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Title Field Evaluation of Ion Uptake of Avocado Rootstocks as Affected by Salinity
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Author Celis, Nydia
Publication Date 2016
Peer reviewed|Thesis/dissertation
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UNIVERSITY OF CALIFORNIA RIVERSIDE
Field Evaluation of Ion Uptake of Avocado Rootstocks as Affected by Salinity
A Thesis submitted in partial satisfaction of the requirements for the degree of
Master of Science in
Environmental Sciences
by
Nydia Celis
June 2016
Thesis Committee: Dr. Laosheng Wu, Chairperson Dr. Donald L. Suarez Dr. David E. Crowley
Copyright by Nydia Celis 2016
The Thesis of Nydia Celis is approved:
Committee Chairperson
University of California, Riverside
ACKNOWLEDGMENTS
I would like to express my sincerest gratitude to my major advisor, Dr. Laosheng Wu, for accepting me into the program and for his advice and guidance throughout this process. I am especially grateful to Dr. Donald Suarez for his guidance, support, expertise, and patience. I also want to thank him for allowing me to use the facilities at the U.S. Salinity
Laboratory to conduct the soil and plant analysis and for training me on various instruments. I would also like to thank Dr. David Crowley for serving in my thesis committee and giving me valuable recommendations and suggestions.
Special thanks to Dr. Peggy Mauk for her collaboration on the funded research project. I would also like to thank all the staff who helped keep the project running Rui
Li, Bruce Martin, Charles Farrar, Mahlet Desta, and Priya Kumar. I would also like to thank the Avocado Commission for funding this project.
To Nancy Phu for sharing this journey with me and for being supportive and helpful along the way. To my sisters Monica and Nathalie for always believing in me and cheering me on.
iv
DEDICATION
I would like to dedicate this thesis to my husband, Rene, for his love and support and to my parents Ernesto and Juana who at an early age instilled in me the value of education and for their unconditional love and encouragement. I would also like to dedicate this to
God without whom none of this would have been possible.
v ABSTRACT OF THE THESIS
Field Evaluation of Ion Uptake of Avocado Rootstocks as Affected by Salinity
by
Nydia Celis
Master of Science, Graduate Program in Environmental Sciences University of California, Riverside, June 2016 Dr. Laosheng Wu, Chairperson
With potable water becoming a scarce resource in semi-arid regions, we must evaluate the potential to use degraded water for irrigation without reducing yield. Avocado is one of the most salt sensitive crops, and one of the highest value per acre. The objectives of this experiment were to screen avocado rootstocks for salinity tolerance, study the effect of sodium and chloride on growth and yield, quantify the salt distribution in the root zone, and relate the soil salinity to leaf ion composition and in turn to salt tolerance. A field experiment was conducted to evaluate the salt tolerance of 13 different avocado rootstocks grafted onto Hass scion. The experiment consists of 252 trees arranged in a randomized block design with four rows per block and four replications. The experimental plot is arranged in rows which alternate between those irrigated with a fresh water control, and a salt treatment with electrical conductivities of 0.5 dS m -1 and 1.5 dS
-1 -1 -1 m and chloride levels of 0.73 mmol c L and 4.94 mmol c L respectively.
vi The salt movement during the salinization process was recorded by selected intensive soil sampling, and soil resistivity profiling using the SuperSting ® 56 electrode resistivity imaging system. We collected soil samples and leaves from the plot and analyzed them for major ions. The rootstocks R0.06, R0.07, PP14, and R0.17 were found to have high concentrations of chloride and sodium in the leaves and therefore the least salt tolerant having 100 % mortality in the salt treated rows after being irrigated for 23 months.
The rootstocks that had low concentrations of sodium and chloride ions in the fully expanded leaves were R0.05, PP40, R0.18 and DUSA, which were also the rootstocks whose growth and yield was minimally affected also exhibiting the highest yield, highest trunk diameter and highest survival percentage.
vii
TABLE OF CONTENTS
Chapter Page
ACKNOWLEDGMENTS ...... iv
DEDICATION ...... v
ABSTRACT ...... vi
TABLE OF CONTENTS ...... viii
LIST OF TABLES ...... x
LIST OF FIGURES ...... xi
CHAPTER I: INTRODUCTION ...... 1
CHAPTER II: BACKGROUNG AND LITERATURE REVIEW ...... 3 Avocado...... 3 Salt Tolerance...... 5 Avocado Salt Tolerance...... 6 Plant Ion Composition ...... 8 Toxicity...... 10 Sodium Toxicity...... 11 Chloride Toxicity...... 11 Salinity Problem...... 12 Electrical Tomography ...... 14 2 Dimensional Surveys ...... 14 Advantages and Disadvantages of the Different Arrays...... 16 Electrical Resistivity and Soil Properties...... 17
CHAPTER III: METHODOLOGY ...... 21 Experimental Design ...... 21 Irrigation ...... 23 Electrical Resistivity ...... 24 Soil ...... 25 1:1 Soil Extracts...... 26 Soil Texture ...... 27 Soil Electrical Conductivity ...... 29 pH Measurements ...... 29 Chloride Titrator...... 30
viii
Chloride in Soil Extracts...... 31 Leaf Collection ...... 32 Chloride in Plant Samples...... 33 Wet Digestion ...... 34 Statistics...... 35
CHAPTER IV: RESULTS ...... 36 Irrigation Water ...... 36 Soil Sampling Analysis ...... 40 Soil Temperature...... 44 Electrical Resistivity ...... 45 Plant Ion Analysis ...... 47 Rootstock Survival ...... 60 Plant Ion Interaction...... 62 Avocado Yield ...... 64 Plant Growth Parameters...... 71
CHAPTER V: SUMMARY...... 75
CHAPTER VI: CONCLUSION...... 78
REFERENCES...... 80
ix
LIST OF TABLES
Table Page
Table 1: Chloride Tolerance of fruit crop cultivars and rootstock ...... 7
Table 2: Electrical conductivity of saturation extracts at which yield reductions become significant ...... 8
Table 3: Irrigation Water Composition...... 23
Table 4: Calculated leaching fractions based on the chloride and the electrical conductivity of the 1:1 extracts ...... 44
Table 5: Average leaf ion composition collected October 2013...... 49
Table 6: Average leaf ion composition in control rows collected in October 2014. . . . . 52
Table 7: Average leaf ion composition in salt rows collected in October 2014...... 52
Table 8: Average leaf ion composition in control rows collected in October 2015. . . . . 55
Table 9: Average leaf ion composition in salt rows collected in October 2015...... 56
Table 10: Potassium-Sodium ratio for each rootstock in the salt treated rows ...... 62
x
LIST OF FIGURES
Figure Page
Figure 1: California Avocado Acreage Summary by County in 2014...... 4
-1 Figure 2: Dusa in control row EC w = 0.5 dS m ...... 36
-1 Figure 3: Dusa in a salt treated row EC w = 1.5 dS m ...... 36
-1 Figure 4: R0.07 in control row EC w = 0.5 dS m ...... 37
-1 Figure 5: R0.07 in a salt treated row EC w = 1.5 dS m ...... 37
Figure 6: Water meter data for row 26 in 2015...... 38
Figure 7: Water meter data for row 28 in 2015...... 38
Figure 8: Water meter data for row 29 in 2015...... 38
Figure 9: Water meter data for row 32 in 2015...... 38
Figure 10. Average leaching fraction tracked monthly in both the salt -1 -1 treated rows (EC w = 1.5 dS m ) and in the control (EC w = 0.5 dS m )...... 39
Figure 11: Mean electrical conductivity of soil extracts collected in December 2013 ...... 40
Figure 12: Mean chloride concentration in soil extracts collected in December 2013...... 40
Figure 13: Mean electrical conductivity of soil extracts collected in March 2014 ...... 41
Figure 14: Mean chloride concentration in soil extracts collected in March 2014...... 41
Figure 15: Mean electrical conductivity of soil extracts collected in August 2014 ...... 42
Figure 16: Mean chloride concentration in soil extracts collected in August 2014...... 42
xi
Figure 17: Mean electrical conductivity of soil extracts collected in June 2015 ...... 43
Figure 18: Mean chloride concentration in soil extracts collected in June 2015 ...... 43
Figure 19: Electrical resistivity survey for row 28, a control row, profiled in June 2014...... 45
Figure 20: Electrical resistivity survey in a salt treated row, 29, profiled in June 2014...... 46
Figure 21: Electrical resistivity survey in control row 28, showing soil EC a profile in June 2015...... 46
Figure 22: Electrical resistivity survey in salt treated row 29, showing EC a profile in June 2015...... 47
Figure 23: Average sodium concentration in leaves per rootstock collected October 2013...... 50
Figure 24: Average chloride concentration in leaves per rootstock collected in October 2013...... 51
Figure 25: Average sodium concentration in leaves per rootstock collected October 2014...... 53
Figure 26: Average chloride concentration in leaves per rootstock collected October 2014...... 54
Figure 27: Average sodium concentration in leaves per rootstock collected October 2015...... 57
Figure 28: Relative sodium in avocado leaves sampled October 2015...... 58
Figure 29: Average chloride concentration in leaves per rootstock collected October 2015...... 59
Figure 30. Avocado survival percentage by rootstock in salt treatment ...... 61
Figure 31: Calcium and sodium concentration in the salt treated (1.5 dS m -1) rows. . . . .63
xii
Figure 32: Yield based on the average number of fruit and weight for each rootstock in rows irrigated with an EC of 0.5 dS m -1 harvested February 2014...... 64
Figure 33: Yield based on the average number of fruit and weight for each rootstock in rows irrigated with an EC of 1.5 dS m -1 harvested February 2014...... 65
Figure 34: Yield based on the average number of fruit and weight for each rootstock in rows irrigated with an EC of 0.5 dS m -1 harvested February 2015...... 66
Figure 35: Yield based on the average number of fruit and weight for each rootstock in rows irrigated with an EC of 1.5 dS m -1 harvested February 2015...... 67
Figure 36: Relative yield for avocados harvested in February 2015...... 68
Figure 37: Average avocado yield vs. average sodium concentration in leaves collected in 2015...... 69
Figure 38: Average avocado yield vs average chloride concentration in leaves collected in 2015...... 70
Figure 39: Average trunk diameter of each rootstock in the salt (1.5 dS m -1) and control (0.5 dS m -1) rows 2015...... 71
Figure 40: Average trunk diameter of each rootstock in the salt treated rows 2015. . . . .72
Figure 41: Relative trunk diameter measured in 2015 ...... 73
Figure 42: Average tree canopy of each rootstock in the salt (1.5 dS m -1) and control (0.5 dS m -1) rows measured in 2015...... 74
xiii CHAPTER I:
Introduction
World population is growing at an exponential rate and it is anticipated to reach about 9.6 billion by the end of 2050, according to the 2015 United Nations world population report. As population increases, it is uncertain if we will be able to produce the necessary food to feed our growing population. Some of the causes for the reduction in agriculture include drought, salinity, and water availability. With clean water becoming such a scare resource, it is imperative that we develop the proper irrigation scheduling and monitoring in order to be able to produce the food necessary to feed our growing population. Due to the drought California has been experiencing over the past several years, it is essential that we expand the use of lower quality or recycled water in irrigated agriculture. The current 4 year drought has aggravated the trend of increasing scarcity and costs of fresh water. Due to cost considerations and availability of fresh water, California growers will likely be forced to rely more on either saline water or other low quality water for irrigation. In some cases, river water or well water could be supplemented with recycled water to produce a blend with a lower salinity than that of straight recycled water. There is significant opportunity to substantially expand the use of recycled water for agricultural irrigation while still conserving water. Both drip and microsprinkler, efficient irrigation systems, have been successfully used to irrigate avocado trees. Avocado is one of the most salt sensitive crops, and one of the highest value crops per acre.
