Managing Quality in Tomatoes for Processing

Transcription

1 Managing Quality in Tomatoes for Processing David Francis, OSU/OARDC Alba McIntyre, OSU/OARDC Tim Hartz, U.C. Davis Funding: Mid-America Food Processors Association, USDA/CSREES (IFAFS)

2 Specific problem: Yellow shoulder disorder Yellow Shoulder Red- Ripe Research/process and resulting recommendations Color quality for whole-peel and diced products YSD reduces nutritional value of the fruit YSD starts early in fruit development If you see YSD in the field it is too late to correct the problem Management should be based on prevention and risk reduction Mature- Green

3 YSD has environmental and genetic causes Location 20-30% Soil structure and/or texture Soil nutrients Weather 5-10% Water Temperature Variety 10-15% Uniform (resistant) Non-uniform (susceptible) Nutrient uptake Interactions 10-20% Weather x Variety Weather x Location Variety x Location Unexplained 25-50% unknown causes more complex interactions

4 Location and Variety represent components that we can manage. Effects of location can be traced to soil: Nutrient availability (K, K/Mg, P) Organic matter Soil structure K + CEC Ca ++ Mg ++

5 Field Survey of nutrient status and fruit quality >640 Samples from commercial fields were analyzed: Fruit color data Hue =color L = light to dark Hue difference = a uniformity measurement. Large values indicate YSD L difference = a uniformity measurement. Large values indicate internal white tissue Soil fertility data Exchangeable K, Ca, Mg, %Organic Matter, ph, CEC.

6 Correlation between quality and soil characteristics for different varieties OX23 ph OM CEC Ca Mg K Hartz Ca/Mg p r2 p r2 p r2 p r2 p r2 p r2 p r2 p r2 L Hue Ldiff Hdiff H9423 ph OM CEC Ca Mg K Hartz Ca/Mg p r2 p r2 p r2 p r2 p r2 p r2 p r2 p r2 L Hue Ldiff Hdiff

7 Organic matter vs uniformity of color for two varieties 10 Ldiff vs. OM: GEM Ldiff vs. OM: H r 2 = ; p = r 2 = ; p = Ldiff OM (%) Ldiff OM (%) < 0.2% of variation 14 % of variation

8 Hartz Ratio vs uniformity of color for two varieties 10 Ldiff vs. Hartz ratio: GEM Ldiff vs. Hartz ratio: H r 2 = ; p = r 2 = ; p = Ldiff Ldiff Hartz ratio (K/sqrt Mg) Hartz ratio (K/sqrt Mg) ~ 0.2% of variation 23 % of variation

9 Not all varieties demonstrate a linear correlation between color quality and soil quality. Uniform varieties tend to show significant correlations. Non-Uniform varieties tend not to show correlations.

10 Soils in the Midwest tomato growing regions are variable relative to natural fertility, mineralogy, and texture Alfisols (IN, OH) Entisols (IN, MI) Mollisols (IN, OH)

11 Mixed Mineralogy, coarser texture USDA land use map Mixed or Illitic mineralogy, medium finer texture Mixed mineralogy, coarser texture 17 1

12 Diverse soils can be divided into two management types Mean Fine soil Mean Coarse Soil P-Value Sand *** Clay *** Silt *** ph NS OM *** CEC *** K ** K fixed *** Ca *** Mg *** Hartz ratio *** Kact *** Ca/Mg ratio *** K%CEC *** P ** 1 - Percent basis 2 cmol Kg -1 basis. 3 - ug g -1 (ppm) basis

13 Coarse soil (sand) logistic regression % 25.8% Coarse Soils L* L*diff Hue o Hue o diff 1.0 Number of Observations % 16.0% 10.4% Slope of Expected Probability % % 1.7% 0.6% Soil Exchangeable K (cmol. Kg -1 ) 0.0

14 Coarse soil (sand) Optimum range for K 25.8% 25.8% Coarse Soils L* L*diff Hue o Hue o diff Number of Observations % 16.0% 10.4% Slope of Expected Probability % % 1.7% 0.6% Soil Exchangeable K (cmol. Kg -1 ) 0.0

