Floral analysis in fruit crops: A potential tool for nutrient constraints diagnosis

C H A P T E R

13 Floral analysis in fruit crops: A potential tool for nutrient constraints diagnosis Chengxiao Hua,b,*, Zhihao Donga,b, Yuanyuan Zhaoa,b, Wei Jiaa,b, Miaomiao Caia,b, Ting Zhana,b, Qiling Tana, Jinxue Lia,b a

College of Resources and Environment/Micro-element Research Center/Hubei Provincial Engineering Laboratory for New Fertilizers, Huazhong Agricultural University, Wuhan, People’s Republic of China b Key Laboratory of Horticultural Plant Biology (HZAU), MOE, Wuhan, People’s Republic of China *Corresponding author. E-mail: [email protected]

O U T L I N E 1 Plant response to nutrient concentration

157

2 Nutrient deficiency symptom diagnosis

158

3 Floral analysis in fruit crops: Sampling and analysis 159 3.1 Sampling 160 3.2 Handling and analysis of leaf samples 162

4.1 Result interpretation 162 4.2 Nutrient constraint diagnosis and recommendation 162 5 Floral analysis in fruit crops: Combining use of the soil testing 169 References

171

4 Floral analysis in fruit crops: Results interpretation and nutrient constraints diagnosis 162

1 Plant response to nutrient concentration As we know, inadequate fertilizer particularly chemical fertilizer use usually results in yield losses, lower product quality, weaker resistance to stresses (such as low temperature, drought, disease, pests, etc.), and adverse environmental impact (such as nonpoint contamination). Scientific fertilizer recommendation for crop is becoming increasingly important due to: (i) growing demand in the global world market for food quality, (ii) increasing fertilizer particularly chemical fertilizer costs, (iii) increasing awareness of environmental problems caused by agriculture, and (iv) increasing climate changes such as drought, flooding, and heavy storms. For scientific fertilizer recommendation, correct diagnosis of plant nutrient deficiency is important and should be an integrative approach to crop production (Petra Marschner, 2012). Plant growth response curve (Fig. 13.1) shows the relationship between plant growth or production yield and nutrient concentration in tissue (such as leaf ); the diagnosis of plant nutrition status depends on this curve of diverse growing plant. There are four phases as follows: (i) Under severe deficiency, rapid increase in yield with added nutrient can cause a small decrease in nutrient concentration; this is called the Steenberg effect, which results from dilution of the nutrient in the plant by rapid plant growth. (ii) The nutrient concentration in the plant below which a yield response to added nutrient occurs means that critical levels or ranges vary among plants and nutrients but occur somewhere in the transition between nutrient deficiency and sufficiency. (iii) The nutrient concentration range in which added nutrient will not increase yield but can increase nutrient concentration means luxury consumption is often used

A.K. Srivastava, Chengxiao Hu (eds.) Fruit Crops: Diagnosis and Management of Nutrient Constraints https://doi.org/10.1016/B978-0-12-818732-6.00013-7

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© 2020 Elsevier Inc. All rights reserved.

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13. Floral analysis in fruit crops: A potential tool for nutrient constraints diagnosis

FIG. 13.1 Plant growth response curve to concentration of nutrient in tissue.

Yield or biomass, % of maximum Luxury consumption

Excessive nutrient concentration

Critical levels or ranges

Steenberg effect

Concentration of nutrient in plant tissue as leaf (Dry basis)

to nutrient absorption by plant that does not influence yield. (iv) When the concentration of essential or other elements is high enough to reduce plant growth and yield means excessive nutrient concentration can cause an imbalance in other essential nutrients that can also reduce yield. Depending on careful sampling, analysis and tests that are correlated with plant response, the roles of soil and plant analysis in quantifying crop nutrient requirement are that of high yield, high profit and good quality, friendly environment.

2 Nutrient deficiency symptom diagnosis Several techniques are commonly employed to assess the nutrient status of plant: (i) nutrient deficiency symptoms of plants, (ii) analysis of tissue from plants growing on the soil, (iii) soil analysis, and (iv) biological tests in which the growth of either higher plants or certain microorganisms is used as a measure of soil fertility. Growing plants act as integrators of all ecosystem factors, visual characteristic symptoms may appear while a plant is lacking a particular nutrient, or plants exhibit a host of symptoms reflecting various disorders that can impact their health, vigor, and productivity to varying degrees. Identifying symptoms correctly is also an important aspect of nutrient management, as inappropriate fertilizer applications or other actions can be highly costly and sometimes detrimental to the ecosystem or themselves. Nutrient deficiency symptom diagnosis of plants or visual evaluation of nutrient stress requires systematic approach described as you can see in Table 13.1. TABLE 13.1

Nutrient deficiency symptom diagnosis of crop.

1. Occurs only on new growth while persist in mature growth

1.1 Leaves with uniform color, growth decreasing with shorter internodes and bushy appearance

1.2 Leaves with chlorosis patterns

1.1.1 Leaves are large and dark green; shoots are long and willowy at early stages but have short and bushy secondary growth after long shoots dieback; gum blisters form along vigorous shoots at base of each petiole; multiple buds or sprouts form at the nodes; fruits show gum in tips of locules and brownish eruption on peel surface (exanthema)

Cu

1.1.2 New leaves are pale green but turning yellow green with expanding; growth is sparse

N

1.1.3 New growth is drab green, lusterless, sparse with misshapen leaves; fruit has gum deposits in the albedo peel layer

B

1.2.1 Leaves are pointed and narrow with reduced size and sharply contrasting bright yellow mottling

Zn

1.2.2 Approximately normal leaves in size and shape with pale green mottle over whole leaf, or marbled mottle with dark green color, and crooked veins network with light color in between

Mn

1.2.3 Approximately normal leaves with feather-like straight veins on light green or yellow background, or leaves turning totally yellow and reduced size with twigs dying on the outer end of branches

Fe

3 Floral analysis in fruit crops: Sampling and analysis

TABLE 13.1

159

Nutrient deficiency symptom diagnosis of crop.—cont’d

2. Occurs on mature leaves with normal or nearly normal young leaves

2.1 Fading of chlorophyll in localized area and gradual enlargement with time

2.2 Fading of chlorophyll not localized

2.1.1 Chlorophyll fading starts from the leaf basal between midrib and lateral leaf margin, spreads usually outward with green “wedge” at the base of leaf, and turns inward and yellow wedge, or all leaf fade to golden bronze color

Mg

2.1.2 Chlorophyll fading starts along lateral leaf margin, spreads inward about halfway to midrib, and forms irregular front margin

Ca

2.1.3 Chlorophyll fading starts as blotches in distal half of leaf, blotches spread and coalesce with pale yellow firstly and deepened to bronze, foliage is drab, and fruit becomes smaller but of good quality

K

2.1.4 Chlorophyll fading occurs in spots distributed randomly over whole leaf; brown centers with yellow or orange halo develop from spots that range from one-quarter to one-half inch in diameter but appear only in the fall

Mo

2.2.1 Leaves fade to dull green then orange yellow eventually and turn burned tips and spots extremely. Fruits become coarse, spongy, and hollow-centered with thickened peel and abovenormal acid

P

2.2.2 Leaves fade to pale green then yellow color with whitish veins; fruits become sparse and pale-colored extern and internally with good quality but low juice concentration

N

Nutrient-deficiency symptoms appear only when the nutrient supply or concentration is so low that the plant can no longer function properly, so it should be used only as a supplement to other diagnostic techniques, the reasons are that the visual symptom may be caused by more than one nutrient, deficiency of one nutrient may be related to an excessive quantity of another, deficiency symptoms are difficult to distinguish among disease, insect, or herbicide damage which resemble micronutrients deficiency sometime in field, somehow a visual symptom may be caused by more than one factor. In contrast, symptoms resulted from diseases and pests are nonsymmetric or randomly positioned for individual plants and in a field particularly at the advanced phase of infections. Some differences are shown in Table 13.2. Visual diagnosis of plant nutrient deficiency provides sometimes enough information for fertilization recommendation as fertilizer type and amount, especially applying foliar spraying micronutrient (B, Zn, Fe, and Mn) or Mg. But sometimes, the visual diagnosis is inadequate for making fertilizer recommendation, it is necessary to acquire additional information including soil pH, water and soil testing, and application of fertilizers, pesticides, etc., especially further chemical and biochemical leaf analysis.