1 California is the top agricultural producing state in the United States.
California ranks number one in the United States fruit production, growing an overwhelming majority of the nation’s grapes, strawberries, peaches, nectarines, avocados, raspberries, kiwifruit, olives, dates, and figs (USDA, 2015). Avocado is an important crop for California agriculture and one of the most sought after and exported fruit. California dominates U.S. production, accounting for about 88 percent of the U.S. avocados harvested. Nearly all of it takes place in Southern California, along the coast in a five county region extending from San Luis Obispo to San Diego (CDFA 2014).
2 CHAPTER II:
Background and Literature Review
Avocado
Avocado (Persea Americana Mill) is one of the most salt sensitive crops known, grown in tropical and subtropical climates. California has great weather for growing avocados with a semi-arid climate, mild winters and winter rainfall, but it requires substantial irrigation. Hass is a hybrid between the Guatemalan (85 percent) and Mexican
(15 percent) race (Bergh and Ellstrand, 1986). It is recognize as having superior overall quality and is therefore the industry standard. Bender 2012, stated that it has the longest harvest season (January - August in San Diego county, and as late as June - October in
Santa Barbara and San Luis Obispo counties), and is currently the recommended cultivar for new plantings. Hass is grown in most of the southern California’s coastal counties and the western end of Riverside County, especially in locations that have mild summer temperatures and little or no frost. Hass acreage by county is presented in Figure1, showing San Diego County producing 35 percent, followed by Ventura country with 33 percent and then Riverside with 11 percent (Acreage Inventory Summary, 2014).
3
Figure 1. California Avocado Acreage Summary by County in 2014 data from the Avocado Commission.
Figure 1, also shows the varietal distribution with ninety-five percent being Hass, three percent Lamb and two percent other. Hass is also recognized to have several shortcomings, including poor fruit set in some locations, sensitivity to saline irrigation water, intolerance to cold temperature below 30 °F (Bergh 1984), and susceptibility to persea mites and phytophthora root rot.
4 Due to biotic and abiotic stresses, Hass is grafted onto other rootstocks, and it is an industry priority to develop new rootstocks that will be resistant to these stresses.
Salt Tolerance
Salinity has a broad range of effects on plants, therefore there are also many different mechanisms for plants to tolerate this stress. Stuart et al. 2014, classified these mechanisms into three main categories: firstly, osmotic tolerance, which is regulated by long distance signals that reduce shoot growth and is triggered before shoot
Na + accumulation; secondly, ion exclusion, where Na + and Cl − in the roots reduce the accumulation of toxic concentrations of Na + and Cl − within leaves; and finally, tissue tolerance, where high salt concentrations are found in leaves but are compartmentalized at the cellular and intracellular level (especially in the vacuole). They point out that not much is known about tolerance in the osmotic phase. But a lot is known about the ionic phase which is caused by the accumulation of Na + and Cl − in the leaf blade. Plants can reduce toxicity by reducing accumulation of toxic ions in the leaf blades (Na + and
Cl − exclusion), and/or by increasing their ability to tolerate the salts that they have failed to exclude from the shoot, such as by compartmentation into vacuoles (tissue tolerance) (Munns and Tester 2008).
5 Avocado Salt Tolerance
Most fruit crops are more sensitive to salinity than are grasses or root crops (Maas and Hoffman 1977). Grapes, citrus, stone fruits, pome fruits, berries and avocados are all relatively sensitive to salinity. Multiple researchers have found citrus and avocado rootstocks to vary in their capability to absorb and transport sodium and chloride ions resulting in different salt tolerances.
Increased varietal salt tolerance gives farmers an opportunity to continue growing great quality avocados while using low quality water or planting in salt affected soils. For this reason, classification of fruit crops with respect to specific salinity according to varieties and rootstocks is important. Oster and Arpaia (1992) showed that the tolerance level of the avocado scion is dependent on the rootstock used.
Bernstein (1965) pointed out that for many fruit crops damage to the plants could be related to the concentration of specific ions, e.g. chloride or sodium in the soil solution and/or plant leaves rather than to the total soil salinity. This is why injury symptoms may appear before any effect of total salt concentration is observed. Such a tolerance classification is presented in Table 1 (after Maas 1984), which shows the maximum permissible chloride content in the root zone, expressed as the concentration in the saturation extract (Cl e) and in irrigation water in order to avoid leaf injury and in turn fruit yield. If the chloride concentration in the leaves exceeds the tolerance of the crop, then injury symptoms develop such as leaf burn or necrosis of the leaf tissue.
6 Table 1 Chloride Tolerance of some fruit crop cultivars and rootstock Maximum Permissible Cl- without Leaf Injury
Crop Rootstock of Cultivar Root Zone (Cl e) Irrigation Water (Cl w) (mmolc L-1) (mmolc L-1)
Rootstock
Avocado West Indian 7.5 5
(Persea americana) Guatemalan 6 4
Mexican 5 3.3 Table 1. After Maas 1984, which show the maximum permissible chloride content in the root zone and in irrigation water in order to avoid leaf injury.
High concentrations of sodium in the saturated paste extract in particular are considered toxic to sensitive rootstocks although the same rootstock can tolerate a higher total salinity if it is not due to sodium chloride salts, because many of these salts are considered nutrients to the plant. Cooper and Gorton, 1950 give an example were the
-1 chloride level of 10 mmol c L in a saturated paste extract is considered toxic to sensitive rootstocks although the same rootstock can tolerate a higher total salinity if it is not due to chloride salts. Symptoms vary with the growth stage, being more severe if the salts affect the plant during the early stages of growth.
7 Table 2. Electrical conductivity of saturation extracts at which yield reductions become significant dS m -1
Crop @ 25 oC
Date palm 8
Grape 4
Muskmelon 3.5
Orange, grapefruit, lemon 2.5-3
Plum, prune, peach, apricot,
almond 2.5
Avocado 2
Strawberry 1.5 Table 2. After Bernstein 1965, the table shows that when the electrical conductivity of the -1 soil saturation extract reaches an EC e of 2 dS m then a 10 percent reduction in avocado yield is observed. More recently Oster and Arpaia (2007) observed a threshold at EC e= 1 dS m-1 for Mexican rootstock.
Plant Ion Composition
Soil and plant tests provide valuable information about the chemical properties that affect plant growth. Various elements have been identified as essential for plant growth, initially von Liebeg (1840) and subsequently others, began analyzing plants for their ion content.
8 Elements considered essential for plants are carbon, oxygen, and hydrogen which the plant gets from water in the form of irrigation and rainfall and carbon dioxide in the atmosphere. The macronutrients are nitrogen, phosphorus, potassium, calcium, magnesium and sulfur and the micronutrients are boron, chloride, copper, iron, molybdenum, manganese, and zinc. Plants require these essential nutrients for normal function and growth. These nutrients are absorbed from the soil and taken up through the plants roots. These essential nutrients might be available in the soil in low concentrations and this is when fertilizers and/or amendments are applied.
Plant analyses allow us to determine the concentration of these specific elements in the plant tissue and are good diagnostic tests that provide a glimpse into the nutrient levels of the plant at the time of sampling. They provide useful information about nutrient deficiencies, toxicities or imbalances that the plant might be experiencing even when no symptoms are displayed.
Plant nutrients need to fall within specific ranges in order to meet the plants nutritional requirements. Values outside of this range can cause crop health and growth to decline due to either a deficiency or toxicity. Nutrient deficiency occurs when an essential nutrient is not available in sufficient quantity to meet the requirements of the growing plant. Toxicity occurs when a nutrient is in excess of the plant needs and decreases plant growth or quality (Ayers and Westcot, 1985). When an essential element is deficient or accumulating in excess then photosynthesis and other metabolic process are disturbed.
9 If ion toxicity or deficiency continues for an extended period of time then visual symptoms may appear in the leaves or there may be a decrease in growth rate or development. Crop yield will also be significantly decreased unless the deficiency can be corrected.
Toxicity
A toxicity problem is different from a salinity problem in that it relates to specific ion concentrations in the plant rather than general (osmotic) effects. Toxicity normally results when certain ions are taken up by the plant and accumulate in the leaves as a result of transpiration to an extent that result in leaf damage.
The degree of damage depends upon time, concentration, crop sensitivity and crop water use, and if damage is severe enough, crop yield is reduced. The usual toxic ions in irrigation water are chloride, sodium and boron (Ayers and Westcot, 1985). Damage can be caused by each, individually or in combination. Not all crops are equally sensitive to these toxic ions. But almost any crop can suffer from toxicity if the concentrations are high enough. Toxicity often accompanies or complicates a salinity or infiltration problem although it may appear even when salinity is low (Ayers and Westcot, 1985).
Toxic ions such as sodium and chloride can also be absorbed by the plant through the leaves moistened during overhead sprinkling with irrigation water containing high salts.
This occurs typically during periods of high temperature and low humidity. The leaf absorption speeds the rate of accumulation of a toxic ion and may be a primary source of the toxicity (Ayers and Westcot, 1985).
10 Sodium Toxicity
Sodium toxicity is not as easily diagnosed as chloride toxicity. Symptoms of sodium toxicity in avocados are described as necrotic spots scattered on the leaf between the veins. Symptoms appear first on the older leaves, starting at the outer edges and, as the severity increases, move progressively inward between the veins toward the leaf center
(Ayers and Westcot, 1985). These symptoms are always associated with high sodium content in the leaves. Sodium can rise to toxic levels in older leaves, causing them to die.
For tree crops Avocado is a salt sensitive crop, sodium in the leaf tissue in excess of 0.25 to 0.50 percent (dry weight basis) is often associated with sodium toxicity (Ayers and
Westcot, 1985). Sodium exclusion by the roots or reduced transport from the roots to the leaves is beneficial if it prevents Na accumulation to toxic concentrations. The ability to maintain low sodium concentration in the leaves is a desirable trait in plants grown under saline conditions.
Chloride Toxicity
The most common toxicity is from chloride in the irrigation water. If the chloride concentration in the leaves exceeds the tolerance of the crop, injury symptoms develop such as leaf burn or drying of leaf tissue. Normally, plant injury occurs first at the leaf tips (which is common for chloride toxicity), and progresses from the tip back along the edges as severity increases (Mass 1984). Excessive necrosis (dead tissue) is often accompanied by early leaf drop or defoliation.
11 With sensitive crops, these symptoms occur when leaves accumulate from 0.3 to 1.0 percent chloride on a dry weight basis, but sensitivity varies among these crops.
Many tree crops, for example, begin to show injury above 0.3 percent chloride (dry weight) (Ayers and Westcot, 1985). For some crops, injury or reduction in yield in can occur in concentrations below 0.3 percent chloride. For irrigated areas, the chloride uptake depends on the soil chloride concentration, irrigation water chloride, the amount of leaching that has taken place, and the ability of the crop to exclude chloride (Ayers and
Westcot, 1985). Chemical analysis of plant tissue is commonly used to confirm chloride toxicity. Chloride toxicity is a major contributing factor for salt tolerance in avocado, but high sodium aggravates the problem. Soil salinity is a major environmental constraint to crop production.
Salinity Problem
Worldwide, more than 45 million hectares of irrigated land have been damaged by salt, and 1.5 million hectares are taken out of production each year as a result of high salinity levels in the soil (Munns & Tester, 2008). While many growers are concerned about the introduction of mineral salts that are carried in composts, manures, and fertilizers, by far the largest input of salt to the soil comes from irrigation water (Branson and Gustafson, 1972). All irrigation water contains dissolved mineral salts, but the concentration and composition of dissolved salts varies according to the source of the water and time of year (Hanson et al. 2006).
12 Soil salinity refers to soluble salts in the soils aqueous phase, which consist mostly of the cations sodium, calcium and magnesium, and the anions chloride and sulfate, and in lessor amounts potassium, bicarbonate, carbonate and nitrate ( U.S. Salinity Laboratory
Staff, 1954). A salinity problem occurs when these salts accumulate in the crop root zone to a concentration that affects the plants ability to extract sufficient water resulting in water stress and consequently in a reduction in yield. Salinity is quantified in terms of total concentration of such soluble salts, or more practically, in terms of the electrical conductivity of the solution, because the two are closely related ( U.S. Salinity Laboratory
Staff, 1954). An EC value of 1 is equivalent to approximately 640 parts per million of salt, but will vary depending on the composition of the ions in the solution. Other common units of measurement are mmho cm -1, and mS cm -1 (1 dS m -1 = 1,000 mS cm -1
= 1 mmho cm -1). McNeal et al. (1970) were among the first to establish the relationship between electrical conductivity and molar concentrations of ions in the soil solution.
