Conceived and designed the experiments: JPR PA JR LCB. Performed the experiments: JPR PA JR LCB CS CR ND. Analyzed the data: JPR PA JR LCB JRA. Contributed reagents/materials/analysis tools: JPR PA CS CR ND JZ. Wrote the paper: JPR PA JR LCB. Set up and managed the overall study: JPR.
The authors have declared that no competing interests exist.
Sale of organic foods is one of the fastest growing market segments within the global food industry. People often buy organic food because they believe organic farms produce more nutritious and better tasting food from healthier soils. Here we tested if there are significant differences in fruit and soil quality from 13 pairs of commercial organic and conventional strawberry agroecosystems in California.
At multiple sampling times for two years, we evaluated three varieties of strawberries for mineral elements, shelf life, phytochemical composition, and organoleptic properties. We also analyzed traditional soil properties and soil DNA using microarray technology. We found that the organic farms had strawberries with longer shelf life, greater dry matter, and higher antioxidant activity and concentrations of ascorbic acid and phenolic compounds, but lower concentrations of phosphorus and potassium. In one variety, sensory panels judged organic strawberries to be sweeter and have better flavor, overall acceptance, and appearance than their conventional counterparts. We also found the organically farmed soils to have more total carbon and nitrogen, greater microbial biomass and activity, and higher concentrations of micronutrients. Organically farmed soils also exhibited greater numbers of endemic genes and greater functional gene abundance and diversity for several biogeochemical processes, such as nitrogen fixation and pesticide degradation.
Our findings show that the organic strawberry farms produced higher quality fruit and that their higher quality soils may have greater microbial functional capability and resilience to stress. These findings justify additional investigations aimed at detecting and quantifying such effects and their interactions.
Although global demand for organic products remains robust, consumer demand for these products is concentrated in North America and Europe
While there is strong evidence that organic foods have significantly less pesticide residues
In the past 10 years, ten review studies of the scientific literature comparing the nutrition of organic and conventional foods have been published. Eight of these review studies
A widely accepted definition of soil quality is the capacity of a soil to sustain biological productivity, maintain environmental quality, and promote plant and animal health
The majority of previous organic/conventional studies have focused on either comparing fruit quality or soil quality. The few studies that have compared both facets have limited their analyses to selected properties. Currently, no published study has integrated interdisciplinary knowledge and robust methodologies in a systems approach to quantitatively compare a comprehensive range of both fruit and soil quality indices using multiple organic and conventional farms, multiple varieties, and multiple sampling times. Here, we assembled an interdisciplinary team of scientists representing agroecology, soil science, microbial ecology, genetics, pomology, food chemistry, sensory science, and statistics to address the following question: Are there significant differences in nutritional and organoleptic fruit properties and in soil quality, including soil ecosystem functional genes, between commercial organic and conventional strawberry agroecosystems?
Although some farm production conditions can be simulated at research stations, farming systems research that measures multiple variables can often only be properly studied under actual farming or agroecosystem conditions
To determine if differences in food and soil quality exist, we sampled repeatedly harvested strawberry varieties (‘Diamante’, ‘San Juan’, and ‘Lanai’) and soils at multiple sampling times in 2004 and 2005 from 13 pairs of adjacent organic and conventional fields from commercial farms. Each organic/conventional field pair had the same soil type and the same strawberry variety planted at similar times. Because strawberries go through different growth cycles during the 7-month harvest season, we analyzed 42 fruit, 11 leaf, and 6 organoleptic properties multiple times during the two years of our study. Strawberries in each field pair were analyzed at the same time and stage of harvest maturity, and under identical storage conditions and transportation methods so that the strawberries were as close to retail consumption as possible. In addition to measuring 31 traditional soil chemical and biological properties, we analyzed soil DNA using microarray technology to target those microbial genes involved in 11 specific ecosystem processes.