3 Floral analysis in fruit crops: Sampling and analysis The use of chemical analysis of plant material for nutrient diagnosis scientifically depends on the hypothesis that the plant growth or yield is related closely to nutrient concentration in shoot or tissue dry or fresh matter. The plant nutritional status is oftentimes better reflected by the element concentration in leaves than the other organs; thus, the leaves are usually used for plant analysis. In fact, the leaf or floral analysis is more important and efficacious for fruit crops than field crops or annual crops; the tissue sap analysis and soil test can be used for annual crops nutrient diagnosis and TABLE 13.2

Distinguish between pathological diseases and nutrient deficiency.

Difference

Pathological disease

Nutrient deficiency

Process

Developing from a center

Developing in a scattered way

Relation to the soil type

Easily appeared in high fertility soil

Related to soil type significantly, such as acid soil

Relation to the climate

Easily appeared as it’s cloudy and wet

Be relevant to drought, water logging, and low temperature

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13. Floral analysis in fruit crops: A potential tool for nutrient constraints diagnosis

fertilizer recommendation because of crop nutrient mainly relying on soil fertility or soil supply, while soil test cannot reflect but only floral analysis does reflect the nutritional status of fruit tree because of the prior year nutrient storage in plant being the main sources for the following growth. Numerous researches have been carried out in the past to develop and improve leaf analysis for identifying nutritional constrains and subsequently the fertilizer recommendation in fruit trees (Srivastava and Singh, 2004). Leaf analysis is a useful tool to detect problems and adjust fertilizer programs for citrus trees because leaf nutrient concentrations are the most accurate indicator of fruit crop nutritional status. Of all nutrient diagnosis methods, leaf tissue testing is useful to evaluate tree nutritional status with respect to most nutrients but is particularly effective for (i) macronutrients, primarily nitrogen (N) and potassium (K), that readily move with soil water and (ii) the micronutrients copper (Cu), manganese (Mn), zinc (Zn), and iron (Fe). Leaf tissue analysis is a much better indicator of the effectiveness of soil-applied fertilizer for these elements than soil analysis. Because citrus is a perennial plant, it is its own best indicator of appropriate fertilization. In addition, if particular elements have not been applied as fertilizer, leaf tissue analysis indicates the availability of those nutrients in the soil, leaves reflect nutrient accumulation and redistribution throughout the plant, so the deficiency or excess of an element in the soil is often reflected in the leaf. An annual leaf tissue sampling program can establish trends in tree nutrition resulting from fertilizer practices carried for several years (Obreza and Morgan, 2008). Considerable research involving citrus leaf testing has established its reliability as a management tool, but sampling guidelines should be followed precisely to insure that analytical results are meaningful. Leaf analysis integrates all the factors that might influence nutrient availability and uptake. Tissue analysis shows the relationship of nutrients to each other. For example, K deficiency may result from a lack of K in the soil or from excessive Ca, Mg, and/or Na. Similarly, adding N when K is low may result in K deficiency because the increased growth requires more K.

3.1 Sampling Samples from field-growing plants are usually contaminated by dust or spraying and need to be washed; however, washing leaf samples may result in different loss among different elements. For example, washing leaves with water for a few minutes resulted in high boron loss due to boron passive diffusion across plasma membranes (Brown et al., 2002), while washing with diluted acids or chelating reagents did not remove the leaves surface contaminants of Fe, Zn, and Cu completely. The greatest challenge in utilizing leaf analysis for nutrient diagnosis purpose is the short-term fluctuations in nutrient concentration frequently; it means the leaf nutrient concentration reflecting deficiency, sufficiency, and toxicity ranges may change with climate factors, environmental factors and plant genotype, and growth stage of whole plant and single leaf. For example, the K concentration on DW basis declines with plant age, but the K concentration in plant cell sap remains relatively constant during growth (Petra Marschner, 2012). Procedures for proper sampling, preparation, and analysis of leaves have been standardized to achieve meaningful comparisons and interpretations. If done correctly, the reliability of the chemical analysis, data interpretation, fertilization recommendations, and adjustment of fertilizer programs will be sound. Therefore, considerable care should be taken from the time leaves are selected for sampling to the time they are received at the laboratory for analysis. 3.1.1 Leaf sample timing Leaf samples must be taken at the correct time of year because nutrient concentrations within leaves continuously change. For citrus, as leaves age from spring to fall, N, P, and K concentrations decrease; Ca increases; and Mg first increases and then decreases. However, leaf mineral concentrations are relatively stable from 4 to 6 months after emergence in the spring. The best time to collect 4–6-month-old spring flush leaves is July and August (Obreza and Morgan, 2008). If leaves are sampled later in the season, summer leaf growth can easily be confused with spring growth. Leaf sampling time adopted for K analysis on different citrus-growing countries (Srivastava and Singh, 2004) can be seen in Table 13.3. 3.1.2 Leaf sampling technique Leaves are collected from nonfruiting spring branches (6 months old) of the citrus trees in mid-September (Obreza et al., 1992) each year. A sampled citrus grove block or management unit should be no larger than 20 acres. The sampler should make sure that the selected leaves represent the block being sampled. Each leaf sample should consist of about 100 leaves taken from nonfruiting twigs of 15–20 uniform trees of the same variety and rootstock that have received the same fertilizer program. Use clean paper bags to store the sample, and label the bags with an identification number that

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3 Floral analysis in fruit crops: Sampling and analysis

TABLE 13.3

Leaf sampling time (months) adopted for K analysis on different citrus-growing countries (Srivastava and Singh, 2004).

Months

Position and cultivar

Country

3–7

Spring cycle leaves from behind the green fruits

United States

4–7

Spring cycle leaves from fruit-bearing terminals

United States

4–7

Spring cycle leaves from nonfruiting terminals

California, United States

4–10

Spring cycle leaves from fruiting terminals

United States

4–10

Nonfruiting terminals of Washington Naval and Valencia

United States

5–7

Nonfruiting terminals of Naval

United States

9–10

Nonfruiting terminals of Valencia

Israel

5–7

Nonfruiting terminals of Valencia and Washington

Morocco

6–7

Fruiting terminals of Clementine mandarin

Corsica

6–7

Fruiting terminals of Valencia

Brazil

4–5

Nonfruiting terminals of Valencia

Carmer, Ivory C.

8–10

Nonfruiting terminals of Valencia

Israel

8–10

Nonfruiting terminals of Valencia

South Africa

5.5–7.0

Fruiting terminals of Valencia

Morocco

7–9

Leaves from fruiting terminals of grapefruit and lemon

South Africa

7–9

Nonfruiting terminals of Valencia

South Africa

4–6

Middle leaves from nonfruiting spring flush terminal of Valencia

Australia

4–7.5

Nonfruiting terminals of kinnow mandarin

India

6–8

Middle leaves from nonfruiting terminals of Nagpur mandarin

India

3–5

Middle leaves from nonfruiting terminals of Acid lime in central India

India

4.5–6.5

Middle leaves from nonfruiting terminals of Acid lime in north-west India

India

5–6

Fruiting terminals of Eureka lemon on Citrus volkameriana

South Africa

can be referenced when the analytical results are received. Avoid immature leaves due to their rapidly changing composition. Do not sample abnormal-appearing trees, such as at the edge of the block or at the end of rows, because they may be coated with soil particles and dust. Do not include diseased, insect-damaged, or dead leaves in a sample. Select only one leaf from a shoot and remove it with its petiole (leaf stem). To minimize soil- and tree-type variability of the sampling grove, the sampling scheme is one area of the nutritional testing process controlled by the individual taking the sample. Thus, the individual needs to ensure that its leaf sample is representative of a particular area trees. For sampling purposes, partition the grove into management units of not more than 20 acres; each unit contains similar soil and scion/rootstock types. For small groves, the entire grove is partitioned into these units with a sample taken from each. For large groves, it is unfeasible to sample the entire grove; indicator block is used as a designated zone within a uniform span of grove from which the sample is taken (e.g., a 20-acre block within a uniform 100-acre span of grove). Aerial photos are useful for designing indicator blocks. The sample results obtained from the indicator block are assumed to represent the entire span, and management decisions made from the sample data are applied to the entire span; the same block should be sampled repeatedly in the succeeding years. A more elaborate approach to citrus leaf tissue sampling is to use the global positioning satellites (GPS) and geographic information system (GIS). Groves sample in a regular, grid-like pattern and record the geographic position of each sample using GPS technology. The analyzed results of the samples are processed with GIS, and contour maps are drawn. According to the maps, the spatial variation of tree nutritional level and areas of high or low nutrition in whole grove can be determined and identified. This method is more expensive than the traditional one but may provide a higher level and more information that can improve management decisions.