These salts can originate from the soil itself or from water applied.
Salt distribution in the soil varies in both space and time. Depending upon leaching, salinity profiles may be uniform or vary with depth; they may be approximately that of the irrigation water near the soil surface to concentrations many times higher at the bottom of the root zone as a result of evapotranspiration and drainage (Mass and
Hoffman, 1977).
13 Electrical tomography
Electrical resistivity monitoring of soils has received a lot of attention due to its ability to estimate some soil physical properties while being minimally invasive. The basic principal and purpose of electrical resistivity surveys are to determine the subsurface resistivity distribution by making measurements on the ground surface, and from these measurements estimate the true resistivity of the subsurface (Loke 2000). The instrument works by sending an electric current into the ground through two electrodes
(A and B) that are planted in the ground and the resulting electric potential is measured between another two electrodes (m and n) (Samouelian et al., 2005). When heterogeneities exist in the subsurface, the resistivity varies according to the relative position of the electrodes (Samouelian et al., 2005). The calculated resistivity is then known as the apparent resistivity. This enables a qualitative estimation of the electrical parameters of the medium (Meheni et al., 1996) but does not give the true resistivity and shapes of the anomalies. Measured data is the apparent resistivity and is inverted to produce true subsurface resistivity distributions (Loke 2000).
2 Dimensional Surveys
Two dimensional multi-electrode surveys provide a two dimensional vertical profile of the soil. The current and potential electrodes are placed at fixed distances. The distribution of the resistivity depends on how much the soil material opposes the electric current. The depth is 15-20 percent of the array length (Greenwood 2014).
14 In order to survey at different depths with different array configurations depends on the spacing between electrodes. By increasing the distance the depth is also increased.
The depth of investigation inferred from the spacing is called the “pseudo-depth”. The data are then arranged in a 2 dimensional “pseudo-section” plot that gives a simultaneous display of both the horizontal and vertical variations in resistivity (Edwards 1977).
Each data acquisition corresponds to a volumetric measurement and constitutes qualitative information that is plotted against a pseudo-depth. Thus, the apparent resistivity values in a pseudo-section distort the real subsurface model picture and are closely dependent on the type of electrode array configuration (Andrew et al ., 1995) for a given study, the resulting apparent resistivity distribution depends on the sensitivity of the electrode array. Quantitative information on the resistivity distribution requires a mathematical inversion of the apparent resistivity measurement into interpreted resistivity which the instrument does internally.
Several different array configurations can be selected depending on the resolution of interest including but not limited to wenner, schlumberger, dipole, dipole-dipole, and pole. It is important that a proper configuration is selected for the specific objective as each configuration has specific advantages and limitations.
15 Advantages and Disadvantage of the Different Arrays
Greenwood 2014, describes the strength and weaknesses of each array configuration.
Dipole-dipole array has a depth penetration of approximately 15 % of the total spread length, good lateral resolution, and produces the highest resolution, but has poor signal to noise ratio.
Schlumberger array has a depth penetration of approximately 20 % of the total spread length, reasonably good lateral resolution, and good signal strength in low resistive conditions, but is unable to use multichannel, making the collection process slower.
Wenner has excellent vertical resolution and should be used when mapping horizontal layers, because it has poor lateral resolution and is unable to take advantage of the multichannel. It only takes one reading at a time.
The improvement of computer controlled multi-channel resistivity meter using multi- electrode arrays has led to an important development of electrical imaging. Switching channels allows any combination of four electrodes to be connected to the resistivity meter at any one time. The electrical data measurement is then fully automated and collection can be rapid (Bindley et al., 1996). Monitoring has advanced to where we can now monitor resistivity surveys remotely and obtain real time data.
16 Electrical resistivity and Soil Properties
Geospatial measurements of apparent electrical conductivity, EC a, have been successfully used for (1) identifying the soil physicochemical properties influencing crop yield patterns and soil conditions (2) establishing the spatial distribution of these soil properties, and (3) characterizing the spatial distribution of soil properties influencing solute transport through the vadose zone (Corwin et al, 1999, 2003a, 2003b, 2004; Kaffka et al., 2004).
Electrical resistivity is a function of a number of soil properties, including particle size distribution, porosity, water content, solute concentration (fresh water vs salt water) and temperature. Electrical resistivity decreases when the temperature increases.
Comparisons of electrical resistivity measurements require a correction of the electrical resistivity to a standard temperature of 25 o C.
In soils temperature variations are seen during the day and night and seasonal variation during the seasons from summer to winter. Electrolytic conductivity increases at a rate of approximately 2 % per o C increase in temperature ( U.S. Soil Salinity Lab Staff,
1954 ). It is necessary to take into account seasonal variation of the temperature and its relationship to electrical resistivity to avoid misinterpretation of field measurements when comparing resistivity collected at the same location during different times.
Electromagnetic surveys , EM, is another monitoring technique which can be made by mounting a fixed electrode array on a vehicle coupled with a data logger and GPS, which geo-references the EC a measurement (Rhoades, 1993; Carter et al., 1993).
17 Electrical resistivity and electromagnetic technology are both well suited for field-scale applications because their volume of measurements are large, which reduces the influence of local-scale variability (Corwin and Lesch 2005).
Electrical resistivity and electromagnetic surveying are very useful methods for soil characterization. Contrary to classical soil measurements and observations which disturb the soil by drilling and sampling, electrical resistivity and electromagnetic surveying is nondestructive and can provide continuous measurements over large area. This technology has been used at field scales to monitor a variety of anthropogenic properties including leaching, irrigation and compaction patterns without disturbing the soil structure (Corwin and Lesch 2005).
The greatest advantage of electrical resistivity monitoring is that it is minimally invasive and does not disturb the soil. It can also be rapidly and easily carried out over several meters and thus applied to describe both horizontal and vertical variability of soil structure and properties at the scale of interest (Tabbagh et al., 2000). Park (1998) underlines that this method may show details of fluid migration that are unavailable with conventional hydrological monitoring techniques.
Electrical current in soils is also based on the movement of ions in water, and is therefore greater with the presence of dissolved salts. This is why electrical resistivity surveys are used to monitor the salt movement in the subsurface soil. Since salts have to be in a dissolved form to conduct current the amount of water in soil governs the available paths of conductivity.
18 Real-time measurements obtained by electrical resistivity surveys may assist in our understanding of the transport of water and its spatial distribution. This approach is very useful for monitoring changes in soil and water distribution. Electrical resistivity measurements provide a powerful tool for detailed studies of vertical water movement in the vadose zone and help to monitor infiltration conditions.
Soils that have accumulated salts at the surface can be leached down below the root zone if more irrigation water is applied than the amount of water transpired or used up by the plant. Salts that contribute to a salinity problem are water soluble and easily transported by water. Leaching is the key to controlling salt accumulation in the root zone, especially in avocado since the crop uses water in the top few centimeters first since this is where most of the roots are located. However, the water used for leaching must not be very concentrated in salts or those salts can build up and affect the crop. The amount of leaching required is dependent upon the irrigation water quality and the salinity tolerance of the crop grown (Ayers and Westcot, 1985).
The objectives of this experiment were to screen avocado rootstocks for salinity tolerance, study the effect of sodium and chloride on growth and yield, quantify the salt distribution in the root zone, and relate the soil salinity to leaf ion composition and in turn to salt tolerance.
19 The results of this experiment will provide avocado growers with information as to which rootstocks are most useful for avocado production under moderately saline conditions, and allow growers to use irrigation water having higher salinity content with lesser damage to the trees and crop yield.
20 CHAPTER III:
Methodology
Experimental Design
A field experiment was conducted at the Agricultural Operation Station at the
University of California, Riverside between 2013 to 2015 to determine the salt tolerance of 13 different rootstocks grafted onto Hass scion. The avocado rootstocks tested included R0.05, R0.06, R0.07, R0.16, R0.17, R0.18, PP4, PP14, PP24, PP40, PP25,
Thom, and Dusa. The rootstocks were grafted onto Hass scion, as the Hass variety is the dominant commercial variety. It is important to screen these 13 different avocado rootstocks because California wells used for irrigation are high in salt content and crop salt tolerance is of major importance and interest. The experiment consists of 252 trees arranged in a randomized block design consisting of four rows per block. The plot was arranged in rows which alternate between those irrigated with a control and a salt treatment with electrical conductivities of 0.5 dS m -1 and 1.5 dS m -1 and chloride levels of
-1 -1 0.73 mmol c L and 4.94 mmol c L respectively. The control rows are irrigated with
Riverside Gage Canal water while the salt treatment consists of a blend of Gage Canal water (90 percent) and a concentrated salt water solution (10 percent). The field was irrigated with micro-sprinklers (which apply .16 gallons per minute) located beneath the tree canopy and installed approximately 30 cm from the tree trunk.
21 Avocados have very shallow roots. With avocado, approximately 80 % of the roots that take up water and nutrients are located in the top 25-30 cm of the soil, with the majority of the roots occurring close to the surface (Salgado and Cautín 2008). The tree consequently does not have access to a large volume of stored water, therefore mulch was applied beneath the tree canopy to reduce evaporation.
We used CIMIS (California Irrigation Management Information System) a system of automated weather stations that measures reference evapotranspiration ET o, and various weather data including temperature, solar radiation, humidity, wind speed, and precipitation. The CIMIS station is located at the Agricultural Operation Station on
University of California, Riverside and was used in conjunction with the Irrigation
Scheduling Calculator from Avocado Source to calculate the amount of irrigation water that needed to be applied. The field was irrigated 2 to 3 times per week depending on the trees water requirements. Salinization was imposed in a stepwise manner to avoid osmotic shock. The salt treatment was imposed in three stages, quarter strength in
November 2013, half strength in December 2013, and then full strength concentration in
January 2014 with each application interval lasting about a month. Soil samples were taken throughout the experiment to monitor the salt accumulation in the rootzone. In order to obtain a representative soil sample that characterizes the soil in which the avocado trees are grown, soil samples were collected 30 centimeters from the tree trunk in the wetted perimeter and in-between trees. Soil samples were collected using an auger
5 cm in diameter in 15 cm increments down to 75 cm.
22 The soil samples were collected in plastic Ziploc bags in order to maintain moisture content until analysis and prevent evaporation. The samples were then transported to the laboratory were moisture content was measured upon arrival. We conducted laboratory tests of soils and plant tissue to determine the effect of ion uptake on our 13 different avocado rootstocks. We also use electrical resistivity surveys to monitor the salt movement in the subsurface soil.
Irrigation
Table 3 shows the irrigation water composition for both treatments. The
-1 -1 concentrated salt solution was composed of 69.91 mmol c L of Na 2SO 4, 23.64 mmol c L
-1 -1 -1 of CaCl 2, 14.92 mmol c L MgCl 2, 4.13 mmol c L NaCl, 0.62 mmol c L KNO 3, and 0.11
-1 mmolc L of KCl.
Table 3. Irrigation Water Composition -1 (mmol c L )
-1 Ca Mg Na K HCO 3 SO 4 Cl NO 3 EC (dS m ) SAR
Control 3.09 0.45 1.65 0.07 1.50 2.68 0.73 0.35 0.50 1.24
Salt 5.15 1.90 8.89 0.14 1.35 9.41 4.94 0.38 1.50 4.74 Table 3. Irrigation water ion composition
23 Electrical Resistivity
We conducted surveys in the summer and in the spring to monitor the salinization process. These surveys were temperature adjusted (to 25 oC) and calibrated with soil cores collected at the same locations. Loke 2000, describes the typical set up for a 2 dimensional survey as a number of electrodes along a straight line attached to a multi- core cable with equal spacing. The multicore cable is attached to an electronic switching unit which is connected to a laptop computer which has been programed with the sequence of measurements, the type of array and other survey parameters. After reading the control file, the computer program automatically selects the appropriate electrodes for each measurement.