Strawberry leaves were analyzed for plant nutrients and fruit were analyzed for plant nutrients, fruit quality, nutritional value, and organoleptic properties. Leaf P and fruit P and K concentrations were significantly higher in conventionally grown strawberry plants than in organically grown plants (
Mineral Element | ‘Diamante’ | ‘Lanai’ | ‘San Juan’ | ||||
ORG | CON | ORG | CON | ORG | CON | ||
Leaves | |||||||
Nitrogen (% FW) | 2.51±0.10 | 2.76 |
3.06±0.12 | 2.91±0.12 | 2.84±0.11 | 2.84±0.11 | 0.020 |
Calcium (% FW) | 0.73±0.36 | 1.19 |
0.81±0.37 | 0.88±0.37 | 0.87±0.37 | 0.77±0.37 | 0.036 |
ORG | CON | ||||||
Phosphorus |
0.37±0.016 | 0.45 |
0.001 | ||||
Potassium |
1.56±0.04 | 1.58±0.04 | 0.71 | ||||
Sulfur (% FW) | 0.215±0.009 | 0.214±0.009 | 0.91 | ||||
Magnesium (% FW) | 0.311±0.047 | 0.354 |
0.066 | ||||
Boron (ppm) | 38.9±2.36 | 38.7±2.36 | 0.95 | ||||
Zinc (ppm) | 59.9±1.31 | 63.3±1.31 | 0.73 | ||||
Manganese (ppm) | 128±34.5 | 182±34.5 | 0.19 | ||||
Copper (ppm) | 5.24±1.46 | 4.81±1.46 | 0.14 | ||||
Iron (ppm) | 207±24.1 | 214±24.1 | 0.70 | ||||
Fruit | |||||||
Nitrogen (% FW) | 1.02±0.11 | 1.08 |
0.078 | ||||
Phosphorus (% FW) | 0.247±0.012 | 0.286 |
0.001 | ||||
Potassium (% FW) | 1.50±0.05 | 1.65**±0.05 | 0.010 | ||||
Calcium (% FW) | 0.120±0.015 | 0.132±0.015 | 0.18 | ||||
Magnesium (% FW) | 0.130±0.003 | 0.134±0.003 | 0.26 | ||||
Boron (ppm) | 14.5±17.5 | 15.2±17.5 | 0.57 | ||||
Zinc (ppm) | 9.95±0.71 | 9.96±0.71 | 0.99 |
Leaves and fruit were sampled in June 2004 and April and June 2005 from 13 pairs of organic (ORG) and conventional (CON) farm fields. Probabilities (
*Means are notably different at
Means are significantly different at
Based on Dietary Reference Intakes (DRI)
When susceptibility to fungal post-harvest rots was evaluated, organic strawberries had significantly longer survival times (less gray mold incidence) than conventional strawberries (
Mean survival days were CON = 4.15±0.06 and ORG = 4.54±0.06. (Error bars indicate standard error.)
Fruit Quality Variable (units) | Organic | Conventional | |
Fruit fresh weight (g) | 24.07±0.68 | 27.78±0.68 | 0.001 |
Dry matter (%) | 10.03±0.20 | 9.26±0.20 | 0.006 |
Fruit weight loss (%) | 25.40±5.16 | 27.52±5.16 | 0.048 |
Fruit firmness (N) | 4.36±1.90 | 4.17±1.90 | 0.30 |
External L |
37.66±0.76 | 38.65±0.76 | 0.030 |
External C |
42.21±0.37 | 41.76±0.37 | 0.25 |
External hab (°) | 31.26±0.63 | 32.14±0.63 | 0.048 |
Total antioxidant activity (mmol Trolox equivalents/g FW) | 11.88±0.35 | 10.95±0.35 | 0.019 |
Total phenolics (mg gallic acid equivalents/g FW) | 1.37±0.13 | 1.24±0.13 | 0.0003 |
Total ascorbic acid |
0.621±0.015 | 0.566±0.015 | 0.009 |
Total anthocyanins (µg P-3-Glc |
205±19.4 | 192±19.4 | 0.103 |
Strawberries (‘Diamante’, ‘Lanai’, and ‘San Juan’) were sampled from 13 pairs of organic and conventional farm fields in June and September 2004 and April, June, and September 2005. Means and standard errors of fruit characteristics for individual sampling/harvest times, varieties, and years are listed in
*Based on Dietary Reference Intakes (DRI)
Pelargonidyn-3-glucoside.
