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Apple Orchard Effects on Weather Variables

by Karisma Yumnam, Lav Khot

Growers use weather data such as air temperature, relative humidity, wind speed, and solar radiation to make key orchard management decisions. Decision support models/tools for frost/ heat stress mitigation, irrigation scheduling and pest management are driven by open-field weather data and may not account for varying orchard microclimates throughout the year. This article provides information about orchard effects on weather variables in sites with Solaxe, Bi-axis, and V-trellis training systems (Fig. 1). Weather data from six apple orchards collected over two years using paired open-field and in-orchard station was used to study these effects.

Drawings of three trees showing three different training systems
Fig 1. Schematic of solaxe (left), bi-axis (middle), and V-trellis (right) canopy architectures.

Orchard effects

Air temperature inside the orchard blocks can be 3.4 to 7.9 ℉ (yearly mean of the daily maximum offsets) lower than the open-field conditions. Voluminous canopies typical in solaxe type architecture tend to have the highest offsets compared to V-trellis and Bi-axis trained orchards. Air temperature offsets increase as the canopy starts developing from April and tends to peak during July or August, when the open-field mean air temperatures are also the highest.

Relative humidity inside the orchard can be 9.2 to 27.5 % (yearly mean of the daily maximum offsets) higher than the open-field conditions due to canopy transpiration, especially during the growing season. The offsets tend to be minimal as the tree transpiration slows down in winter months.

Wind Speed inside the orchard can be 4.9 to 8.3 mph (yearly mean of the daily maximum offsets) lower than what is observed in open field. Voluminous canopies, typical to solaxe training system, tend to have higher obstruction to wind than less voluminous and open canopies in V-trellis blocks. Wind obstruction can affect other weather variables namely air temperature and relative humidity as well. Wind speed offsets are less prominent during winter (seasonal mean offsets: 1.1 to 3.4 mph) with calmer open-field wind speed data trends. Solar radiation sensors may get obstructed by the canopy when the sensor is installed at 5 ft above the ground. This reduces solar radiation quantification and influences the offset. To avoid solar radiation offsets, one may consider installation above canopies or use the nearby open-field solar radiation data for decision making.

 

Often, offset variability depends on the hour of the day, month of the year, season, canopy growth, training system, and open-field weather condition. Tables 1 summarizes the monthly averages of the daily maximum offsets for air temperature, relative humidity, and wind speed considering three orchard training systems. These tabulated offsets could be used for translating open-field data to realize in-orchard weather conditions to potentially help in guiding the management decisions.

Table 1

Month Air temperature (℉)
Solaxe
Air temperature (℉)
Bi-axis
Air temperature (℉)
V-trellis
Relative humidity (%)
Solaxe
Relative humidity (%)
Bi-axis
Relative humidity (%)
V-trellis
Wind Speed (mph)
Solaxe
Wind Speed (mph)
Bi-axis
Wind Speed (mph)
V-trellis
Jan
Day
2.7 1.1 1.4 -4.6 -1.5 -3.2 4.6 3.8 4.2
Jan
Night
3.9 2 2.3 -7.5 -2 -4 4.5 4.5 4.3
Feb
Day
3.1 1.6 1.6 -6.6 -2.4 -2.9 6.6 5.4 5.5
Feb
Night
5.2 2.7 2.3 -10.6 -3.3 -4.4 6.5 6 5.7
Mar
Day
3.2 1.6 2.2 -6.9 -3 -4.9 6.7 5.8 5.8
Mar
Night
5 2.3 2.2 -10.6 -3.7 -4.3 4.8 5.4 4.9
Apr
Day
2.9 3.4 3.1 -7.5 -4.8 -8.2 8.1 7.2 7.1
Apr
Night
4.7 4 2.9 -10.3 -6.5 -8.2 5.7 6.9 6.1
May
Day
4.9 2.2 3.3 -17.3 -6.9 -10.7 8.5 8.1 7.9
May
Night
5.9 4 3.8 -17.8 -10.4 -11.2 6.8 7.2 7
June
Day
7.3 4.3 4.4 -26.2 -17.7 -15.8 7.7 7.6 7.8
Jun
Night
7.4 4.9 4.4 -23.9 -16.8 -13.4 6.5 7.6 7.1
Jul
Day
11.3 7.7 6.7 -40.7 -25.3 -23.4 6.3 6.7 6.8
Jul
Night
10.2 6.7 5.3 -32.1 -19.2 -17 4.4 8.3 8.1
Aug
Day
10.1 6.7 6.8 -38.2 -24.7 -23.9 6.2 6.3 6.6
Aug
Night
9.2 7.4 5.8 -32.1 -24 -19.6 4.9 7.6 7
Sept
Day
7.2 5 5.9 -25.7 -18.7 -21.1 5.6 5.4 5.3
Sept
Night
8.7 7 6.1 -27.4 -23.8 -21.6 5.1 6.5 5.9
Oct
Day
5.9 4.1 4.3 -20 -13.3 -16.1 5.9 5.1 5.3
Oct
Night
8.1 5.6 4.9 -23.3 -17.3 -15.1 5.9 5.8 5.4
Nov
Day
3.2 2 2.5 -7.7 -4.1 -6.1 7.4 4.9 6.1
Nov
Night
4.9 3.2 3.4 -11.6 -5.8 -7 6.7 6 6
Dec
Day
2.6 2.2 1.9 -6.4 -4.1 -3.8 5.8 5.6 5.3
Dec
Night
3.5 3.1 2.4 -8.8 -7 -5.2 5.6 6.3 5.5

Orchard sites: Solaxe (Grandview, WA), Bi-axis (Quincy, WA), V-trellis (Pasco & Wahluke, WA). Positive values indicate a decrease in-orchard temperature and wind speed. Negative values indicate relative humidity increase inside the orchard. *These offsets can vary by 20 to 121% for air temperature; 22 to 241% for relative humidity, and 24 to 75% for wind speed depending on the time of the year and prevailing weather patterns at the site. Higher variability was observed during winter months. The site elevation can also impact these offsets.

Effects of drip, under-tree irrigation, and overhead evaporative cooling

Overall, drip irrigation has minimal contribution to the orchard microclimate. However, under-tree sprinklers may create moist air in the lower (3 ft above ground) orchard microclimate, cooling the air. Overhead sprinklers have pronounced effects with maximum air temperature reduction up to 9.4 ℉, and maximum relative humidity increase up to 9.2 % (Fig. 2). These effects linger in the orchard during evening and night hours. The effects can also vary depending on the evaporative cooling frequency and amount of water applied to mitigate the heat stress.

two graphs

Fig. 2. Mean hourly air temperature and relative humidity offsets (line) and 0.5 standard deviation (shaded) between paired open- and in-orchard stations for no-rainfall (dry), irrigation, and overhead sprinkler days.

Acknowledgements

This report is a snapshot of the Orchards Effect Project conducted by a research team including Karisma Yumnam, Matthew D. Cann, Lav Khot, David J. Brown, Lee Kalcsits, and Joseph Boomgard-Zagrodnik, funded by the Washington Tree Fruit Research Commission. Please refer to the full report for additional clarity on the reported methods and pertinent results. We thank WSU AgWeatherNet, Precision Ag Group members, and grower cooperators for their help in completion of this study.

Contacts

Karisma Yumnam professional photo

Karisma Yumnam
Graduate student
Karisma.yumnam@wsu.edu

Lav Khot professional photo

Lav Khot
AgWeatherNet Director
lav.khot@wsu.edu

 


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