Soil surveys were performed 30 cm away from each tree trunk at one meter spacing with a wenner array configuration. We performed two dimensional multi-electrode surveys that were installed in a straight line running parallel to the trees.
The electrodes were installed 1 meter apart beginning 2 meters before the first tree and two meters past the last trees for a total of 50 meters. The instrument had previously been programed to run a wenner array. The programing is downloaded from the AGISAdmin software onto the instrument. This command file is then selected to collect the data. The electrode switching is automatic and does not require the electrodes (stakes) to be moved during the survey. The SuperSting ® R8 has the capability of taking 8 readings simultaneously at about 2 seconds per data point therefore being able to collect a large number of data in a short period of time.
24 Once a file was created and the correct command file selected and all of the cables and stakes connected, a resistance test was run. A resistance test is used as quality control to ensure that everything is connected property and that each sensor is able to take a reading. If one of the cables is not connected properly or a stake is not conducting, a warning will show up on the instrument notifying the user of the general area where the problem is occurring. Once the problem is identified and resolved then the resistance test is run again. Once the resistance test passed we began the survey. When the survey ends the instrument beeps multiple times to notify the user. Once the survey finished taking readings, and before anything was disconnected the file was opened to verify that the survey had collected all of the data. Then the survey was moved to the next row until the entire field was imaged.
Soil
In an attempted to obtain a representative soil sample that would characterize the soil the avocado trees are exposed to, soil samples were collected 30 cm from the tree trunk next to (in wetted area) and in-between trees. The samples were collected using an auger
5 cm in diameter. Samples were taken in 15 cm increments down to 75 cm. The soil samples were collected in prelabeled plastic Ziploc bags and securely sealed in order to maintain moisture content until analysis and prevent evaporation. The samples were then transported to the laboratory were the moisture content was measured upon arrival.
Those samples were then processed in the laboratory and moisture content was measured.
25 This was done by weighing an empty container which will be used for the calculation.
The soil was thoroughly mixed, and then a subsample of the soil was taken and the weight of both the container and the soil was determined. Next the sample was placed in an oven at 105 degrees centigrade for 24 hours until completely dry and there were no changes in weight. The samples were then removed from the oven, then let cool to room temperature and then a dry weight taken.
The percent water content was calculated using the following formula:
% Water content = (Moist wt. (Can+soil) - dry wt. (can +soil)) / (dry wt. – can wt.) *100
Once the moisture content was measured the remaining soil sample was prepared for ion analysis. The soils samples was allowed to air dry until completely dry and then was passed through a 2 mm sieve in order to get uniform grain size for the analysis.
1:1 Soil Extracts
The 1:1 soil extraction method used was taken from Zhang et al., 2005. We weighed out 25 milliliters of deionized water and added it to 25 grams of ground (2mm sieved) oven dry soil to create a solution with equal parts soil and water. The solution was allowed to equilibrate by putting it on a shaker and gently shaking for 3 hours. After shaking the sample was mixed and transferred to a filter funnel with a Whatman No. 2 filter paper and extracted using a low pressure vacuum and the extract was collected in plastic bottles. Randomized samples were selected and a saturation paste was made.
26 Based on the consistency in soil texture and saturation percentage a ratio to the saturation paste extract was made and applied to the electrical conductivity of the 1:1 extracts in order to convert them to saturation extract values.
Soil Texture
The soil texture was determined using the hydrometer method adapted from
Bouyoucos (1936). Once the soil was air dried and ground to pass through a 2 mm sieve it was then placed in an oven at 105 oC overnight to remove all moisture and placed in a desiccator to avoid the absorption of any moisture. Then 40 grams of the soil was weighed and placed into a 250 mL wide mouth plastic bottle.
We then added 200 mL of sodium chloride to the bottle. Sodium chloride was added to ensure that the electrical conductivity of the samples was above 5 dS m -1. The samples were next placed on the shaker set to low position, for 1 hour, and then centrifuged on
60,000 rpm for 30 minutes. The following equation was used to convert revolutions per minute to relative centrifugal force:
RCF or G-force= 1.12 * R * (RPM/1000)²
Where RCF or G Force = relative centrifugal force, RPM = revolutions per minute,
R= centrifugal radius in mm. The RCF was calculated to be 322,560.
After centrifugation the sample was decanted and the supernatant discarded. We then added 200 mL of sodium hexametaphosphate and placed on a shaker overnight on the low setting. A blank was also prepared with 200 mL of sodium hexametaphosphate and left on the shaker overnight.
27 We poured and rinsed (with deionized water) the contents of the bottle into a 1 liter graduated cylinder and bought it up to the 1 liter mark with deionized water. The prepared blank with 200 mL of sodium hexametaphosphate was also poured into a 1 liter graduated cylinder and brought to 1 liter mark and left overnight to equilibrate.
The following day a clean hydrometer was placed into the blank cylinder and the reading was recorded. The contents of the remaining cylinders were vigorously mixed with a plunger for 2 minutes to mix thoroughly making sure to suspend the sediment in the bottom of the cylinder. We next lowered the hydrometer carefully into the cylinder as to not disturb the solution and read above the meniscus. Readings and temperature were recorded at 35 seconds, 55 seconds (sand), 75 seconds, 6 hours (clay), 7 hours 20 min, and at 24 hours 2 minutes. The hydrometer was placed into the cylinder 30 seconds before the reading in order to have minimal bobbing when taking the reading. After each reading the hydrometer was carefully removed from the cylinder and wiped clean. A watch glass was placed on top of the cylinder to avoid contamination between readings.
The United States Department of Agriculture (USDA Staff, 1987) categorized soils into 3 size categories: sand (2 mm – 0.05 mm), silt (0.05 mm – 0.002 mm) and clay (<
0.002 mm). The hydrometer analysis is based on Stokes’ law (Jury and Horton, 2004), which establishes a relationship between particle size and the rate of sedimentation. Thus, particles are assessed by their settling velocities from suspension in a water solution that can be used to quantify particle size. The viscosity of water is affected by temperature, and this is why it is necessary to measure the temperature when taking readings and to correct to a standard temperature (typically corrected to 20 oC).
28 Once the data was collected, it was entered into the Mathematica program which calculated the following percentage composition 50.36 % sand, 41.11 % silt, and 8.53 % clay making this soil a loam (Wolfram 2008) .
Soil Electrical Conductivity
To determine the electrical conductivity (EC), the soil solution is placed between two electrodes of constant geometry and distance of separation (Bohn et al., 1979). An Amber
Science Model 1056 digital conductivity meter was used to measurement the conductivity of the soil extracts. At the beginning of the day when conductivity would be measured the instrument was calibrated with 0.718 dS m -1 standard calibrating solution that came with the instrument. Once the instrument was calibrated we began reading our samples. About 3.0 mL of solution were added to the vial and the probe was dipped into the container and moved up and down a few times to dislodge any air bubbles. The probe was then submerged in the sample and allowed to equilibrate and the reading was recorded. The cell was then rinsed and dried and repeated for every sample.
pH measurements
Oakton pH/CON Ion 510 Series Benchtop Meter was used to measure pH. The electrode was rinsed with deionized water and then submerged in a pH 4 buffer solution to initiate the two point calibration. The probe was submerged into the buffer solution making sure the tip of the probe was completely immersed in the sample.
29 The probe was used to gently mix the sample and then we used the cal/meas to enter the calibration mode. Then we waited for the pH value to stabilize and pressed enter to accept the calibration. This same process was performed for the pH 7 buffer solution.
After a two-point calibration was performed, the meter automatically returns to the pH measurement mode. The probe was rinsed after each calibration. Once the calibration was complete the probe was rinsed with deionized water and inserted into the unknown sample, making sure it was mixed and thoroughly homogenized while the sensor was completely submerged into the sample. Once the sample reached equilibrium it displays ready and the pH was recorded. After each use we stored the probe in the electrode storage solution in order to prevent it from drying out.
Chloride Titrator
The chloride content was determined with a Labconco Digital Chloridometer, a coulometric titrator. It displays concentration in milliequivalents of chloride per liter. The chemistry taking place is the oxidation of the silver electrode producing silver ions that react with the chloride ions resulting in a quantitative reaction of insoluble precipitate of silver chloride (AgCl). This reaction is carried out at a constant rate by passing a fixed direct current between a pair of silver electrodes immersed in an acid solution. The anode, which is consumed in this reaction, is a continuous spool of silver wire. As the immersed portion of the wire is consumed, fresh wire is drawn from the spool. As the equivalence point of the reaction is reached, silver ions build up in solution and an increase in current between a pair of separate indicator electrodes is detected.
30 At a preset indicator current, the instrument automatically stops the incremental counter and the generation of silver ions. Since the generator current is constant, the titration time is directly proportional to the number of chloride ions that are present into the sample vial
(Bishop, M. L., et al. 2000).
Chloride in Soil Extracts
First the chloridometer was turned on and the low range selected. Three standard concentrations were run prior to running the unknown samples. The first standard
-1 consisted of 1.0 mL of 4.0 mmol c L NaCl, 1.0 mL of polyvinyl alcohol, and 2 drops of gelatin, and 2 mL of deionized water to fill the vials to 4.0 mL The second standard
-1 consisted of 2.0 mL of 4.0 mmol c L NaCl, 1.0 mL of polyvinyl alcohol, and 2 drops of gelatin, and 1 mL of deionized water to fill the vials to the 4 mL mark. The third standard
-1 consisted of 3.0 mL of 4.0 mmol c L NaCl, 1.0 mL of polyvinyl alcohol, and 2 drops of gelatin. The standards were titrated and the value was recorded. The electrode was cleaned using a soft cloth and small amount of silver polish and rinsed with deionized water after about 10 samples or when white precipitate formed around the silver wire.
From the standards a correction factor was calculated. Depending on the amount of chloride in each sample 1.0 to 3.0 mL of each unknown sample was taken and titrated using the coulometric chloride titration in order for the concentration to fall within the standards. The samples were prepared by pipetting 2.0 mL of the unknown sample and adding 2 drops of gelatin, 1.0 mL of polyvinyl alcohol and filling the vial to the 4.0 mL mark with deionized water.
31 The samples were then titrated and the reading was recorded. After every 10 samples additional standards were run to confirm that there was no instrument drift.
Leaf Collection
By Dr. Mauk`s recommendation (P. Mauk personal communication, October 2013) we collected twenty of the most recent fully expanded leaves from non-flushing and non- fruiting branches as these are the best indicators of nutritional status. Leaf samples were collected in October from 2013 to 2015 in order to track ion composition over time. We recorded elemental analysis from season to season in order to determine if there was a trend in concentration or exclusion of the individual rootstocks. Leaves were prepared
+ + 2+ 2+, - 3- 2 - and analyzed for Na , K , Mg , Ca Cl , PO 4 , and SO 4 . Determination of the nutrient concentrations will help improve selection of salt tolerance rootstock.
The leaves were sampled and the fresh weight was taken using an analytical balance
(+/- .01 grams). Then leave samples were washed to remove any dust or other residues that might be found on the leaves that could contaminate the samples. The leaves were washed by rinsing them in tap water twice followed by a rinse in deionized water and then pat dried using a paper towel. The leaves were then placed in a paper bag and placed in an oven at 80 oC to dry for 48-72 hours. After drying, the dry weight of the samples was measured.
32 The percent water content was calculated using the following formula:
Water content (%) = (Fresh wt.-Dry wt. / Fresh wt.)*100
After grinding and homogenization, the ground plant material was placed in a container and securely sealed for analysis and storage.