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13. Floral analysis in fruit crops: A potential tool for nutrient constraints diagnosis

The leaf position on the tree twig has distinctly significant effect on mineral element distribution; to avoid these positional effects, sampled leaves should be collected from the north, east, south, and west directions and the same height of the tree’s periphery. In special cases as sampling for diagnosing disorders of trees growth, leaf samples should be collected from both affected trees and normal trees. Trees selected for comparison sampling should be of the same age, scion type, and rootstock. If possible, confine the sampling area to trees that are close to each other.

3.2 Handling and analysis of leaf samples Protect leaves from heat and keep them dry, place them in a refrigerator for overnight storage if they cannot be washed and oven dried the day of collection. For macronutrient analysis, leaves do not need to be washed; if accurate micronutrient analysis is desired, the leaves will need to be washed. Leaf samples are firstly inactivated at 105°C for 30 min and then drying at 70°C to constant weight in a ventilated oven. The oven-dried leaves should be ground to fine powder with an agate mortar or ground prototype. Leaves that have been sprayed with micronutrients for fungicidal (Cu) or nutritional (Mn and Zn) purposes should not be analyzed for those elements because it is almost impossible to remove all surface contamination from sprayed leaves. For accurate Fe and B or other micronutrient determinations, leaf samples require handwashing that is best done shortly after collection before they dehydrate. For micronutrient determinations, rub the leaves between the thumb and forefinger while soaking them in a mild detergent solution, and then, thoroughly rinse with pure water. It is difficult to remove all surface residues, but this procedure removes most of them. If samples require handwashing for accurate element determination, it is best done when the leaves are still in a fresh condition. Laboratories do not normally handwash leaves, so washing should be finished by the person who collected the sample at that time. When the sample arrives at the laboratory, the following steps are typically taken: (1) Dry and finely grind the leaves; (2) weighted sample is either digested in strong acid (for N analysis) or ashed in furnace (for all other elements); (3) the element concentration of solution originated from the digested or ashed are measured; (4) nutrient concentrations are expressed as either percentage or milligram per kilogram in the tissue. Procedures for plant tissue analysis are usually the same among laboratories because the entire amount of each nutrient in the leaves is measured, thus results from different laboratories can be directly compared.

4 Floral analysis in fruit crops: Results interpretation and nutrient constraints diagnosis 4.1 Result interpretation As total, each nutrient concentration of leaf sample is measured in laboratory, with standard sample simultaneously, there should be no difference in leaf analysis results among laboratories. To interpret analysis results, compare the values with the leaf analysis standards, for example, citrus tree leaf shown in Tables 13.4 and 13.5; these standards are based on long-term field observations and experiments conducted in different countries with different citrus varieties, rootstocks, and management practices and are used to gauge citrus tree nutrition level throughout the world. Well-defined categories of classification for citrus leaf tissue analysis values are “deficient,” “low,” “optimum,” “high,” and “excess.” As shown in Tables 13.4 and 13.5; remember that this classification applies only to the standard age leaf sample taken from mature trees as described earlier and is not valid for young, nonbearing trees.

4.2 Nutrient constraint diagnosis and recommendation Actually, the first commercial citrus or fruit tree growers understood some needs of the macronutrients nitrogen, phosphorus, and potassium for citrus growth. Meanwhile, worldwide researches proved gradually that plants needed nutrients in addition to nitrogen, phosphorus, and potassium to grow properly. In 1939, A.F. Camp and B.R. Fudge showed that secondary nutrients and micronutrients were needed to grow citrus. Included examples were deficiency symptoms of copper, zinc, manganese, magnesium, boron, and iron. As early as in 1908, the yellow spot disease in citrus was first reported in Florida, which was rather widespread and caused extensive defoliation and tree death, and till 1951, it was Ivan Stewart and C.D. Leonard who reported that the yellow spot disease in citrus was due to molybdenum deficiency and could be corrected by spraying as little as 1 oz of sodium molybdate per acre. Then,

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4 Floral analysis in fruit crops: Results interpretation and nutrient constraints diagnosis

TABLE 13.4 Guidelines for interpretation of orange tree leaf analysis based on 4–6-month-old spring flush leaves from nonfruiting twigs (Koo et al., 1984). Element

Unit of measure

Deficient

Low

Optimum

High

Excess

N

%

<2.2

2.2–2.4

2.5–2.7

2.8–3.0

>3.0

P

%

<0.09

0.09–0.11

0.12–0.16

0.17–0.30

>0.30

K

%

<0.7

0.7–1.1

1.2–1.7

1.8–2.4

>2.4

Ca

%

<1.5

1.5–2.9

3.0–4.9

5.0–7.0

>7.0

Mg

%

<0.20

0.20–0.29

0.30–0.49

0.50–0.70

>0.70

Cl

%

–

–

<0.2

0.2–0.7

>0.70

Na

%

–

–

–

0.15–0.25

>0.25

Mn

mg/kg

<18

18–24

25–100

101–300

>300

Zn

mg/kg

<18

18–24

25–100

101–300

>300

Cu

mg/kg

<3

3–4

5–16

17–20

>20

Fe

mg/kg

<35

35–59

60–120

121–200

>200

B

mg/kg

<20

20–35

36–100

101–200

>200

Mo

mg/kg

<0.05

0.06–0.09

0.10–2.0

2.0–5.0

>5.0

TABLE 13.5

Leaf analysis standards for mature, bearing citrus trees, which exist from years of experimentation in Florida and California, based on 4–6-month-old spring cycle leaves from nonfruiting terminals.

Element

Unit of measure

Deficient

Low

Optimum

High

Excess

N

%

<2.2

2.2–2.4

2.5–2.7

2.8–3.0

>3.0

P

%

<0.09

0.09–0.11

0.12–0.16

0.17–0.30

>0.30

K

%

<0.7

0.7–1.1

1.2–1.7

1.8–2.4

>2.4

Ca

%

<1.5

1.5–2.9

3.0–4.9

5.0–7.0

>7.0

Mg

%

<0.20

0.20–0.29

0.30–0.49

0.50–0.70

>0.70

Cl

%

–

–

0.05–0.10

0.11–0.25

>0.25

Na

%

–

–

–

0.15–0.25

>0.25

Mn

mg/kg

<17

18–24

25–100

101–300

>300

Zn

mg/kg

<17

18–24

25–100

101–300

>300

Cu

mg/kg

<3

3–4

5–16

17–20

>20

Fe

mg/kg

<35

35–59

60–120

121–200

>200

B

mg/kg

<20

20–35

36–100

101–200

>200

Mo

mg/kg

<0.05

0.06–0.09

0.10–2.0

2.0–5.0

>5.0

the fruit leaf analysis and fertilizer recommendation were globally accepted and developed rapidly while particularly focused on the diagnosis standard or norm of leaf analysis. The leaf nutrient norms are developed with great variety of diagnostic methods using different citrus cultivars; the differences in diagnostic methods apart from climate condition of growing area and nutrient uptake characteristic of citrus cultivars are the major contributory factors toward variation in reference values being recommended in relation to yield. It’s not easy to establish the diagnosis standard or norm of fruit tree leaf analysis. Embleton published “Citrus fertilization” and “Leaf analysis as a diagnostic tool and guide to fertilization” in The Citrus Industry (Vol. 3) edited by Reuther as early as 1973, particularly discussed the relationship among soil-nutrient-plant and explained the leaf analysis with detail. What Embleton had introduced was rapidly cited in Soil Testing and Plant Analysis edited by Walsh and Soil Science Society of American in the same year. After that, lots of scientists have done researches and setup diagnosis standards or norms of citrus leaf analysis as we can see in Table 13.6.there are some variations in standards

164 TABLE 13.6

13. Floral analysis in fruit crops: A potential tool for nutrient constraints diagnosis

Leaf macronutrient norms (%) for diagnosis in different countries and area of citrus.