15 Coarse soil (sand) 120 Fields at risk due 100 to low K 25.8% 25.8% Coarse Soils L* L*diff Hue o Hue o diff Number of Observations % 16.0% 10.4% Slope of Expected Probability % 6.2% 1.7% 0.6% Soil Exchangeable K (cmol. Kg -1 )

16 Coarse soil (sand) 120 Fields at risk due 100 to low K 25.8% 25.8% Coarse Soils L* L*diff Hue o Hue o diff Number of Observations % 16.0% 10.4% Fields with adequate K Slope of Expected Probability % % 1.7% 0.6% Soil Exchangeable K (cmol. Kg -1 ) 0.0

17 Coarse soil (sand) Number of Observations % 21.9% 31.7% 21.3% 9.0% Coarse Soils L* L* diff Hue o Hue o diff Slope of Expected Probability % % % 0.6% 0.0% 0.3% 0.0% K. Mg -1/2 (cmol. Kg -1 ) 1/2 Hartz ratio

18 Coarse soil (sand) % Coarse Soils L* L* diff Hue o Hue o diff Number of Observations % 16.6% 12.9% Slope of Expected Probability % 5.6% 3.1% % 1.1% 0.8% 0.3% 0.0% 0.3% 0.0% Soil Availabe P (uġg -1 )

19 Recommendations Fine Textured Soils (Clay loam) Parameter Target Notes P ppm 100 lb/ac 50 ppm K cmol/kg 200 lb/ac cmol/kg K/(Mg) 1/ Coarse Textured Soils (Sandy loam) Parameter Target P ppm K K/(Mg) 1/ Note relationship between these values and current recommendations: K 0.35 cmol/kg (Hartz et al., 2005); 0.46 cmol/kg (OMAFRA, 2008) P 60 ppm (OMAFRA, 2008) cmol/kg

20 Soil Fertility in the Midwest N Mean Min Max Std.Dev. Coef. Var. (%) ph % OM P (ppm) K(ppm) Ca (ppm) Mg(ppm) Average fertility of soil in our region is good; There is opportunity for improvement: ~23% of commercial fields are at risk of YSD due to low P fertility; ~ 2% of fields may be over-fertilized with P.

21 Phosphorous in the Maumee Watershed m1 g 1 / L 1 a s 1 P /6 5/2 8/22 Dec. Jan. Feb. March April May June July Aug. Sept. Significant run-off occurs through Winter and early Spring.

22 K fertilization and fixation K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + Root Exchangeable K and soil solution K are the first ones to be taken up by plants K + + K + K + K + K + K + K + K + K + K + K + K + K + K + Root Intensive cropping causes exhaustion of the readily available K K + K + K + + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + K + Root When K fertilizer is applied, it will first be trapped inside the clay minerals. As those sites are satisfied, K will occupy external sites and reach an equilibrium with the soil solution.

23 Soil Fixation: Fine soils fix ~16% of K, Coarse soils fix ~6%, deficient soils fix more Location Samples Exch. K ppm K fix (%) OH IN PA CA All Grps K Fixation (%) Exchageable K (ppm) OH, IN and MI have predominantly mixed-illitic mineralogy. By nature these soils fix less cations than California soils. CA fields are historically under fertilized (Hartz,2003).