Chloride in Plant Samples
We weighed out 0.1 gram of sample, ranging between 0.1000 to 0.1002 grams and placed it into a 25 mL Erlenmeyer flask. Next, 1.0 mL of 95 % ethyl alcohol was added into the flask and the flask was swirled gently so that all the plant material came in contact with the acid. We then added 24.0 mL of a 0.1 N nitric acid and 10 % acetic acid solution to fill to the 25 mL mark. A stopper was placed in the flasks and the samples were mixed and allowed to sit overnight. The following day the samples were shaken before beginning the titration process and the chloridometer was turned on and the low range selected. Three standard known concentrations were run prior to running the
-1 unknown samples. The lowest standard consisted of 1.0 mL of 1.0 mmol c L NaCl, 1.0 mL of 0.4 N nitric acid and 4 % acetic acid mixture, and 2 drops of gelatin, and 2.0 mL of deionized water to fill the vials to the 4.0 mL mark. The second standard consisted of 2.0
-1 mL of 1.0 mmol c L NaCl, 1.0 mL of 0.4 N nitric acid and 4 % acetic acid mixture, and 2 drops of gelatin, and 1 mL of deionized water to fill the vials to the 4 mL mark. The third
-1 standard consisted of 3.0 mL of 1.0 mmol c L NaCl, 1.0 mL of 0.4 N nitric acid and 4 % acetic acid mixture, and 2 drops of gelatin. The standards were titrated and the valued were recorded.
33 The electrode was cleaned as described earlier. From the standards a correction factor was calculated. 1.0 to 3.0 mL of each unknown sample depending on the amount of chloride present was taken and titrated using the coulometric chloride titration in order to for the concentration to fall within the standards. The samples were prepared by pipetting
2.0 mL of the unknown sample and adding 2 drops of gelatin and filling the vial to the
4.0 mL mark with 0.1 N Nitric acid/10 % acetic acid solution. The samples were then titrated and the reading was recorded. After every ten samples additional standards were run.
Wet Digestion
We weighed out 0.40 grams of plant material that had been oven dried at 80 °C and ground and thoroughly homogenized into a 50 mL test tube. Following this step we added 10.0 mL of concentrated 70 % nitric acid to each sample and swirled the flask gently so that all the plant material came in contact with the acid and allowed it to sit in hood for 24 hours as part of the predigesting process. The following day the samples were placed on a VELP Scientifica DK heating digester block and allowed to digest overnight at 105 oC for 600 minutes, then at 140 oC for 420 min, 155 oC for 120 min, 165 oC for 120 min, 180 oC for 120 minutes and if the sample had not digested completely then at 200 oC for an additional 60 minutes. Once the sample was colorless and reduced to about 1.0 mL in volume it was removed from the heating block and while the sample was still warm, 50.0 mL of deionized water were added and mixed to incorporate the entire sample.
34 Once the sample had cooled completely then it was filtered through a Whatman No. 42 filter paper and then an additional 49.0 mL of deionized water were added to the test tube to get the entire digest out. Then the filtered sample was collected in a plastic bottle and was thoroughly mixed and a subsample of 10.0 mL was furthered filter through a 0.2 µm filter into a 15 ml falcon tube for the elemental analysis to be done by ICP- OES.
Inductively Coupled Plasma–Optical Emission Spectroscopy (ICP-OES) is capable of determining the content of plant macro and micronutrients as well as heavy metals in a single sample (Huang and Schulte, 1985). Complete dissolution of the sample into a liquid matrix prior to analysis is necessary to ensure that the total content is analyzed (Huang and Schulte 1985; Ali, Zoltai, and Radford 1988). The advantages of
ICP-AES include simultaneous multi-element determinations from major to ultra-trace levels, acceptable precision and accuracy, small inter-element effects, and capability of performing rapid analyses have been discussed by several authors. These attributes make
ICP-OES a fast, and accurate elemental determination for plant analysis.
Statistics
The SAS software package (SAS Institute, 2004) was used for one and two way analyses of variance (ANOVA), followed by Tukey and Tukey-Kramer pair wise comparison of means. Differences with an alpha = 0.05 or less were considered significant. ANOVA and Tukeys test was used to analyze multiple variables using the general linear model proc GLM.
35 CHAPTER IV:
Results
Irrigation Water
-1 Our irrigation water in the salt treatment had a chloride concentration of 4.94 mmol c L which is above the threshold from Ayers and Westcot 1985. They stated that the
-1 maximum permissible chloride in the irrigation water without leaf injury was 4 mmol c L for a Guatemalan Rootstock (Hass is 85% Guatemalan). All of the rootstocks in the salt treated rows experienced leaf burn, with Dusa, PP40, and R0.05 ranking as the varieties having the least amount of leaf burn.
Figure 2. Dusa in control row Figure 3. Dusa in a salt treated -1 -1 EC w = 0.5 dS m row EC w = 1.5 dS m
36
Figure 4. R0.07 in control row Figure 5. R0.07 in a salt treated -1 -1 EC w = 0.5 dS m row EC w = 1.5 dS m
After 23 months of irrigating with an electrical conductivity of 1.5 dS m -1, there was a distinct difference in the amount of leaf damage between rootstocks. In figure 2 and 3 we notice there is minimal difference between the Dusa tree in the control and that in the salt treated row. In contrast R0.07 (Figure 4 and 5) experienced leaf burn in the control and was completely defoliated (dead) in the salt treated row.
37
Eighty-nine water samples were collected and the volume of water applied was calculated. The EC, pH, and chloride were also measured in the water samples to ensure that we meet target values in the irrigation water.
The amount of irrigation water applied in rows 26, 28, 29, and 32 was monitored and recorded using water meters, confirming essentially equal application of irrigation water to all rows (Figures 6-9). Rows 26 and 29 were salt treated rows, and row 28 and
32 were control rows.
Irrigation applied Row 26 Irrigation applied Row 28 May- Aug 2015 May- Aug 2015 1000 1000 800 800 600 600 400 400
Irrigation (gal) 200 200 Irrigation (gal) 0 0 5/1/15 6/1/15 7/1/15 8/1/15 5/1/15 6/1/15 7/1/15 8/1/15
Figure 6. Water meter data for row 26 Figure 7. Water meter data for row 28
Irrigation applied Row 29 Irrigation applied Row 32 May- Aug 2015 May- Aug 2015 1000 1000 800 800 600 600 400 400 Irrigation (gal) 200 Irrigation (gal) 200 0 0 5/1/15 6/1/15 7/1/15 8/1/15 5/1/15 6/1/15 7/1/15 8/1/15
Figure 8. Water meter data for row 29 Figure 9. Water meter data for row 32
38 The leaching fraction of the irrigation water was calculated using the formula below:
LF = Water applied – ET c Water applied
Where LF is the leaching fraction, the Water applied is the water measured by the water meters plus precipitation, and the ET c is the crop evapotranspiration, calculated by the crop coefficient, K c, multiplied by a reference evapotranspiration, ET o, obtained from
CIMIS. The K c used to calculate crop evapotranspiration was .55 this value was obtained from FAO 56 for avocados with no ground cover and was ratio based on 50 % canopy cover for avocados based on a 50 % canopy cover for citrus.
Leaching fraction over time 1
0.8 Salt rows Control rows 0.6
0.4
Leaching Fraction Leaching 0.2
0 Nov. Dec. Jan. Feb. March. April. May. June. 2014 2014 2015 2015 2015 2015 2015 2015
Figure 10. Average leaching fraction tracked monthly in both the salt treated rows -1 -1 (EC w = 1.5 dS m ) and in the control (EC w = 0.5 dS m ).
39 Soil Sampling Analysis
Soil samples were collected prior to the initiation of the salt treatment. The sampling hole was filled and the gravimetric water content of the soil samples was obtained. Saturation paste extracts and 1: 1 soil to water extraction method were used to analyze the soil for pH, electrical conductivity, moisture content and chloride. The 1:1 soil extracts were converted to EC e using the following equation
EC e = EC 1:1 *(SP 1:1 /SP e)
Where EC e = electrical conductivity of the saturation extract, EC 1:1 = the electrical conductivity of the 1 to 1 extracts, SP 1:1 = the saturation percentage of the 1 to 1, SP e= the saturation percentage of the saturation extracts. Since the texture was relatively constant we applied this conversion to all the data.
Soil cores were collected in 15 cm intervals down to 90 cm, parallel to the trees
30 cm away from the tree trunk. In December of 2013 the two treatment plots were very similar in both electrical conductivity and chloride concentration, Figure 11 and 12, as expected because there was no salt treatment being applied.
Mean EC e December 2013 Mean Cl- December 2013 0 0 10 10 EC 0.5 dS/m 20 20 30 30 EC 1.5 dS/m 40 EC 0.5 dS/m 40 50 50 EC 1.5 dS/m
Depth (cm) 60 Depth (cm) 60 70 70 80 80 90 90 0 5 10 15 0 5 10 15 20 EC (dS/m) Cl- (mmol L-1) e c Figure 11. Mean electrical conductivity Figure 12. Mean chloride concentration of soil extracts collected in December in soil extracts collected in December 2013 2013 40
Between December 2013 and March 2014, a total of 288 soil samples were
collected in 15 centimeter increments down to 90 centimeters and analyzed for EC e,
pH, moisture content and chloride. In March we can see that the salts and chloride are
accumulating in the top 20 cm of the soil profile in the salt treated rows (Figure 13
-1 and 14). The average EC e at 20 cm in the control rows was 4.33 dS m and 8.22 dS
m-1 in the salt treated rows at that same depth. After three months after salinization,
-1 -1 the average chloride content was 4.46 mmol c L and 11.55 mmol c L for the control
and salt rows respectively at 20 cm depth.
Mean EC e March 2014 Mean Cl- March 2014 0 0 10 10 20 20 30 30 40 EC 0.5 dS/m 40 50 50 EC 0.5 dS/m 60 EC 1.5 dS/m
Depth (cm) Depth (cm) 60 EC 1.5 dS/m 70 70 80 80 90 90 0 5 10 15 0 5 10 15 20 EC (dS m -1) -1 e Cl- (mmol c L ) Figure 13. Mean electrical conductivity Figure 14. Mean chloride concentration
of soil extracts collected in March 2014 in soil extracts collected in March 2014
Between August 2014 through September 2014, a total of 730 soil samples were collected. A total of eight holes were sampled from each row (row 19-36), four soil samples were collected 30 cm from the tree and the other four in between the trees.
41 The soil samples were collected in 15 cm increments down to 75 cm for a total of 5 depths per hole and analyzed for ECe, pH, moisture content, and chloride. The average electrical conductivity of these soils at around 7 cm down to 65 cm in depth ranged from
3.62 – 2.02 dS m -1 and 6.06 – 3.15 dS m -1 in the control and salt rows respectively (Figure
15). The average chloride content around 7 cm down to 65 cm in depth ranged from 7.56
-1 -1 to 4.84 mmol c L and 16.41 to 8.78 mmol c L in the control and salt rows respectively with depth (Figure 16).
Mean ECe August 2014 Mean Cl- August 2014 0 0 10 10 20 20 30 30 40 EC 0.5 dS/m 40 50 50 60 EC 1.5 dS/m EC 0.5 dS/m
Depth (cm) Depth (cm) 60 70 70 EC 1.5 dS/m 80 80 90 90 0 5 10 15 0 5 10 15 20 EC (dS m -1) -1 e Cl- (mmol c L )
Figure 15. Mean electrical conductivity Figure 16. Mean chloride concentration of soil extracts collected in August 2014 in soil extracts collected in August 2014
On June 15, 2015, 720 soil samples were collected. A total of eight holes were sampled from each row (Row 19-36) down to 75 cm for a total of 40 soil samples per row. These samples were processed and analyzed. The surveys indicated that soil electrical conductivity in the salt treatments is just above that in the control with an electrical conductivity at the surface of 3.20 dS m-1 and 4.10 dS m -1 for the control and salt respectively (Figure 17).