Resources

N

P

K

Ca

Mg

Chapman (1949a,b)

2.20–3.16

0.09–0.182

0.38–1.12

–

–

Chapman (1960)

2.20–2.70

0.12–0.18

1.0–1.7

3.0–6.0

0.3–0.6

Rodriguez and Gallo (1961)

2.20

0.12

1.00

3.00

0.30

Smith (1966)

2.5–2.7

0.12–0.16

1.2–1.7

3.0–4.5

0.30–0.49

Jorgensen and Price (1978)

2.4–2.6

0.14–0.16

0.9–1.2

3.0–6.0

0.23–0.60

Takidze (1981)

2.5–2.8

0.19–0.20

1.5–1.7

2.5–3.0

0.30–0.35

Koo et al. (1984)

2.5–2.7

0.12–0.16

1.2–1.7

3.0–4.9

0.30–0.49

Early Satsuma

2.71–3.2

0.16–0.20

1.01–1.6

2.01–4.5

0.21–0.30

General Satsuma

2.91–3.4

0.16–0.20

1.01–1.6

2.01–4.5

0.21–0.30

Summer orange, ponkan

2.51–3.0

0.16–0.20

0.71–1.3

2.01–4.5

0.21–0.30

Navel orange

3.11–3.6

0.16–0.20

1.41–2.0

2.01–4.5

0.21–0.30

Wang (1985)

3.0–3.5

0.15–0.18

1.0–1.6

2.5–5.0

0.30–0.60

Zhuang et al. (1986)

2.7–3.3

0.12–0.15

1.0–1.8

2.3–2.7

0.25–0.38

Ko and Kim (1987)

2.97

0.15

1.48

3.57

0.34

Wang (1987)

3.0–3.5

0.15–0.18

1.0–1.6

2.5–5.0

0.30–0.60

Wang et al. (1988)

2.7–3.3

0.12–0.15

1.0–1.8

2.3–2.7

0.25–0.38

Gallasch and Pfeiler (1988)

2.4–2.69

0.14–0.17

0.70–1.49

–

–

New Zealand, 1989

2.4–2.6

0.14–0.16

0.9–1.2

3.0–6.0

0.25–0.6

Koto et al. (1990)

2.4–2.6

0.13–0.15

–

3.4–4.8

0.20–0.29

Zhuang et al. (1991)

2.5–3.1

0.14–0.18

1.4–2.2

2.0–3.8

0.32–0.47

Zhou et al. (1991)

2.75–3.25

0.14–0.17

0.7–1.5

3.2–5.5

0.2–0.5

Wang et al. (1992a,b)

2.5–3.0

0.12–0.18

1.0–2.0

2.0–3.5

0.22–0.40

Ponkan

2.9–3.5

0.12–0.16

1.0–1.7

2.5–3.7

0.25–0.50

Guanxi pomelo

2.5–3.1

0.14–0.18

1.4–2.2

2.0–3.8

0.32–0.47

Sweet orange

2.5–3.3

0.12–0.18

1.0–2.0

2.0–3.5

0.22–0.40

Valencia orange

2.0–2.4

0.11–0.16

0.95–1.5

3.5–5.5

0.30–0.55

Navel

2.4–2.8

0.11–0.16

0.9–1.1

3.5–5.5

0.30–0.55

Grapefruit

2.3–2.6

0.11–0.16

0.9–1.6

3.5–5.5

0.30–0.55

Lemon

1.9–2.2

0.11–0.15

1.1–1.4

3.5–5.5

0.30–0.55

Dettori et al. (1996)

2.15–2.55

0.10–0.12

0.9–1.3

4.4–5.5

0.3–0.4

Perez (1996)

2.5–2.7

0.15–0.18

–

3.0–4.0

0.4–0.5

Beridze (1986)

2.1–2.2

0.15–0.16

0.95–1.23

5.74–6.94

0.27–0.34

Quaggio et al. (1996)

2.3–2.7

0.12–0.16

1.0–1.5

3.5–4.5

0.25–0.40

Zhuang et al. (1997)

2.8–3.3

0.14–0.18

1.4–2.1

2.0–4.0

0.25–0.45

Xie et al. (2014)

2.7–3.2

0.13–0.20

1.2–1.9

2.5–4.0

0.22–0.45

Recommendation norm

2.5–2.9

0.13–0.17

1.0–1.6

2.8–4.5

0.28–0.45

Japan (Yu, 1984)

Wang et al. (1992a,b)

Du Plessis et al (1992)

Data from Srivastava, A.K., Singh, S., 2004. Diagnosis methods, Nutrient Diagnosis and Management in Citrus. National Research Center for Citrus, Nagpur, pp. 3–19.

165

4 Floral analysis in fruit crops: Results interpretation and nutrient constraints diagnosis

or norms among citrus cultivars and planting areas, but it is difficult to distinguish particularly significantly. We use the average or high frequency value as the recommended standards or norms showed as the last row as showed in Tables 13.7 and 13.8; the macronutrient (N, P, K, Ca, and Mg) concentration range of the standards is relatively narrower than the micronutrients (Fe, Mn, Cu, Zn, B, and Mo); the upper limit/lower limit of macronutrients is 1.16, 1.31, 1.60, 1.61, and 1.61 and of micronutrient is 2.00, 4.50, 3.00, 2.80, 3.33, and 10.0, respectively; the lower ratio of the micronutrient means the narrower range; the higher ratio of macronutrient means the wider range and a wider range concentration for plant to tolerate. The lower limit of the nutrient diagnosis norm is often the critical concentration of the deficiency for the plant, while the upper limit is of the excess or toxicity for plant. According to this diagnosis standard, the leaf diagnosis results of nearly whole China’s citrus orchard are showed in Table 13.8; the citrus leaves of more than half sampled orchards are deficient of Ca, Mg, and Zn; all these deficient nutrients shall be suggested to apply. Leaf analysis shows promise of being a reliable guide for nitrogen fertilization practices. An 8-year study in northern San Diego County was started with 11-year-old Fuerte trees. Statistical analyses of the data collected show that the curvature in the graph was highly significant at the 1% level. The trees with nitrogen leaf values below the most productive range were deficient of nitrogen and weakly vegetative, with small sparse and light green to yellow leaves and less new shoot growth. The trees with nitrogen leaf values higher than the most-productive-range were highly vegetative with large, dense, deep green leaves and abundant long new shoot growth. It also indicates that from 100 to 150 pounds of actual nitrogen per acre annually will generally be adequate unless a volunteer or planted cover crop exists in an orchard (Embleton et al., 1960). The interpretation of leaf nutrient levels is based on the promise that there is a significant relationship between the nutrient concentration in leaf, plant growth, and fruit yield with the aim at predicting fertilizer requirement of fruit TABLE 13.7

Leaf micronutrient norms (mg/kg) for diagnosis in different countries and areas of citrus.

Resources

Fe

Mn

Cu

Zn

B

Mo

Chapman (1949a,b)

70–200

20–80

4–100

20–80

20–100

0.1–0.2

Chapman (1960)

60–150

25–100

5.1–15.0

25–100

50–200

0.10–3.0

Smith (1966)

50–120

25–49

5–12

25–49

36–100

0.10–1.0

Jorgensen and Price (1978)

12–60

10–25

5–10

25–100

–

–

Koo et al. (1984)

60–120

25–100

5–16

25–100

36–100

0.10–1.0

Wang (1985)

50–120

25–100

4–10

25–100

–

–

Wang (1987)

50–120

25–100

4–10

25–100

30–100

–

Gallasch and Pfeiler (1988)

50–129

–

6–15

25–60

–

–

New Zealand, 1989

60–120

25–100

5–10

25–100

30–100

Koto et al. (1990)

40–46

14–23

3.7–10

23–30

17–19

–

Zhuang et al. (1991)

60–140

15–140

8–17

24–44

15–50

–

Zhou et al. (1991)

60–170

20–40

4–8

13–20

40–110

–

Wang et al. (1992a,b)

90–160

20–100

4–18

25–70

25–100

–

Ponkan

50–140

20–150

4–16

20–50

20–60

–

Guanxi pomelo

60–140

15–140

8–17

24–44

15–50

–

Sweet orange

90–160

20–100

4–18

25–70

25–100

–

Dettori et al. (1996)

100–150

17–37

5–7

19–43

68–85

–

Quaggio et al. (1996)

50–120

35–50

–

35–50

36–100

0.10–1.0

Perez (1996)

30–80

25–90

–

30–90

–

–

Zhuang et al. (1997)

50–160

20–100

5–18

20–50

25–100

–

Xie et al. (2014)

50–200

20–150

5–25

20–50

35–150

0.05–1.0

Recommendation norm

60–120

20–90

5–15

25–70

30–100

0.1–1.0

Wang et al. (1992a,b)

Data from Srivastava, A.K., Singh, S., 2004. Diagnosis methods, Nutrient Diagnosis and Management in Citrus. National Research Center for Citrus, Nagpur, pp. 3–19.