Strawberries from organic farms were significantly smaller (by 13.4%) than those from conventional farms, but had significantly greater dry matter content (by 8.3%) (
Sensory Property | ‘Diamante’ | ‘Lanai’ | ‘San Juan’ | ||||
ORG | CON | ORG | CON | ORG | CON | ||
Hedonic/intensity ratings | |||||||
Overall acceptance | 6.09 a±0.23 | 5.35 b±0.23 | 6.24 a±0.29 | 6.24 a±0.29 | 6.09 a±0.27 | 6.36 a±0.27 | 0.029 |
Flavor | 5.95 a±0.16 | 5.17 b±0.16 | 6.08 a±0.17 | 5.92 a±0.17 | 5.86 a±0.19 | 6.07 a±0.19 | 0.044 |
Sweetness | 5.56a±0.22 | 4.73 b±0.22 | 5.69 a±0.24 | 5.56 a±0.24 | 5.52 a±0.25 | 5.74 a±0.25 | 0.029 |
Appearance | 6.73 a±0.37 | 5.97 b±0.37 | 6.78 a±0.39 | 6.97 a±0.39 | 7.09 a±0.39 | 7.03 a±0.39 | 0.067 |
ORG | CON | ||||||
Juiciness | 6.21±0.09 | 6.35±0.09 | 0.11 | ||||
Tartness | 4.61±0.27 | 4.75±0.27 | 0.38 |
Strawberry fruit (‘Diamante’, ‘Lanai’, and ‘San Juan’) were sampled from 13 pairs of organic and conventional farm fields in September 2004 and April, June, and September 2005. Differences between values within rows followed by different letters are significant at
Organic strawberries had significantly higher total antioxidant activity (8.5% more), ascorbic acid (9.7% more), and total phenolics (10.5% more) than conventional berries (
Polyphenol (mg 100 g−1 FW) | ‘Diamante’ | ‘Lanai’ | ‘San Juan’ | ||||
ORG | CON | ORG | CON | ORG | CON | ||
April | |||||||
Quercetin glycoside | 4.00±1.38 | 6.72 |
9.18 |
5.43±1.41 | 9.01±1.56 | 7.60±1.56 | 0.009 |
Quercetin, total | 7.02±1.17 | 9.45 |
11.71 |
7.92±1.17 | 11.22±1.56 | 10.11±1.56 | 0.020 |
Kaempferol | 0.93±0.08 | 1.13 |
0.99±0.08 | 1.05±0.08 | 1.28 |
1.07±0.10 | 0.026 |
June | |||||||
Quercetin glycoside | 6.27±1.17 | 7.20±1.17 | 2.87±1.41 | 6.09 |
5.01±1.28 | 5.32±1.28 | 0.009 |
Quercetin, total | 8.78±1.14 | 9.72±1.14 | 6.42±1.17 | 8.80 |
7.92±1.15 | 7.81±1.15 | 0.020 |
Kaempferol | 1.21 |
0.96±0.07 | 0.98±0.08 | 1.03±0.08 | 1.06±0.07 | 0.98±0.07 | 0.026 |
September | |||||||
Quercetin glycoside | 4.97±1.17 | 4.87±1.17 | 3.89±1.41 | 3.93±1.41 | 4.90±1.28 | 7.13 |
0.009 |
Quercetin, total | 7.51±1.14 | 7.33±1.14 | 6.61±1.17 | 6.57±1.17 | 7.37±1.15 | 9.19±1.15 | 0.020 |
Kaempferol | 0.96±0.07 | 0.92±0.07 | 1.03±0.08 | 1.05±0.08 | 0.93±0.07 | 1.00±0.07 | 0.026 |
ORG | CON | ||||||
Quercetin | 2.79±0.06 | 2.71±0.06 | 0.17 | ||||
Kaempferol glycoside | 4.28±0.97 | 4.34±0.97 | 0.88 | ||||
Kaempferol, total | 5.32±1.02 | 5.35±1.02 | 0.93 | ||||
Ellagic acid glycoside | 55.0±13.1 | 53.8±13.1 | 0.92 | ||||
Ellagic acid | 2.27±1.48 | 2.08±1.48 | 0.70 | ||||
Ellagic acid, total | 57.2±1.31 | 55.9±1.31 | 0.88 | ||||