42
There was also a decrease in the chloride concentration in the top 20 cm from a
-1 -1 concentration of 16.41 mmol c L in August 2014 to a 10.07 mmol c L concentration in
June 2015 in the salt treated row, but the trend of chloride decreasing with depth continued (Figure 18).
Mean ECe June 2015 Mean Cl- June 2015 0 0 10 10 20 20 30 30 40 EC 0.5 dS/m 40 50 50 60 EC 1.5 dS/m EC 0.5 dS/m
Depth (cm) Depth (cm) 60 70 70 EC 1.5 dS/m 80 80 90 90 0 5 10 15 0 5 10 15 20 EC (dS m -1) Cl- (mmol L-1) e c
Figure 17. Mean electrical conductivity Figure 18. Mean chloride concentration of soil extracts collected in June 2015 in s oil extracts collected in June 2015
Based on the soil profiles above, the leaching fraction of the soil salinity was calculated below the rootzone at 40 cm using the following calculation:
LF = EC w
EC sw
Where LF is the leaching fraction, EC w is the electrical conductivity of the irrigation water, and EC sw is the electrical conductivity of the soil water.
43 Control Rows Salt Treated Row
Leaching Fraction Cl-1:1 0.06 0.25
Leaching Fraction EC 1:1 0.10 0.22
Table 4. Calculated leaching fraction based on the chloride and the electrical conductivity of the 1 to 1 extracts below the rootzone at 40 cm in both the control (0.5 dS m -1 ) and the salt treated rows(1.5 dS m -1 ).
Based on the calculated leaching fraction in the soil at field moisture content the salts are getting pushed down and the electrical conductivity and the chloride remains about constant below 40cm.
The difference in the leaching fractions between the control and salt treated rows can be explained based on the transpiration. The plants in the control are able to take up more water therefore decreasing the leaching fraction while those in the salt treatment are leached further down.
Soil Temperature
Soil temperature measurements were taken on Jan 9, 2015 and Jan 12, 2015.
These measurements were needed to correct soil resistivity surveys to a standard temperature of 25 o C. Soil temperature increased with depth and ranged between 12.4 oC to14.4 oC. This correction was done using the equation from Samouelian et al., 2005
o o Ϭt= Ϭ25 C [1+α (T-25 C)]
o Where Ϭt is the conductivity at the experiment temperature, Ϭ25 C the conductivity at 25 oC, and α is the correction factor equal to 2.02%.
44 Electrical Resistivity
Electrical resistivity surveys provide us with detailed depth information about the soil salinity in the individual rows and related with each tree. Soil EC a has become one of the most reliable and frequently used measurements to characterize field variability for application to precision agriculture due to its ease of measurement and reliability
(Rhoades et al., 1999a, Rhoades et al., 1999b and Corwin and Lesch, 2003).
Between August to September 2014, electrical resistivity surveys were performed along the individual rows within the experiment. During this time, one survey was collected at every row from row 19 through row 36 inclusively, with electrodes placed 30 cm from tree row line. Each survey consisted of 50 electrodes spaced one meter apart using a Wenner configuration. This data compliments the comprehensive soil survey of all rows conducted in the same time period
Each survey consisted of 50 electrodes running parallel to the tree rows. The first survey, Figure 19, is row 28 a control row and the second, Figure 20, is row 29 a salt treated row after 5 months of salinization surveyed in June 2014.
Figure 19. Electrical resistivity survey for Row 28, a control row irrigated with water -1 with an EC of 0.5 dS m . EC in the figure is expressed in apparent soil ECa showing soil EC profile in June 2014.
45 We can see that row 28, a control row, is not accumulating any salts in the soil profile since the electrical conductivity is low.
Figure 20. Electrical resistivity survey showing differences in soil apparent electrical conductivity in a salt treated row 29, irrigated with water with an EC of 1.5 dS m -1. EC in the figure is expressed in apparent soil EC a showing soil EC profile in June 2014.
In row 29, a salt row, we notice higher electrical conductivities in the top half meter.
In July 2015, 18 soil electrical resistivity surveys (one in each row) were completed.
The first survey is row 28 a control row and the second is row 29 a salt treated row.
Figure 21. Electrical resistivity survey EC a in control row 28 showing soil EC a profile in June 2015.
46 Figure 22. Electrical resistivity survey, ECa, in salt row 29 showing a higher soil EC profile in June 2015 as compared to the control row.
The apparent soil electrical conductivities ranged between 5 to 63 millisiemen/meter (mS/m). In Figure 21 and 22 we can see the effect of salinity on individual tree and the salt leaching in the top 60 cm. Apparent electrical conductivity is determined by water content, soil texture, and soil water conductivity. It is necessary to establish the soil properties that most significantly influence the EC a measurements within a field in order to establish the soil properties that are influencing the apparent conductivity (Corwin and Lesch, 2005).
Plant Ion Analysis
Observations in citrus and avocado orchards on saline soils in the Rio Grande
Valley showed that all varieties of avocados under cultivation, including Waldin (West
Indian race), Itzamna (Guatemalan race), Lula (West Indian-Guatemalan hybrid), Fuerte
(Guatemalan-Mexican hybrid) and Jalna (Mexican race), showed considerably more leaf burning than adjacent trees of grapefruit and orange (Cooper and Gorton, 1950).
47 There was, however, a wide range in extent of salt injury of the various avocado varieties on the same rootstock; Waldin, Lula, and Itzamna showed less salt injury than the Fuerte and Jalna.
Salt injury is associated with high soil salinity and a high chloride content of the leaves, just as in citrus (Cooper and Gorton, 1950). This experiment shows under field conditions the influence of rootstock on the concentration of chloride and sodium and other elements in the leaves since rootstocks can impart salt tolerance to the scion of trees, usually by limiting the excessive accumulation of chloride (Cl) and sodium (Na) from the scion (Bañuls et al., 1990).
Leaf samples were collected in October of 2013, 2014, and 2015. Twenty fully expanded leaves were sampled from each tree from terminals that were not fruiting or flushing. Samples were weighed, washed, oven dried, reweighed, digested and subsequently analyzed for calcium, magnesium, sodium, potassium, phosphorus, sulfur, chloride, iron, copper, manganese, and zinc by ICP OES. Chlorides were determined by weighing 0.100 grams of ground sample reacted with 25 milliliters of 0.1 N nitric acid and 10 % acetic acid solution overnight. They were then analyzed by amperiometric chloride titrator.
48 In October 2013, 266 leaves were collected and analyzed for calcium, magnesium, sodium, potassium, phosphorus, sulfur, and chloride.
Ca Mg Na K P S Cl mmol/kg mmol/kg mmol/kg mmol/kg mmol/kg mmol/kg mmol/kg R0.06 487.94 149.93 52.24 379.14 36.07 99.16 139.21 Dusa 628.36 188.23 55.41 272.15 39.00 83.29 103.64 PP4 463.34 160.39 62.93 321.75 42.28 87.24 159.10 PP14 546.81 163.37 53.55 340.26 31.95 86.61 226.56 PP24 525.53 157.88 56.35 350.55 30.31 78.65 154.89 PP45 504.82 161.26 56.35 408.12 34.57 110.88 179.67 R0.07 503.65 132.90 60.91 342.89 32.08 70.69 152.29 R0.16 497.59 146.18 53.35 372.87 37.22 96.67 179.00 R0.18 607.20 214.30 53.04 342.30 37.01 80.87 163.50 R0.05 572.18 167.18 51.74 317.08 35.40 83.73 76.60 Thom 501.67 147.87 68.70 335.25 35.57 92.56 212.40 R0.17 540.85 192.81 54.60 338.65 37.50 84.71 187.40 PP 40 532.09 179.79 56.13 335.78 31.75 84.03 138.79 Table 5. Average leaf ion composition collected October 2013
49
In October 2013, the leaf samples were fairly similar in sodium concentration. The
salt treatment had not yet been implemented at this time (Figure 23).
Average Sodium Concentration in Leaves Oct. 2013 100 90 80 a 70 a a a a a a 60 a a a a a a 50 40 Na Na Na (mmol/kg) 30 20 10 0
Figure 23. Average sodium concentration in leaves per rootstock collected October 2013. Rootstocks with different letters dignify significant difference according to Tukey test at alpha = 0.05.
The sodium concentration in the leaves ranged between 51.74 mmol kg -1 for
R0.05 to 68.70 mmol kg -1 in Thom (Figure 23). There were no significant differences in the amount of sodium in the leaves of the different rootstocks.
50 Chloride Concentration in Leaves Oct. 2013 300
250 a a 200 ab ab ab abc abc abc abc 150 bc bc cd Cl
Cl(mmol kg-1) Cl(mmol 100 d
50
0
Figure 24. Average chloride concentration in leaves per rootstock collected in October 2013. Rootstocks with different letters dignify significant difference according to Tukey test at alpha = 0.05.
In October 2013 leaf samples varied in chloride concentration between 76.60 mmol kg -1 for R0.05 to 226.56mmol kg -1 in PP14. Prior investigators (Ben-Ya'Acov,
1970; Downton, 1978; Gustafson et al., 1970; Kadman, 1963 and 1964; Oster et al. 1985) have reported that the mechanism of salinity tolerance appears to include reduced transport of Cl- and exclusion of Na+ by the rootstock. Based on these preliminary analysis we identified R0.05 as having low sodium and chloride concentrations and Dusa as having low chloride concentrations. R0.05 was significantly different in the accumulation of chloride in the leaves as compared to the other 12 rootstocks.
Leaf samples from 109 trees (10-15 leaves/tree) were collected in October 2014 after 9 months of application of the full strength saline water.
51 The leaves were analyzed for calcium, magnesium, sodium, potassium, phosphorous, sulfur, iron, copper, manganese, zinc, and chloride (Table 6 and 7).
Control Ca Mg Na K P S Fe Cu Mn Zn mmol mmol mmol mmol mmol mmol mmol mg Mg mg kg -1 kg -1 kg -1 kg -1 kg -1 kg -1 kg -1 kg -1 kg -1 kg -1 R0.06 357.06 134.44 28.07 461.34 27.72 81.64 21.58 4.82 89.01 15.87 Dusa 535.94 171.02 5.91 337.29 37.29 81.94 73.52 6.02 102.39 27.70 PP4 377.03 127.04 138.53 352.62 49.76 100.19 99.38 10.80 67.47 33.87 PP14 407.32 158.30 34.61 439.60 39.18 87.32 56.56 4.93 220.45 29.61 PP24 396.99 156.68 5.98 407.49 30.18 76.86 55.67 4.19 92.77 28.72 PP45 352.91 144.60 20.46 454.20 34.57 99.28 53.66 6.23 70.54 21.35 R0.07 375.45 116.22 84.44 529.21 39.81 75.60 82.88 7.00 74.29 29.37 R0.16 363.85 127.42 26.18 488.92 30.17 89.61 52.29 6.53 67.57 35.31 R0.18 415.48 161.39 8.65 471.57 45.58 85.91 48.19 7.95 65.55 34.58 R0.05 544.31 165.85 6.18 339.98 31.12 83.61 38.07 5.52 74.74 19.88 Thom 323.08 117.90 37.08 415.18 34.79 78.33 31.66 4.81 79.75 23.09 R0.17 430.76 174.38 41.76 407.82 38.45 81.79 52.90 6.71 91.68 29.16 PP40 378.48 163.05 6.13 388.34 28.85 75.35 54.80 5.01 84.10 32.92 Table 6. Average leaf ion composition in control rows collected in October 2014
Salt Ca Mg Na K P S Fe Cu Mn Zn mmol mmol mmol mmol mmol mmol mmol mg mg mg kg -1 kg -1 kg -1 kg -1 kg -1 kg -1 kg -1 kg -1 kg -1 kg -1 R0.06 153.62 89.77 846.69 902.40 142.69 163.16 25.75 20.57 81.64 64.45 Dusa 379.91 151.00 290.70 434.76 56.63 88.77 41.65 9.78 65.60 35.30 PP4 217.45 121.30 378.08 781.62 124.31 113.78 74.61 19.76 79.57 58.42 PP14 414.53 119.53 498.48 894.23 111.28 125.51 152.78 13.88 62.85 67.65 PP24 355.41 159.59 60.49 516.46 32.69 80.73 21.04 4.88 60.64 39.25 PP45 * R0.07 139.19 78.70 863.13 1107.53 121.63 124.33 104.65 16.68 26.88 56.63 R0.16 * R0.18 399.68 152.34 196.06 564.11 65.80 81.45 98.72 10.80 52.68 42.28 R0.05 445.31 164.48 269.32 599.06 73.63 99.96 136.27 11.04 72.37 57.58 Thom 266.32 91.61 447.91 355.69 44.19 76.87 38.94 7.46 120.70 33.79 R0.17 288.68 103.30 665.03 881.35 171.05 117.38 105.18 20.50 110.08 88.05 PP40 352.25 197.39 7.81 570.89 39.43 85.14 45.53 6.41 67.26 37.70 Table 7. Average leaf ion composition in salt rows collected in October 2014 *No leaf samples were collected for these rootstock because they were dead.