166 TABLE 13.8

13. Floral analysis in fruit crops: A potential tool for nutrient constraints diagnosis

The leaf diagnosis and soil testing results of China’s citrus orchards (not published, more than 2000 leaf and soil samples were tested). Deficient low (%)

High excess (%)

Nutrient

Soil

Leaf

Soil

Leaf

N

58.80

35.89

4.43

20.04

P

39.77

13.32

20.74

24.92

K

36.78

43.32

28.08

19.90

Ca

57.51

54.06

25.98

12.47

Mg

65.10

61.49

20.31

4.96

Fe

22.96

7.61

79.06

49.53

Mn

16.93

12.45

47.53

51.19

Zn

22.72

72.66

25.86

0.96

Cu

22.63

20.78

60.75

43.25

B

81.02

32.51

0.00

9.14

tree. An extensive survey of 108 Khasi mandarin orchards located in humid tropical climate of northeast India covering 590 km2 from 50 locations of 8 states was carried out during 2001–05. The optimum values of leaf nutrient concentration in relation to fruit were fixed through multivariate quadratic correlation and regression based on pooled data of both the states considering the similarity in growing conditions and cultural practices. Linear coefficient of correlation and regression analysis were used to test the soil properties governing the fruit yield and quality. The different nutrient concentration in leaf showed significant difference when divided in various yield levels, except Fe and Mn, while fruit yield was more strongly correlated with N (r ¼ 0.708, P ¼ .01), P (r ¼ 0.697, P ¼ .01), Ca (r ¼ 0.817, P ¼ .01), Cu (r ¼ 0.519, P ¼ .01), and Zn (r ¼ 0.793, P ¼ .01) than K (r ¼ 0.436, P ¼ .01), Fe (r ¼  0.309, P ¼ .05), and Mn (r ¼  0.296, P ¼ .05). The leaf nutrient optimum values using multivariate quadratic regression analysis were estimated as 2.32% N, 0.10% P, 1.92% K, 2.26% Ca, 0.30% Mg, 158.4 mg/kg Fe, 82.0 mg/kg Mn, 12.2 mg/kg Cu, and 30.3 mg/kg Zn in relation to fruit yield of 46.3 kg/tree according to regression equation (Singh et al., 2006), and you can see it in Table 13.9 (Srivastava and Singh, 2002, 2006). Based on the relationship between leaf nutrient content and fruit yield or using multivariate quadratic regression analysis, the leaf nutrient optimum values for diagnosis can be used not only to identify the nutrients constrains but also to predict the fruit yield or sometimes fruit quality.

TABLE 13.9

Leaf nutrient optimum values for diagnosis in citrus related to fruit yield.

Nutrients

Nagpur mandarin

Khasi mandarin

Kinnow mandarin

Mosambi sweet orange

Sathgudi sweet orange

N (%)

1.70–2.81

1.97–2.56

2.28–2.53

1.98–2.57

2.01–2.42

P (%)

0.09–0.15

0.09–0.10

0.10–0.13

0.091–0.17

0.09–0.12

K (%)

1.02–2.59

0.99–1.93

1.28–1.63

1.33–1.72

1.12–1.82

Ca (%)

1.80–3.28

1.97–2.49

2.12–3.12

1.73–2.98

1.93–2.73

Mg (%)

0.43–0.92

0.24–0.48

0.32–0.53

0.32–0.69

0.36–0.53

Fe (mg/kg)

74.9–113.4

84.6–249.0

52.3–89.4

69.5–137.1

53.5–82.1

Mn (mg/kg)

54.8–84.6

41.6–87.6

41.7–76.3

42.2–87.0

48.7–79.3

Cu (mg/kg)

9.8–17.6

2.13–14.4

6.1–10.3

6.6–15.8

3.7–8.9

Zn (mg/kg)

13.6–29.6

16.3–26.6

21.3–28.5

11.6–28.7

16.5–23.2

Yield (kg/tree)

47.7–117.2

31.6–56.3

61.8–140.3

76.6–137.9

81.2–145.3

Data from Srivastava and Singh (2002) and Srivastava, A.K., Singh S., 2006. Diagnosis of nutrient constraints in citrus orchards of humid tropical India. J. Plant Nutr. 29 (6), 1061–1076.

TABLE 13.10

Macronutrient norms (%) for leaf diagnosis of the other fruit tree.

Resources

N

P

K

Ca

Mg

2.0–2.4

0.12–0.25

1.0–2.0

1.0–2.5

0.25–0.80

Peach

2.8–4.0

0.15–0.29

1.5–2.7

1.5–2.2

0.30–0.70

Grape

0.6–2.4

0.14–0.41

0.44–3.0

0.7–2.0

0.26–1.50

Zhu et al. (2008)

1.0–1.7

0.31–0.54

2.3–2.9

0.6–1.1

0.28–0.41

Ontario, Canada

0.7–1.3

0.15–0.40

1.0–3.0

1.0–3.0

0.50–1.50

New Zealand

0.8–1.0

0.21–0.50

1.5–3.5

1.4–2.5

0.31–0.38

Michigan, United States

0.8–1.2

0.16–0.30

1.5–2.5

0.5–1.0

0.30–0.50

New York, United States

0.8–1.2

0.14–0.30

1.5–2.5

1.2–2.0

0.35–0.50

Recommendation

0.8–1.5

0.18–0.41

1.4–2.9

0.9–2.0

0.33–0.80

Li et al. (1987)

2.0–2.6

0.15–0.23

1.0–2.0

1.0–2.0

0.22–0.35

Jiang et al. (1995)

2.24

0.184

1.40

1.39

0.288

Guiyang (2004)

2.31–2.50

0.14–0.17

0.73–0.98

1.7–2.2

0.37–0.43

New Zealand

2.2–2.5

0.15–0.20

1.0–1.4

1.2–1.6

0.25–0.35

American

2.0–2.25

0.2–0.3

1.25–1.75

1.25–1.75

0.25–0.40

France

2.3–2.5

0.16–0.18

1.8–2.0

1.49–2.0

0.22–0.26

Australia

2.0–2.4

0.15–0.20

1.2–1.5

1.1–2.0

0.21–0.25

American

1.8–3.0

0.15–0.40

1.3–2.5

1.5–2.0

0.24–0.40

Italy

2.0–2.6

0.16–0.24

1.3–1.9

1.4–2.0

0.24–0.36

Canada

2.0–2.7

0.15–0.30

1.4–2.2

0.8–1.5

0.24–0.40

Japan

3.4–3.6

0.17–0.19

1.3–1.5

0.8–1.3

0.27–0.40

Hebei of China

2.2–2.9

0.09–0.13

0.85–1.04

1.29–1.55

0.11–0.12

Henan of China

2.0–2.6

0.21–0.27

0.79–1.07

–

–

Shanxi of China

2.3–2.5

0.14–0.17

0.7–1.0

1.7–2.3

0.37–0.43

Shandong of China

2.7–3.2

0.11–0.25

0.6–0.9

0.9–1.4

0.19–0.27

Liaoning of China

2.5–2.9

0.17–0.25

0.95–1.29

0.95–1.75

0.28–0.52

Jiangsu of China

1.5–1.8

0.08–0.10

1.03–1.34

1.25–1.79

0.25–0.40

Recommendation

2.2–2.6

0.15–0.22

1.1–1.5

1.2–1.9

0.25–0.35

Wang et al. (1988)