24 To maximize availability to the plant and minimize loss to fixation and run-off, feed the plant when it needs it

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26 Results from CA Effects of K fertilization on % YSD CA Continuous Fert (400 Kg/ha) Continuous Fert 800 Kg/ha) Early fert (250 Kg/ha) Late f ert (250 Kg/ha) Foliar (up to 36 Kg/ha) Untreated (0 Kg/ha)

27 Results Fremont % Good Fruit (L<48,H<48) a % GF 03 % GF 04 Drip % GF 04 Solid b ab b a a a No K Fert early Fert mid Fert late No Treatments irrigation a b ab ab ab b

28 Year to year variation K application through Drip lead to increased yield and 5-10% reduction in number of fruit affected by YSD. Magnitude of improvement was variable, perhaps dependent on rainfall during growing season Precipitation (in) Cumulative precipitation - Fremont, OH waterlogging Drip treatments 0 May June July August September Month

29 How does variation affect profit? Economic modeling using POWERSIM Tom Keehner Dr. Sporleder Mean SD Grower return per acre above total cost ($/acre) $ Total cost per acre incurred by the grower ($/acre) $2, Total revenue per acre realized by the grower ($/acre) $2, Premium received for graded quality ($/ton) $0.80 Yield (tons/acre) 32.2 Percentage of the crop graded as either Grade A or Grade B 69.38%

30 Distribution of Return above Cost Non-Uniform Variety (susceptible to YSD) Histogram # Boxplot 5.3+* 17 0.* 85 0.*** ***** ******** 646.************ 955.***************** 1426.******************************* ************************************************ 4104 *--+--*.*************************************** 3271.********************* **************** 1327.************ 1020.***** ** * 41 0.* * * may represent up to 86 counts

31 Distribution of Return above Cost Uniform Color Variety Histogram # Boxplot 5.3+* 17 0.* 85 0.*** ***** ******** 646.************ 955.***************** 1426.******************************* ************************************************ 4104 *--+--*.*************************************** 3271.********************* **************** 1327.************ 1020.***** ** * 41 0.* * * may represent up to 86 counts

32 Return on investment (U.S. $) Non-Uniform Uniform K Historical Variety Variety Fertigation stdev average stdev Simulations based on average yield data and standard deviations Contract prices, grade rewards, and input prices reflect 2004 values Take home message: Management of YSD may not return more on investment, but it can reduce risk of loosing money

33 For more information click on managing color disorders 700 Exchangeable Mg (lb/ac) Exchangeable K (ppm) Hartz Ratio > 0.35 Hartz Ratio < Exchangeable K (Lb/ac) Exchangeable Mg (ppm) 200

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36 Conclusions: To minimize YSD and maximize quality manage variety and soil nutrient availability Base fertilization on soil test K, K/(Mg) 1/2, P Provide K and P nutrients when the plant needs them (win, win, win situation for quality, return on investment, and environmental stewardship) Long-term management: Increase organic matter, replace K

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40 Distribution of Return above Cost Historical Data Histogram # Boxplot 5.3+* 17 0.* 85 0.*** ***** ******** 646.************ 955.***************** 1426.******************************* ************************************************ 4104 *--+--*.*************************************** 3271.********************* **************** 1327.************ 1020.***** ** * 41 0.* * * may represent up to 86 counts

41 Distribution of Return above Cost Adding K when the plant needs it Histogram # Boxplot 5.3+* 17 0.* 85 0.*** ***** ******** 646.************ 955.***************** 1426.******************************* ************************************************ 4104 *--+--*.*************************************** 3271.********************* **************** 1327.************ 1020.***** ** * 41 0.* * * may represent up to 86 counts

42 Fine soil (clay) Number of Observations % 30.0% 23.0% 13.2% Fine Soils L* L* diff Hue o Hue o diff Slope of Expected Probability % 4.5% 1.4% 2.4% % 0.3% 0.0% Soil Echangeable K (cmol. Kg -1 )

43 Fine soil (clay) % Fine Soils L* L* diff Hue o Number of Observations % 25.8% Hue o diff Slope of Expected Probability % % % 0.3% 0.3% 0.0% K. Mg -1/2 (cmol. Kg -1 ) 1/2-0.2 Hartz ratio

44 Fine soil (clay) Number of Observations % 27.2% 17.1% 10.8% 7.3% Fine Soils L* L* diff Hue o Hue o diff Slope of Expected Probability % % 0.7% 0.3% 0.3% 0.0% 0.0% 0.3% 0.0% Soil Availabe P (ug. g -1 ) 0.00

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