52 Cooper and Gorton (1950) found that leaf-burning of avocados at the Hoblitzelle Ranch was associated with a large accumulation of chlorides in the leaves and this chloride accumulation was greater in the Mexican and Mexican-Guatemalan hybrid varieties than in the Guatemalan varieties. We found that some of the leaves samples collected in the salt row had some chlorosis and leaf burn. We also sampled leaves in the fresh row for comparative purposes. After eight months of irrigating with a salt treatment two rootstocks died PP45 and R0.16, and consequently there were no leaves to collect.
Average Sodium Concentration in Leaves Oct. 2014 1000 EC 0.5 dS/m 900 EC 1.5 dS/m 800 700 600 500 400 300 Na kg-1) Na (mmol 200 100 0
Figure 25. Average sodium concentration in leaves per rootstock collected October 2014.
There was large variability in the sodium concentrations among the different rootstocks. This is in agreement with Maas 1990, who stated that the rootstocks influence the salt tolerance of fruit trees and vine crops because of differences in absorption and transport of sodium and chloride.
53 The rootstock that accumulated the least amount of sodium after one year of irrigation with salt water were PP40 (7.8 mmol kg -1), PP24 (60.5 mmol kg -1), R0.18
(196.1 mmol kg -1), R0.05 (269.3 mmol kg -1), and Dusa (290.7 mmol kg -1), in increasing salt concentration order (Figure 25).
Average Chloride Concentration in Leaves Oct. 2014 900 800 EC 0.5 dS/m 700 EC 1.5 dS/m 600 500 400 300
Cl (mmol kg-1) Cl 200 100 0
Figure 26. Average chloride concentration in leaves per rootstock collected October 2014.
The rootstock that accumulated the least amount of chloride after one year of irrigation with salt water were PP40 (454.3 mmol kg-1), PP24 (490.0 mmol kg -1), Dusa
(506.3 mmol kg -1), R0.18 (528.6 mmol kg -1), and R0.05 (574.0 mmol kg -1) in increasing salt concentration (Figure 26). Slight to severe visual injuries, predominantly leaf tip necrosis, have been reported for mature field grown avocado trees with leaf chloride concentrations from 0.5 % to 1.5 % dry weight (Bingham et al., 1968). This range translates to 141 mmol kg -1 to 423 mmol kg -1 of chloride, thus all of the rootstocks in the control row fall within this range.
54 There were no visual leaf injuries in the fresh row to any of the rootstocks. We identified leaf injury and necrosis in the salt treated row which had chloride concentration well above this range. Oster and Arpaia 1992 found differences among rootstocks for plant appearance based on chloride and sodium content of the plant tissue.
In October 2015, 103 leaves were collected and analyzed for calcium, magnesium, sodium, potassium and chloride.
Control Ca Mg Na K Cl -1 -1 -1 -1 -1 mmol kg mmol kg mmol kg mmol kg mmol c L R0.06 495.60 171.79 13.94 411.37 232.50 Dusa 590.64 188.65 15.03 311.88 191.25 PP4 458.70 167.97 21.45 373.73 217.20 PP14 418.17 154.38 14.18 398.39 249.60 PP24 437.50 162.66 16.42 369.50 229.00 PP45 499.81 184.93 14.06 436.06 201.20 R0.07 321.06 120.03 13.25 434.48 226.20 R0.16 452.59 157.18 13.20 436.99 247.40 R0.18 567.94 197.07 14.67 385.80 306.00 R0.05 566.72 169.80 14.96 370.23 141.48 Thom 385.84 132.38 15.36 423.31 246.40 R0.17 431.56 161.19 14.06 427.52 242.60 PP40 493.64 195.07 14.19 365.66 169.20 Table 8. Average leaf ion composition in control rows collected in October 2015
55
In the salt treated rows only seven rootstocks survived.
Salt Ca Mg Na K Cl
-1 -1 -1 -1 -1 mmol kg mmol kg mmol kg mmol kg mmol c L Dusa 463.44 185.28 22.28 467.36 282.50 PP4 329.59 136.30 67.95 484.09 357.50 PP24 422.69 199.06 18.75 434.68 346.00 R0.18 408.53 184.50 15.26 520.38 403.20 R0.05 545.99 210.96 11.36 287.79 274.67 Thom 298.28 125.36 84.41 591.96 469.00 PP40 435.77 187.99 14.30 400.23 335.25 Table 9: Average leaf ion composition in salt rows collected in October 2015
Of the seven surviving rootstocks, Thom accumulated the most sodium in the leaves with 84.4 mmol kg -1, PP4 with 68.0 mmol kg -1, and Dusa with 22.3 mmol kg -1 in decreasing order.
56 Average Sodium Concentration in Leaves Oct. 2015
100 90 EC 0.5 dS/m 80 EC 1.5 dS/m
) 70 -1 60 50 40
Na kg Na (mmol 30 20 10 0
Figure 27. Average sodium concentration in leaves per rootstock collected October 2015.
The sodium concentration in the leaves ranged between 13.2 mmol kg -1 for R0.07 and R0.16 to 21.4 mmol kg -1 for PP4 in the control rows. In the salt treated rows the sodium concentration ranged from 11.4 mmol kg -1 for R0.05 to 84.4 mmol kg -1 in Thom
(Figure 27). In citrus, the rootstock ‘Trifoliata’( Poncirus trifoliate ) accumulated Na mainly in the roots, but not in leaves, and exhibits a higher shoot growth rate than
‘Cleopatra’ mandarin( Citrus reticulate ), which has poor mechanisms of Na exclusion from leaves (Walker, 1986). Our results agree that the rootstocks that were able to exclude sodium from the leaves performed better.
57 Relative Sodium in Avocado Leaves* Sampled Oct. 2015
7 6
5 Na 4
Na Na ratio 3
2 1
0
Figure 28. *These data represent the ratio of the treated (1.5 dS m -1) divided by the control (0.5 dS m -1) sodium concentration ratio in leaves collected October 2015.
Thom accumulated the most sodium followed by PP4 while R0.05 had the lowest sodium ratio (Figure 28).
58 Average Chloride concentration in Leaves Oct. 2015 600
500 EC 0.5 dS/m
) EC 1.5 dS/m
-1 400
300
200 Cl (mmol kg Cl
100
0
Figure 29. Average chloride concentration in leaves per rootstock collected October 2015.
In the salt treated row those that accumulated the most amount of chloride were
Thom with 469 mmol kg -1, R0.18 with 403 mmol kg -1, PP4 with 358 mmol kg -1 in decreasing order. The chloride concentration in the leaves ranged between 142 mmol kg -1 for R0.05 to 306 mmol kg -1 for R0.18 in the control rows. In the salt treated rows the chloride concentration ranged from 248 mmol kg -1 for R0.05 to 469 mmol kg -1 for Thom
(Figure 29).
59 Rootstock Survival
PP14, PP45, R0.06, R0.07, R0.16, and R0.17 all died after about 20 months of being irrigated with 1.5 dS m -1 irrigation water. The rootstocks that had the highest survival rate were PP40 and R0.05 with a survival rate of 66.7 %, followed by R0.18 with
62.5 %, and DUSA with 42.9 % (Figure 30). These were also the rootstocks that accumulated the least amount of chloride and sodium in the leaves in October of 2014.
There were no significant differences in the electrical conductivities of the extracts of the different rootstock located in the same treatment and that is why an average of the ECe`s was taken based on the treatment (control vs salt treated) and not on the individual rootstocks. Since the average rootzone salinity was fairly constant throughout the treatments we are able to attribute the salt tolerance to ion toxicity and the rootstocks ability to translocate sodium and chloride to the leaf.
60 Avocado Survival Percentage per Rootstock in the Salt treatments 2015 100
90
80 66.7 66.7 62.5 70
60 42.9 50
Survival (%) 40 22.2 22.2 30 20.0
20
10 0.0 0.0 0.0 0.0 0.0 0.0
0
Figure 30. Avocado survival percentage by rootstock in salt treatment. After being irrigated with the salt treatment for 20 months only seven varieties survived in the salt rows.
61 Plant Ion interaction
One way the salinity disrupts the mineral relation of plants is by reducing the nutrient availability by competing with major ions (Na +, Cl -). Potassium is an essential factor in protein synthesis, glycolytic enzymes, and photosynthesis; and osmotic cell expansion and turgor driven movement (Niste 2014); Higher K/Na ratios will also improve the resistance of the plant to salinity (Hu et al 2005).
Table 10. Salt Treatment
Rootstock K/Na
PP40 73.1
PP24 8.54
R0.18 2.87
R0.05 2.22
PP4 2.06
PP14 1.79
Dusa 1.49
R0.17 1.32
R0.07 1.28
R0.06 1.06
Thom 0.79
Table 10. Potassium -Sodium ratio for each rootstock in the salt treated rows.
62 Based on the leaf samples collected in Oct 2014, the rootstocks with the highest
K/Na ratios were also some of the most salt tolerant rootstock. This included PP40, PP24,
R0.18, and R0.05 (Table 10).
Calcium and Sodium in Leaves sampled Oct. 2015
Ca 700 Na 600
) 500 -1 400 300 (mmol kg 200 100 0
Figure 31.The graph represents the calcium and sodium concentration in the salt treated (1.5 dS m -1) rows.
There is an inverse relationship between the accumulation of calcium and sodium, as the calcium increases the sodium decreases (Figure 31). Mickelbart et al. 2007 found that in the first (oldest) flush, calcium concentrations decreased with increasing salinity confirming this inverse relationship.
63 Avocado Yield
All avocados were harvested from all 252 trees in April 2014.
Yield in Control 2014
30 Weight of Fruit 25 Number of Fruit 20 15 10 Avg fruit Avg 5 0
PP4 Weight of Fruit PP14 PP24 PP45 R0.16 R0.17 R0.06 Thom R0.07 R0.18 PP40 R0.05 Dusa
Figure 32. Yield based on the average number of fruit and weight for each rootstock in rows irrigated with an EC of 0.5 dS m -1 harvested February 2014.
The top 5 rootstock with the highest average yield in the control treatment were
Dusa with 14 fruit, PP40 with 14 fruit, R0.05 with 13 fruit, R0.18 and R0.07 with 12 fruit in decreasing order. The heaviest fruit was produced on average by PP40 with 5.57 kg,
R0.05 with 3.88 kg, R0.18 with 3.51 kg and Dusa with 3.47 kg (Figure 32).
64 Yield under Salt Treament 2014
30 Weight of Fruit 25 Number of Fruit 20 15
Avg fruit Avg 10 5 0
PP4 Weight of Fruit PP14 PP45 PP24 Thom R0.07 R0.16 R0.17 PP40 R0.05 R0.18 R0.06 Dusa
Figure 33. Yield based on the average number of fruit and weight for each rootstock in rows irrigated with an EC of 1.5 dS m -1 harvested February 2014.