1.5–2.2

0.12–0.18

0.7–1.4

0.3–0.8

0.18–0.38

1.5–2.0

0.10–0.17

0.4–0.8

0.7–1.7

0.14–0.30

Liu et al. (1986)

>1.7

0.12–0.20

0.6–0.8

1.5–2.5

0.20–0.30

Zhuang et al. (1997)

1.4–1.9

0.10–0.18

0.5–0.9

0.9–2.0

0.13–0.30

Recommendation

1.5–1.9

0.10–0.18

0.5–0.8

1.0–2.1

0.15–0.30

Jamaica

2.5

0.20

2.74

–

–

India, 1981

2.8

0.35

3.10

–

–

Australia, 1986

2.8–4.0

0.20–0.25

3.1–4.0

0.8–1.2

0.30–0.46

Zhang (2001)

3.5–4.3

0.23–0.27

3.7–4.4

0.28–0.44

0.15–0.37

Recommendation

2.8–4.2

0.20–0.30

3.1–4.2

0.8–1.2

0.20–0.40

DECIDUOUS FRUIT TREE Li et al. (1987)a

Pear

Grape，Zhang et al. (2003)

Grape

a

Apple

b

Apple

Apple EVERGREEN FRUIT TREES

C

Litchi Longan

Banana

a

b c

The sampling positions. Apple, leaves in the middle part of new shoot on mid-July to mid-August. Pear, leaves in the middle part of new shoot or brachyplast on mid-July to midAugust. Peach, leaves in the middle part of new shoot on mid-July to mid-August. Grape, petiole of leaf locates in the middle part of new shoot-bearing fruit on July to August. Wang et al. (2018). The sampling position. Banana, the third leaves from the plant roof at heading period. Longan, Litchi, the second to third lobule grows at the second compound leaf from the normal annual shoot top or roof, with 5–7 months and 3–5 months growth, respectively.

168

13. Floral analysis in fruit crops: A potential tool for nutrient constraints diagnosis

TABLE 13.11

Micronutrient norms (%) for leaf diagnosis of the other fruit tree.

Resources

Fe

Mn

Cu

Zn

B

Mo

100

30–60

6–50

20–60

20–50

–

Peach

100–250

35–280

7–25

12–60

25–60

–

Grape

30–100

30–65

10–50

25–50

25–60

–

Zhu et al. (2008)

26–52

230–453

78–206

19–47

–

Ontario, Canada

15–100

20–200

–

–

–

New Zealand

31–100

25–200

5–20

25–50

31–50

Michigan, United States

20–100

30–60

10–20

30–60

25–50

New York, United States

30–100

50–1000

10–50

30–60

25–50

Recommendation

25–100

30–200

10–35

25–60

25–50

Li et al. (1987)

150–290

25–150

5–15

15–80

20–60

–

Jiang et al. (1995)

210

78.1

–

39.6

37.5

–

Guiyang (2004)

120–150

52–80

20–50

24–45

33–37

–

New Zealand

90–150

30–90

6–20

20–50

20–50

–

United States

100–300

50–100

5–20

20–50

25–50

–

France

60–240

50–120

5–12

9–53

25–35

–

Australia

>100

50–100

6–20

20–50

–

–

American

0–300

–

5–20

25–50

–

–

Italy

40–150

>8

>1

>15

–

–

Canada

25–200

20–200

–

15–100

–

–

Japan

–

50–200

10–30

30–50

–

–

Hebei of China

87–110

65–80

15–22

8.4–9.8

–

–

Henan of China

89–120

28–40

3–4

25–35

–

–

Shanxi of China

120–150

52–80

20–50

24–45

–

–

Shandong of China

217–353

121–351

–

24–45

–

–

Liaoning of China

114–176

4–20

60–216

25–49

–

–

Jiangsu of China

274–494

41–99

85–354

40–84

–

–

Recommendation

112–218

45–112

18–60

22–50

25–45

–

Longan

Zhuang et al. (1997)

30–100

40–200

4–10

10–40

15–40

–

Banana

Australia, 1986

70–2200

25–1000

7–20

21–35

20–80

–

Zhang (2001)

145–253

567–2339

12–26

30–86

8–18

–

Recommendation

70–250

25–1000

7–26

21–86

8–80

–

DECIDUOUS FRUIT TREE Li et al. (1987)a

Pear

Grape，Zhang et al. (2003)

Grape

a

Apple

b

Apple

Apple EVERGREEN FRUIT TREES

C

a

b c

The sampling positions. Apple, leaves in the middle part of new shoot on mid-July to mid-August. Pear, leaves in the middle part of new shoot or brachyplast on mid-July to midAugust. Peach, leaves in the middle part of new shoot on mid-July to mid-August. Grape, petiole of leaf locates in the middle part of new shoot-bearing fruit on July to August. Wang et al. (2018). The sampling position. Banana, the third leaves from the plant roof at heading period. Longan, Litchi, the second to third lobule grows at the second compound leaf from the normal annual shoot top or roof, with 5–7 months and 3–5 months growth, respectively.

The nutrient norms for leaf diagnosis of the other fruit crops and recommendation norms are also raised as you can see in Tables 13.10 and 13.11; the norms of several fruit trees are gathered as you can see in Table 13.12. It shows the differences in nutrient requirement among several fruit crops: the banana leaves need higher N, P, and K concentrations; the citrus leaves require higher N, Ca, and Mg contents; the Mg content of grape leaves and the

169

5 Floral analysis in fruit crops: Combining use of the soil testing

TABLE 13.12

Macronutrient (%) and micronutrient (mg/kg) norms for several fruit tree leaf diagnosis. Evergreen fruit tree

Deciduous fruit tree

Nutrient

Citrus

Longan

Banana

Apple

Grape

N

2.5–2.9

1.5–1.9

2.8–4.2

2.2–2.6

0.8–1.5

P

0.13–0.17

0.10–0.18

0.20–0.30

0.15–0.22

0.18–0.41

K

1.0–1.6

0.5–0.8

3.1–4.2

1.1–1.5

1.4–2.9

Ca

2.8–4.5

1.0–2.1

0.8–1.2

1.2–1.9

0.9–2.0

Mg

0.28–0.45

0.15–0.30

0.20–0.40

0.25–0.35

0.33–0.80

Fe

60–120

30–100

70–250

112–218

25–100

Mn

20–90

40–200

25–1000

45–112

30–200

Cu

5–15

4–10

7–26

18–60

10–35

Zn

25–70

10–40

21–86

22–50

25–60

B

30–100

15–40

8–80

25–45

25–50

Mo

0.1–1.0

–

–

–

–

TABLE 13.13

The relationship between fruit quality and yield of citrus (not published, five sweet orange orchards for each yield level were tested in the same area in Yunnan Province).

Fruit (kg/ha)

g/fruit

Peel (g/fruit)

TSS (%)

TA (%)

TSS/TA

Edible rate (%)

>1500

188.1

47.85

10.18

0.67

15.2

74.6

1000–1500

185.9

52.62

9.38

1.05

8.9

71.7

<1000

171.3

43.64

9.31

0.92

10.1

74.5

Cu contents of deciduous fruit trees are higher than the other trees. But it is difficult to distinguish the variations in P and particularly the micronutrient contents among fruit crops, because of the wide range of leaf micronutrient norms, so some more detailed researches are necessary in future. Based on the field investigation, most of the standards were calculated from leaf sample analysis of the higher fruit yield tree group, meaning that the nutrient concentration of the higher yield tree leaves was suitable or the critical range or concentration. How it related to the fruit quality? You can see in Table 13.13 that the higher yield group fruit had higher TSS (total soluble solid in juice) concentration and TSS/TA ratio but lower TA (total acid in juice) content than the lower yield group. It seems that the high fruit yield and quality of fruit crop is consistent, the nutrient concentration of the high yield tree leaves is also suitable for high fruit quality tree, and the leaf analysis or nutrient diagnosis is the available tool for nutrient constraints of fruit yield and quality. The goal in nutrition management is to maintain leaf nutrient concentrations within the optimum range every year. If the interpretation for a particular nutrient is not optimum, various strategies can be used to address the situation as you can see in Table 13.14. The optimum or critical nutrient concentration (CNC) of leaf is one approach of leaf analysis and nutrient diagnosis; due to the problems arising from different CNCs during plant development and due to the importance of nutrient ratios or interactions, the Diagnosis and Recommendation Integrated System (DRIS) was developed by Beaufils in 1973. The system bases on large amount of analysis data on plant nutrient concentrations, which can be used to calculate the optimal nutrient ratios of N/P, N/K, K/(Ca + Mg), etc.; Table 13.15 shows the difference between CNC and DRIS briefly. However, the recommendations of DRIS are not always accurate; particularly, it is not the choosing method for cropping system with wide variations of annual crop species, cultivars, rotations, input, and farming scale.