Phloridzin glycoside | 2.04±0.29 | 2.24±0.29 | 0.49 | ||||
Phloretin | 2.40±0.04 | 2.43±0.04 | 0.56 | ||||
Phloretin, total | 4.42±0.31 | 4.64±0.31 | 0.41 | ||||
R-Naringin glycoside | 2.90±0.95 | 1.35±0.95 | 0.27 | ||||
S-Naringin glycoside | 2.90±0.98 | 1.46±0.98 | 0.32 | ||||
R-Naringenin | 0.43±0.07 | 0.44±0.07 | 0.83 | ||||
S-Naringenin | 0.24±0.07 | 0.29±0.07 | 0.51 | ||||
R-Naringenin, total | 3.31±0.95 | 1.77±0.95 | 0.28 | ||||
S-Naringenin, total | 3.12±0.98 | 1.73±0.98 | 0.34 |
Fruit were sampled in June and September 2004 and April, June, and September 2005 from 13 pairs of organic (ORG) and conventional (CON) farm fields. Least square means ± standard error of the means. Probabilities (
*Means are notably different at
Means are significantly different at
Means are significantly different at
Using hedonic/intensity ratings, consumer-sensory panels found organic ‘Diamante’ strawberries to be sweeter and have preferable flavor, appearance, and overall acceptance compared to conventional ‘Diamante’ berries (
Variable | ‘Diamante’ | ‘Lanai’ | ‘San Juan’ | ||||
ORG | CON | ORG | CON | ORG | CON | ||
Soluble solids (°brix) | 8.97 a±0.48 | 7.68 b±0.48 | 8.98 a±0.58 | 9.52 a±0.58 | 8.96 a±0.53 | 8.71 a±0.53 | 0.091 |
TA (mg citric acid g−1 FW) | 9.16 a±0.20 | 7.52 bc±0.20 | 7.18 bc±0.26 | 7.51 bc±0.26 | 6.97 c±0.24 | 7.79 b±0.24 | 0.0005 |
ORG | CON | ||||||
Soluble solids/TA | 1.16±0.07 | 1.14±0.07 | 0.62 | ||||
Reducing sugars (mg Glc g−1 FW) | 69.1±2.05 | 69.4±2.05 | 0.93 | ||||
Total sugars (mg Glc g−1 FW) | 73.0±2.38 | 78.4±2.38 | 0.13 | ||||
pH | 3.77±0.06 | 3.81±0.06 | 0.105 |
Fruit were sampled from 13 pairs of organic and conventional farms in June and September 2004 and April, June, and September 2005. Probabilities (
Soils were sampled and analyzed from the top (0–10 cm) and bottom (20–30 cm) of the raised mounds in June 2004 and 2005. The organically managed surface soils compared to their conventional counterparts contained significantly greater total carbon (21.6% more) and nitrogen (30.2% more) (
Soil Property | Organic (0–10 cm) | Conventional (0–10 cm) | Organic (20–30 cm) | Conventional (20–30 cm) | ||
Sand (g 100 g−1 soil) | 60.3±7.9 | 60.5±8.2 | 0.931 | 61.0±8.0 | 59.8±8.7 | 0.644 |
Silt (g 100 g−1 soil) | 26.8±4.8 | 26.4±5.5 | 0.619 | 25.8±4.9 | 27.3±5.7 | 0.821 |
Clay (g 100 g−1 soil) | 13.0±3.4 | 13.1±2.9 | 0.925 | 13.2±3.5 | 12.9±3.2 | 0.384 |
Nitrate (mg kg−1 soil) | 46.8±12.1 | 31.6±7.3 | 0.402 | 24.5±3.9 | 22.9±7.0 | 0.866 |
Ammonium (mg kg−1 soil) | 2.8±0.3 | 2.9±0.3 | 0.105 | 2.5±0.2 | 2.7±0.3 | 0.316 |
Phosphorus (mg kg−1 soil) | 60.9±13.3 | 64.5±7.7 | 0.652 | 60.1±13.5 | 72.1±10.4 | 0.173 |