The top 5 rootstock with the highest average yield in the salt treated rows was
Dusa with 25 fruit, R0.06 with 12 fruit, R0.18 with 11 fruit, PP40 with 10 fruit and R0.05 with 7 fruit. And the heaviest fruit was produced on average by Dusa with 9.68 kg, followed by PP40 with 3.87 kg, R0.06 with 3.19 kg and R0.17 with 2.61 kg (Figure 33).
65 Yield in Control 2015
60 Weight of Fruit 50 Number of Fruit 40 30 20 Avg fruit Avg 10 0
Weight of Fruit PP4 PP14 PP24 DUSA PP40 PP45 R0.16 R0.17 R0.18 R0.05 R0.06 R0.07 Thom
Figure 34. Yield based on the average number of fruit and weight for each rootstock in rows irrigated with an EC of 0.5 dS m -1 harvested February 2015.
The top 3 rootstock with the highest average yield in the control treatment were
R0.05 with 50 fruit, PP24 with 48 fruit, and PP40 with 43 fruit. The heaviest fruit was produced on average by Dusa with 7.33 kg, followed PP24 with 6.64 kg and PP40 with
5.91 kg (Figure 34).
66 Yield under Salt Treatment 2015
30 25 Weight of Fruit 20 Number of Fruit 15
Avg fruit Avg 10 5 0
Weight of Fruit PP4 PP14 PP24 DUSA PP40 PP45 R0.16 R0.17 R0.18 R0.05 R0.06 R0.07 Thom
Figure 35. Yield based on the average number of fruit and weight for each rootstock in rows irrigated with an EC of 1.5 dS m -1 harvested February 2015.
The top 3 rootstocks with the highest average yield in the salt treated rows were
R0.05 with 29 fruit, DUSA with 15 fruit, and PP40 with 11 fruit. The heaviest fruit was produced on average by R0.05 with 2.83 kg, followed by DUSA with 2.34 kg, and PP40 with 1.50 kg (Figure 35).
67 Avocado Yield 2015*
0.6
0.5
0.4
0.3
0.2 Relative fruit wt. (kg) fruit Relative 0.1
0
Figure 36.* These data represent the ratio of the treated (1.5 dS m -1) divided by the control (0.5 dS m -1) yield ratio for the harvest in February 2015.
Differences in accumulation were then compared with differences in tolerance, as measured by performance in yield in a saline treatment relative to yield in the non-saline treatment. The top rootstocks with the highest relative yield in the salt row were R0.18,
R0.05, DUSA and PP40 in decreasing order (Figure 36). High soil salinity and chloride toxicity cause reductions in fruit yield and tree size, lowered leaf chlorophyll content, decreased photosynthesis, poor root growth, and leaf scorching (Mickelbart et al. 2007).
Thom and PP4 had some of the highest sodium and chloride levels in the leaves and were also some of the rootstocks with the lowest relative yield.
68 We also looked at the correlation between the yield in 2015 and the sodium and chloride concentration in the leaves collected that same year.
Avocado Yield vs. Sodium in 2015
60
50
40
30 No. Fruit No. 20
10
0 0 10 20 30 40 50 60 70 80 90 Na (mmol kg -1)
Figure 37. Average avocado yield vs average sodium concentrations in leaves for 2015. These data points are averages for each rootstock which are color coded. The open circle denotes those in the salt treatment and the closed circles denote those in the control rows.
69 Avocado Yield vs Chloride in 2015 Rootstock 60 R0.06 DUSA 50 PP4 PP14 40 PP24 PP45 R0.07 30 R0.16Salt R0.18 No. Fruit No. 20 R0.05Fresh Thom 10 R0.17 PP40 0 0 100 200 300 400 500
Cl (mmol kg -1)
Figure 38. Average avocado yield vs average chloride concentrations in leaves for 2015. The data points are averages for each rootstock which are color coded. The open circle denotes those in the salt treatment and the closed circles denote those in the control rows.
Based on these graphs (Figures 37-38) we can see that chloride is a better marker for salt tolerance and that there is a reduction in yield at about a chloride concentration 280 mmol kg -1. Cooper 1951 found that the salt tolerance of avocado, oranges and grapefruit is closely related to the Cl- accumulation properties of the rootstock. The varieties that least had the least amount of chloride in the leaves had the highest yield.
70 Plant Growth Parameters
The tree growth was monitored annually by measuring the trunk circumference 5 cm above the tree graft.
Trunk Diameter 2015
35 EC 0.5 dS/m 30 EC 1.5 dS/m 25 20 15
Diameter (cm) Diameter 10 5 0
Figure 39. Average trunk diameter of each rootstock in the salt (1.5 dS m -1) and control (0.5 dS m -1) rows 2015.
There are significant differences between the trunk diameter of those in the salt and those in the control rows and between rootstock which follows the finding of Oster and Arpaia 1992 that tree height, trunk diameter and fresh weight decreased with increasing salinity. However there were no significant differences using Tukey`s test alpha = 0.05 between the interaction of rootstock and treatment (Figure 39).
71 When we looked at the salt treated row exclusively we found significant differences. In the salt rows there are significant differences based on rootstock with
PP40, R0.18, and R0.05 having the largest trunk diameter (Figure 40).
Trunk Diameter 2015
30 a EC 1.5 dS/m a a 25 ab b b b 20 b b b b b b
15
Diameter (cm) Diameter 10
5
0
Figure 40. Average trunk diameter of each rootstock in the salt treated rows 2015.
72 We also looked at the ratio between rootstocks in the salt and control row (Figure
41). The trees that had the highest relative trunk diameter were also the ones that produced the most fruit this included PP40, R0.05, DUSA, and R0.18.
Trunk Diameter Ratio* 2015
1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 Relative Trunk Diameter Trunk Relative 0.2 0.1 0
Figure 41. *These data represent the ratio of the salt treated (1.5 dS m -1) divided by the control (0.5 dS m -1) trunk diameter.
73 The trees that had the largest canopy volume in the salt treated rows were also the ones that had the highest relative fruit weight (fruit weight in salt treatment/fruit weight in control).
Tree Canopy 2015
14 EC 0.5 dS/m EC 1.5 dS/m 12
10 ) 3 8
6 Canopy (m Canopy 4
2
0
Figure 42. Average tree canopy of each rootstock in the salt (1.5 dS m -1) and control (0.5 dS m -1) rows measured in 2015.
The rootstocks with the largest canopy volume in the treated rows were R0.05,
R0.18, PP40, and Dusa (Figure 42). There was no significant difference between the mean canopy in the control and salt treated rows using Tukey`s test with alpha = 0.05.
74 CHAPTER V:
Summary
Avocado yield is directly affected by soil water electrical conductivity. In December of 2013 the salt and control rows were very similar in electrical conductivity and chloride concentration, as expected because salt treatment had not yet been applied. The August
2014 data, after seven months of application of treatment show that sodium and chloride were accumulating in the top 20 cm of the soil profile in the salt treatment rows irrigated
-1 -1 with 1.5 dS m . The average EC e at around 20 cm in the control rows was 2.59 dS m
-1 while the EC e was 4.65 dS m in the salt treated rows at that same depth.
The average chloride content of the saturation extract from the 20 cm depth, was 5.64
-1 -1 mmol c L and 13.32 mmol c L in the control and salt rows respectively.
We collected soil samples and leaves from the plot prior to the initiation of the experiment and analyzed them for major ions. The mean chloride content of the leaves varied from 42 to 120 mmol kg -1 (dry weight) depending on the rootstock. This preliminary analysis showed that the rootstock variety expected to be more salt tolerant was either a chloride excluder or did not translocate chloride to the leaves, a trait that is expected for more salt tolerant plant varieties. From the preliminary leaf analysis we saw that R0.05 and Dusa were chloride excluders which turned out to have some of yields and highest survival rates.
The leaf analyses from October 2014 showed that PP14 and R0.07 were chloride accumulators in both the control and salt water treated rows.
75 All varieties accumulated more chloride in the salt treatment than in the controls as expected (approximately twice as much). The chloride concentration in the leaves varied widely, ranging from 454 mmol kg -1 PP40 to 785 mmol kg -1 PP14 in the salt treated rows, while the chloride in the control ranged from 212 mmol kg -1 for R0.05 to 394 mmol kg -1 for PP14. The rootstocks with the highest sodium concentrations in the leaves were
R0.07 and R0.06 in the salt treated rows. R0.07 also had high concentration of sodium in the control treatment. The sodium content in the individual trees showed great variability among varieties. Sodium ranged between 6 mmol kg -1 PP24 to 106.5 mmol kg -1 PP4 in the control rows and between 7.8 mmol kg -1 PP40 to 863 mmol kg -1 R0.07 in the salt treated rows.
Based on the leaf analyses from October 2015, we observed that PP40 and R0.05 and
Dusa accumulated the least amount of chloride in both the control and salt water treatments. Based on these leaf analyses we observed that PP4 is a sodium accumulator in both the control and salt water treatments, while Thom is a sodium accumulator in salt water treated rows. The sodium concentration in the leaves ranged between 13.2 mmol kg -1 for R0.16 and R0.07 to 21.4 mmol kg -1 for PP4 in the control rows. In the salt treated rows the sodium concentration ranged from 11.4 mmol kg -1 for R0.05 to 84.4 mmol kg -1 for Thom. If we rank the rootstocks based on the sodium accumulation in the leaves in the control rows (0.5 dS m -1), in increasing concentration: R0.07=R0.16 =R0.17 76 We observed that Thom had high sodium and chloride concentrations in the salt water treatments. In the salt treated rows those that had high amounts of chloride were Thom 469 mmol kg -1, R0.18 403 mmol kg -1, PP4 358 mmol kg -1 in decreasing order. The chloride concentration in the leaves ranged between 142 mmol kg -1 for R0.05 to 306 mmol kg -1 for R0.18 in the control rows. In the salt treated rows the chloride concentration ranged from 248 mmol kg -1 for R0.05 to 469 mmol kg -1 for Thom. The sodium in the control row was not a good indicator of the rootstocks that would be the most tolerant. The sodium concentrations in the salt rows showed that R0.05, PP40, and R0.18 accumulated the least amount of sodium in increasing order. If we rank the chloride concentration in the leaves of the control treatment by rootstock then in increasing order we get: R0.05 EC of 1.5 dS m -1 if we rank the chloride concentration in the leaf by rootstock in increasing order we have: R0.05< Dusa< PP40 77 CHAPTER VI: Conclusions: The salt movement during the salinization process was recorded by selected intensive soil sampling, and soil resistivity profiling using the SuperSting ® 56 electrode resistivity imaging system. Irrigation management can be evaluated on a tree by tree basis using the SuperSting ® resistivity technology. This monitoring technique can provide avocado growers with information on the production of avocados under salt-affected soils. Resistivity imaging potentially enables detailed monitoring of salinity distributions in the subsurface if calibrated against soil samples. The salinization process must be monitored after initial application of salts with both soil sampling and with the SuperSting ® imaging system. Multielectrode resistivity profiling has shown to be a useful tool for field study evaluations of soil leaching. Salt tolerance of avocados is complicated because it is a salt sensitive species and ion toxicities cause detrimental effects on the growth and yield. Avocados are especially susceptible to leaf injury caused by the toxic accumulation of sodium and chloride in the leaves. There was large variability among the rootstocks between the sodium and chloride concentrations in the leaves. This variability was imparted by the rootstocks ability to translocate chloride and sodium to the leaves. We looked at 13 different rootstocks since different rootstocks absorb sodium and chloride at different rates in order to identify the tolerance that occurs within each individual rootstock. 78 The influence of these 13 different rootstocks on the chloride concentrations and other elements in the leaves was studied because California growers are faced with having to use irrigation water high in salts, especially high in sodium and chloride. Leaf analysis proved to be a useful method in identifying salt sensitive rootstock such as R0.06, R0.07, PP14, and R0.17 as having high chloride and sodium concentrations in the leaves and therefore being the least salt tolerant which had one hundred percent mortality in the salt treated rows. 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