5 Floral analysis in fruit crops: Combining use of the soil testing Soil testing has been practically utilized in agriculture and horticulture for many years successfully. Its availability is closely related to (i) the extent to the data that can be calibrated with field trials of fertilizer and (ii) the interpretation of

170

13. Floral analysis in fruit crops: A potential tool for nutrient constraints diagnosis

TABLE 13.14

Adjusting a citrus fertilization program based on leaf tissue analysis.

Nutrient

What if it is less than optimum in the leaf? Option could be:

N

1. 2. 3. 4.

P

1. Apply P fertilizer

1. Do nothing or grow green manure

K

1. Increase K fertilizer rate 2. Apply foliar K fertilizer 3. Apply crop straw

1. Decrease K fertilizer rate 2. Check leaf Mg and Ca status

Ca

1. Check soil pH and soil moisture 2. Check tested soil Ca status 3. Consider applying lime or soluble Ca fertilizer depending on soil pH 4. Apply foliar Ca fertilizer

1. Do nothing

Mg

1. Check tested soil Mg status 2. Check soil pH 3. Consider applying dolomitic lime or soluble Mg fertilizer depending on pH 4. Apply foliar Mg fertilizer

1. Do nothing

Micronutrients

1. Check soil pH and adjust if needed 2. Apply foliar micronutrients 3. Include micronutrients in soil-applied fertilizer

1. Check for spray residue on tested leaves 2. Do nothing

Check yield Check tree health Review water management Review and increase N fertilizer rate

What if it is greater than optimum in the leaf? Option could be: 1. Check soil organic matter 2. Review and decrease N fertilizer rate 3. Grow some plants

Revised from Obreza, T.A., Morgan, K.T., 2008. Soil and leaf tissue testing. In: Nutrition of Florida Citrus Trees, second ed. Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences, University of Florida, pp. 24–32.

TABLE 13.15

The difference between CNC and DRIS.

CNC

DRIS

Nutrient concentration varied with growth stage of plant

Optimum value could obtained for each growth stage of plant

Identifying the deficiency or sufficiency of different nutrients, without evaluation for interaction of nutrients

Identifying the demand order of different nutrients

Critical concentration varied with environmental factors

Optimum value may not be affected by environmental factors

the analysis. Soil testing measures organic matter content, pH, and extractable nutrient concentrations that is useful in formulating and improving an enriching soil fertility program, particularly useful when conducted for several consecutive years so that variation trends can be observed. Soil testing mainly reveals the potential or capacity of tested soil supplying nutrients to plant, but does not characterize efficiently or sufficiently the nutrient mobility in the soil. Especially, it commonly fails to display the information of soil structure, microorganism, root development, architecture, etc., which are very important factors affecting plant nutrient uptake under field condition. Similar to leaf analysis, methods to determine organic matter and soil pH are universal, so results should not differ among laboratories. However, soil nutrient extraction procedures can vary from lab to lab. Soil testing uses a wide range of conventional extractive reagents such as dilute acid, salts, complex agents, and water. Several accepted chemical procedures exist that remove different amounts of nutrients from the soil because of the extraction strength or ability of the reagents. To draw useful information from soil tests, consistency in the use of a single extraction procedure from year to year is important to avoid confusion when interpreting the amount of nutrients extracted (Petra Marschner, 2012). Soil extraction procedure does not measure the total nutrient amount in soil, nor does it measure the quantity actually available to fruit trees. A perfect extractive reagent would extract nutrient from the soil nearly the same as the amount available to the plant; it means that the utility of a soil testing procedure depends on how well the extractable values correlate with the nutrient amount that the plant takes up; the process of relating these two quantities is called calibration. Calibration means that nutrient availability to plants increases foreseeing with soil testing value increasing. Low

References

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soil testing values imply that crop will respond to fertilization with particular nutrient; in turn, high soil testing values indicate the soil can supply most of the plant needs, and up to no fertilization required, the soil testing value is called the critical or sufficiency soil testing value, respectively. A soil testing is only useful if it is calibrated with plant response. It’s all known that there is a long-term argument as whether soil testing or plant analysis is more suitable for making fertilization recommendations. As you can see in Table 13.8, only the nutrients of Ca, Mg, Mn, and Cu exhibit the consistency between leaf analysis and soil testing diagnosis. In fruit crops, soil analysis alone is not a satisfactory guide for fertilization recommendation; a citrus grower cannot rely on soil analysis alone to formulate a fertilizer program or diagnose a nutritional problem in a grove, mainly because of the difficulty in determining accurately enough the nutrient availability in root zones where deep-rooted plants take up most nutrients. In reality, the fruit crop as perennial plant stands in a settled site for a long time of several years to more than hundreds years sometimes, the year after year’s inconformity between root absorbing nutrients and farmer applying fertilizers, and the depth and width of tree roots distribution, are more deeply affecting the accuracy of soil testing and availability of soil nutrients. Both leaf tissue and soil testing can be valuable, but leaf analysis provides more useful information about fruit tree nutrition than soil analysis. With the results of a soil test, one tries to predict how much of a particular nutrient will be available to plants in the future. Obviously, the further the future prediction is made, the less accurate it will be. Predictive soil testing works best with (1) short-term crops, and (2) nutrients are not very mobile in soil. Thus, for longterm crops such as fruit tree, predictive sampling should be used for only those nutrients that have slight mobility in soils, such as phosphorus (P), calcium (Ca), copper (Cu), and magnesium (Mg). Soil testing has limited value for the more mobile nutrients such as N and K (Obreza and Morgan, 2008). The single most useful soil test in citrus grove is for pH. Soil pH greatly influences nutrient availability, while some nutrient deficiencies can be avoided by maintaining soil pH between 6.0 and 6.5. In some cases, soil tests can determine the best way to correct a deficiency identified by leaf analysis. For example, Mg deficiency may result from low soil pH or excessively high soil Ca. Dolomitic lime applications are advised if the pH is too low, but magnesium sulfate is preferred if soil Ca is very high and the soil pH is in the desirable range. If soil Ca is excessive and soil pH is relatively high, then foliar application of magnesium nitrate is recommended. A poor relationship may exist between soil test values and leaf nutrient concentrations in perennial crops like citrus. Often, fruit trees contain sufficient levels of a nutrient even though the soil test is low. On the other hand, a high soil test does not assure a sufficient supply to the trees. Tree nutrient uptake can be hindered by problems like drought or flooding stress, root damage, and cool weather. Tissue analysis combined with soil tests can help identify the problem.

