) to maximize crop load (Boler, 1998), trees still produce different fruit or nut sizes and quality in different years. This is a result, in part, of seasonally different weather (particularly temperature) during those years. Our knowledge of the physiology and biochemistry of the plants may guide us on how to approach major climate influences (Barlow and Cumming, 1975; Beattie and Folley, 1978; Jackson and Hamer, 1980). However, it is difficult to identify important growth periods when weather can have a large influence; but, attempts have been made. For example, studies of apple (Malus domestica) have shown that autumn temperatures and sunlight hours can affect the yield the following season (Beattie and Folley, 1977; Jackson et al., 1983; Lakso 1987). Kallsen (2017) used multiple regression to explore the effects of air temperature on pistachio yields. He used more than 10 different periods with different air temperatures to correlate aspects of weather to yield. He found that temperatures in the northern hemisphere from 15 Nov. to 15 Feb. greater than 18.3 °C and during bloom from 20 Mar. to 20 Apr. greater than 26.7 °C correlated with lower yields. Obviously, our knowledge of the response of crops to weather factors is far from complete. This is the case in pistachio (Pistacia vera).
To predict plant responses usefully in such things as fruit size, maturity date, nut quality, and so on, growers not only need to know which climatic factors are important, but also they need to know over which period(s) any specific aspect of the environment is a key driver.
A few studies describe how to find the key periods of weather that influence fruit/nut production behavior. Goldwin (1982), working with apple, used daily temperature recordings during the dormancy period through to harvest time to calculate all r values (correlation coefficients) between temperature over any 2 d during that period and crop yield. He plotted a three-dimensional graph showing the peaks and troughs of r values against different periods. He found that the average maximum daily temperature starting 99 d after full bloom over a 45-d period and the average minimum daily temperature starting 79 d before full bloom over a 46-d period had major influences on yield. Data presentation in studies like this can be difficult. Three-dimensional graphs show patterns of r values changing, without allowing the reader to understand the r values. To find the key period influencing ‘Royal Gala’ apple size, Zhang and Thiele (1992) created r contour maps that showed the actual r values. They collected and summarized data relating monthly mean maximum temperature and apple fruit size from flower bud initiation until apple harvest over an 18-month period (171 month-combinations). They found that maximum temperature between December and January in New Zealand (equivalent to June and July in the northern hemisphere, the fruit enlargement stage) had a strong influence on ‘Royal Gala’ apple size.
In two-dimensional r contour maps such as those presented here (see Table 1), the third dimension is provided through the use of color. The choice of color is based on the common use in geographical maps, where latitude and longitude are represented by the x- and y-axes, and differences in altitude are denoted by different colors. For example, lower nonsignificant r values are green, similar to the lower altitudes in mapping, progressing through yellow (indicating a 5% significant level of positive correlation) to brown (indicating a 1% significant level of positive correlation), equivalent to high-level altitudes in mapping. Blue indicates negative r values, with light blue being the 5% significant level of negative correlation and dark blue being the 1% significant level of negative correlation (Zhang, 1993).
r contour map for correlations between the percentages of blank nuts of pistachio assessed before harvest each year and average daily minimum temperature from the Mildura, Australia, meteorological station from April to March of the following year (equivalent to October to September in the northern hemisphere, or the whole year). Each cell represents temperature data averaged over 1 month or more. If there are filled cells to the left of a particular cell, the independent variable for that cell is the average of the minimum temperature for that month together with the data from the months to the left of the cell. In the cells, green represents no significance, yellow shows significance at P < 0.05, and orange shows significance at P < 0.01.
Although Goldwin (1982) proposed the use of daily temperature values, there are too many data from dormancy period to fruit harvest for this to be a workable technique. With more than 250 d and 10,000 combination, a three-dimensional graph cannot provide accurate information for each r value. The technique of r contour mapping provides us with the opportunity to view the correlation details over different time intervals. Zhang (1993) further explored r contour mapping, focusing on daily records, but only during key periods identified using temperature data averaged over longer periods (e.g., month by month). This technique has also been used for the prediction of nut size in ‘Sirora’ pistachio (Zhang and Joyce, 2011).
In Australia, many aspects of pistachio production, including bud burst and nut quality, are influenced by aspects of weather, and each relationship needs to be understood separately. We have documented chill requirements (Zhang and Taylor, 2011) and heat requirements (Zhang et al., 2015). This study examined the effect of maximum and minimum air temperature over the previous 12 months on a range of nut quality disorders (the percentages of blank nuts, narrow-split nuts, and damaged-shell nuts) at harvest time over 16 years. Verification is provided by applying the models obtained to data collected in the subsequent 5 years.
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