References Beridze, Z.A., 1986. Interrelation between ions in the soil and mandarin tree. Subtropicheskie Kul’tury 4, 109–113. Brown, P.H., Bellaloui, N., Wimmer, M.A., Bassil, E.S., Ruiz, Z., Hu, H., Pfeffer, H., Dannel, F., R omheld, V., 2002. Boron in plant biology. Plant Biol. 4, 205–223. Chapman, H.D., 1949a. Tentative leaf analysis standards. Calif. Citrog. 34, 518. Chapman, H.D., 1949b. Citrus leaf analysis: nutrient deficiencies, excesses and fertilizer requirements of soil indicated by diagnostic aid. Calif. Agri. (11), 11, 12, 14. Chapman, H.D., 1960. Leaf and soil analysis in citrus orchards. Univ. Calif. Div. Agri. Sci. Ext. Ser. Manual 25, 53. Dettori, A., 1996. S.E.M. Studies on Penicellium italicum—’Star Ruby’ grapefruit interaction as affected by hot water dripping. In: Proc. Int. Soc. Citriculture , vol. 2. 1288–1289. Du Plessis, S.F., Koen, T.J., Odendaal, W.J., 1992. Interpretation of leaf analysis by means of N/K ratio approach. Proc. 7th Int. Citrus Congr. 2, 553–555. Embleton, T.W., Jones, W.W., Garber, M.J., 1960. Leaf analysis as a guide to nitrogen. Calif. Agric., 1, 12. Gallasch, P.T., Pfeiler, G.R., 1988. Develop a unique leaf analysis service, including a computer programme. In: Goren, R., Mendel, K. (Eds.), Proc. Sixth Int. Citrus Congress. Balaban Publ, Rehovot, Israel. Guiyang, A., 2004. Study on the Standard Range and Effective Factors of Nutritional Elements in Apple Leaves. (Master Degree thesis). Northwest Agriculture & Forestry University, Xian. Jiang, Y., Gu, M., Shu, H., 1995. Nutrient diagnosis of ‘Starking Delicious’ apple. Acta Hortic. Sin. 22 (3), 215–220 (in Chinese with English abstract). Jorgensen, K.R., Price, G.H., 1978. The citrus leaf and soil analysis system in Queensland. Proc. Int. Soc. Citriculture 1, 297–299. Ko, K.D., Kim, S.K., 1987. Chemical properties of soils and leaf mineral contents in Cheju citrus orchards. J. Korean Soc. Hort. Sci 28 (1), 45–52. Koo, R.C.J., Anderson, C.A., Stewart, I., Tucker, D.P.H., Calvert, D.V., Wutscher, H.K., 1984. Recommended fertilizers and nutritional sprays for citrus. In: Fla. Coop. Extension Serv. Bulletin 536D. Koto, M., Shunrokuro, F., Shengzhi, S., 1990. Leaf analysis standards for determining fertilizer needs and its computerisation. In: Bangyan, H., Qian, Y. (Eds.), Int. Symp. Citriculture, Gunazhou, China 383–389. Li, G., Su, R., Shen, T., 1987. Studies on the nutritional ranges in some deciduous fruit trees. Acta Hortic. Sin. 14 (2), 81–89 (in Chinese with English abstract). Liu, X., Zheng, J., Pan, D., Xie, H., 1986. An investigation on the leaf nutritional diagnosis criteria of Longan (Dimocarpus longan Lour.). J. Fujian Agric. Coll. 15 (3), 237–245 (in Chinese with English abstract).

172

13. Floral analysis in fruit crops: A potential tool for nutrient constraints diagnosis

Obreza, T.A., Morgan, K.T., 2008. Soil and leaf tissue testing. In: Nutrition of Florida Citrus Trees, second ed. Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences, University of Florida, pp. 24–32. Obreza, T.A., Alva, A.K., Hanlon, E.A., Rouse, R.E., 1992. Citrus Grove Leaf Tissue and Soil Testing: Sampling, Analysis, and Interpretation. Cooperative Extension Service, University of Florida, Institute of Food and Agricultural Sciences. Perez, G.C., 1996. Argentina. Proc. Int. Soc. Citri., vol. 2. Petra Marschner, J., 2012. Diagnosis of deficiency and toxicity of nutrients. In: Marschner’s Mineral Nutrition of Higher Plants. Elsevier Ltd, pp. 299–312. Quaggio, J.A., Cantarella, H., Mattos Jr., D., 1996. Soil testing and leaf analysis in Brazil—recent developments. Proc. Int. Soc. Citriculture 2, 1269–1275. Rodriguez, O., Gallo, J.R., 1961. Pela analise foliar. Bragantia, Campinas, SP 20, 184–202. Singh, S., Shivankar, V.J., Gupta, S.G., Singh, I.P., Srivastava, A.K., Das, A.K., 2006. Methods of indentifying nutrientional disorder. In: Citrus in Neh Region. Mudrashilpa Offset Printers, pp. 74–80. Smith, P.F., 1966. Citrus nutrition. In: Childers, N.F. (Ed.), Temperate to Tropical Fruit Nutrition. Horticultural Publications, Rutgers, The State University, New Brunswick, NJ, USA, pp. 208–228. Srivastava, A.K., Singh, S., 2002. Soil analysis based diagnostic norms for Indian citrus cultivar. Commun. Soil Sci. Plant Anal. 33 (11&12), 1689–1706. Srivastava, A.K., Singh, S., 2004. Diagnosis methods. In: Nutrient Diagnosis and Management in Citrus. National Research Center for Citrus, Nagpur, pp. 3–19. Srivastava, A.K., Singh, S., 2006. Diagnosis of nutrient constraints in citrus orchards of humid tropical India. J. Plant Nutr. 29 (6), 1061–1076. Takidze, R.M., 1981. Determination of the requirement of citrus crops in inorganic fertilizers by the foliar diagnostics. Subtropicheskie Kul’tury 1, 61–68. Wang, T.C., 1985. Study on guiding fertilization for satsuma orange by leaf analysis. J. Soil Sci. (Turang. Tongbo) 16 (6), 275–277. Wang, T.C., 1987. Study on citrus leaf analysis for guiding fertilization. J. Guizhou Agri. Sci. 2, 36–37 (in Chinese). Wang, R., Zhuang, Y., Chen, L., Xie, Z., Li, L., 1988. Study on leaf optimum nutrients concentration of main subtropical fruit trees in Fujian. Subtrop. Plant Res. Commun. 2, 1–5 (in Chinese). Wang, R., Zhuang, Y., Chen, L., Xu, W., Li, L., Xie, Z., 1992a. Optimum range of mineral element contents in leaves of sweet orange. Subtrop. Plant Res. Commmun. 21 (1), 11–19 (in Chinese with English abstract). Wang, R., Zhuang, Y., Chen, L., Xu, W., Xie, Z., 1992b. Optimum range of mineral element contents in leaves of citrus. Zhejiang Citrus 4, 1–4 (in Chinese). Wang, L.-B., Chen, X.-W., Li, T.-Y., Shi, Y., Wang, S.-H., Gao, Z.-H., Qu, S.-C., 2018. Appropriate content of leaf mineral element in ‘Fuji’ apple orchards of Fengxian, Jiangsu Province. Acta Agric. Univ. Jiangxiensis 10 (1), 56–65 (in Chinese with English abstract). Xie, W.-L., Li, J., Shi, Q., Li, M.-G., Xie, Z.-C., 2014. Studies on the optimum parameters for mineral nutrition in Newhall navel orange leaves. Acta Hortic. Sin. 41 (6), 1069–1079 (in Chinese with English abstract). Yu, L., 1984. Introduction of citrus nutrient diagnosis of leaf analysis in Japan. Zhejiang Citrus (4), 15–17. 47 (in Chinese). Zhang, H., 2001. Studies on Reverse Season Banana Nutritive Peculiarity and Nutritional Diagnostic Ranges in Hainan Province. Huanan Tropical Agricultural University, Hainan (Master Degree thesis). Zhang, Z.-Y., Qiu, H.-L., Zhang, Y.-F., Ma, W.-Q., 2003. Application of leaf analysis in nutrition diagnosis and grape fertilization. Sino-Overseas Grapevine Wine 5, 17–21 (in Chinese with English abstract). Zhou, X., Cheng, C., Lu, B., Z., L., Hui, L., 1991. Studies on mineral nutrient indices for sweet orange leaves. J. Southwest Agric. Univ. 13 (1), 15–20 (in Chinese with English abstract). Zhu, X.-P., Liu, W., Zhang, J.-Z., Wang, T.-K., Qi, Y.-S., 2008. Study on the nutritional ranges of Cabernet Sauvignon grape by leaf analysis. North. Hortic. (10), 51–52 (in Chinese with English abstract). Zhuang, Y., Li, L., Jiang, Y., Su, M., 1986. Preliminary study on nutrition status of Ponkan orchard in Fujian. Fujian Citrus 1, 13–16 (in Chinese). Zhuang, Y., Wang, R., Chen, L., Xie, Z., Xu, W., Huang, Y., Zhou, Z., 1991. Optimum range of mineral element contents in the leaves of Guanxi Honey Pomelo (Citrus grandis). J. Fujian Acad. Agric. Sci. (2), 52–58 (in Chinese with English abstract). Zhuang, Y., Wang, R., Xie, Z., Xu, W., 1997. Optimum range of mineral element contents in the leaves of parent navel orange. Subtrop. Plant Res. Commmun. 26 (2), 1–6 (in Chinese with English abstract).