Sulfur (mg kg−1 soil) | 134±30 | 119±38 | 0.76 | 119±39.4 | 55.7±14.8 | 0.140 |
Boron (mg kg−1 soil) | 0.88±0.23 | 0.74±0.25 | 0.043 | 0.71±0.19 | 0.75±0.30 | 0.441 |
Zinc (mg kg−1 soil) | 2.88±0.37 | 1.97±0.12 | 0.048 | 2.42±0.37 | 1.81±0.24 | 0.097 |
Manganese (mg kg−1 soil) | 4.52±0.50 | 7.64±2.01 | 0.217 | 3.13±0.37 | 3.68±0.47 | 0.196 |
Copper (mg kg−1 soil) | 1.37±0.31 | 1.17±0.25 | 0.216 | 1.40±0.31 | 1.23±0.29 | 0.291 |
Iron (mg kg−1 soil) | 28.6±3.9 | 26.8±5.0 | 0.064 | 26.4±3.4 | 31.4±5.4 | 0.203 |
Potassium (cmol kg−1 soil) | 0.6±0.1 | 0.5±0.1 | 0.194 | 0.6±0.1 | 0.5±0.1 | 0.230 |
Calcium (cmol kg−1 soil) | 10.7±2.3 | 9.7±2.1 | 0.165 | 10.3±2.5 | 9.6±2.2 | 0.519 |
Magnesium (cmol kg−1 soil) | 4.1±1.1 | 4.2±1.3 | 0.722 | 3.9±1.2 | 4.20±1.3 | 0.695 |
Sodium (cmol kg−1 soil) | 0.4±0.1 | 0.3±0.1 | 0.001 | 0.3±0.04 | 0.3±0.04 | 0.858 |
Total bases (cmol (+) kg−1) | 15.8±3.5 | 14.7±3.3 | 0.244 | 15.1±3.7 | 14.6±3.5 | 0.841 |
pH | 7.05±0.11 | 7.09±0.16 | 0.953 | 7.16±0.10 | 7.09±0.17 | 0.694 |
Buffer capacity pH | 7.51±0.02 | 7.51±0.03 | 0.908 | 7.53±0.02 | 7.52±0.03 | 0.789 |
EC (mmhos cm−1) | 2.72±0.34 | 2.18±0.39 | 0.071 | 2.13±0.37 | 1.50±0.22 | 0.306 |
Total carbon (g kg−1 soil) | 10.04±0.15 | 8.25±0.12 | 0.036 | 9.43±0.17 | 7.71±0.13 | 0.034 |
Total nitrogen (g kg−1 soil) | 0.867±0.014 | 0.666±0.010 | 0.009 | 0.783±0.015 | 0.625±0.012 | 0.010 |
Readily mineralizable carbon (µg MinC g−1 soil) | 17.7±1.1 | 14.1±1.2 | 0.009 | 14.9±1.6 | 11.2±1.2 | 0.019 |
Microbial biomass (µg MicC g−1 soil) |
249±22.5 | 96±6.8 | 0.000 | 211±20.5 | 101±12.1 | 0.042 |
MicC (% of total carbon) |
2.21±0.13 | 1.33±0.26 | 0.005 | 2.16±0.31 | 1.54±0.37 | 0.041 |
MicC MinC−1 |
16.0±1.8 | 8.6±0.6 | 0.004 | 16.8±3.1 | 9.3±0.5 | 0.049 |
Basal respiration (µg CO2-C g−1 soil h−1) |
0.472±0.055 | 0.354±0.032 | 0.009 | 0.731±0.186 | 0.348±0.111 | 0.009 |
Dehydrogenase (µg TPF g−1 soil) | 1.38±0.21 | 0.65±0.05 | 0.000 | 0.89±0.14 | 0.52±0.05 | 0.000 |
Acid phosphatase (µg p-nitrophenol g−1 soil) | 121.5±14.1 | 58.2±5.6 | 0.009 | 104.7±37.4 |
53.1±9.1 |
0.039 |
Alkaline phosphatase (µg p-nitrophenol g−1 soil) | 122.3±13.0 | 55.6±8.5 | 0.002 | 84.4±17.0 |
47.0±13.0 |
0.262 |
qCO2 (ug CO2-C h−1 mg−1 MicC) |
1.9±0.18 | 3.7±0.33 | 0.003 | 3.5±0.73 | 3.4±0.67 | 0.838 |
Protease native (µg amino acid-N g−1 soil h−1) | 2.41±0.29 | 2.81±0.36 | 0.446 | 2.08±0.29 |
1.25±0.35 |
0.107 |
Protease potential (µg amino acid-N g−1 soil h−1) | 4.06±0.65 | 3.49±0.32 | 0.369 | 3.21±0.25 |
2.78±0.31 |
0.150 |
Mycorrhizae total colonized root length (mm) |
122±11 | 104±10 | 0.164 | – | – | – |
Soil samples were taken in June 2004 and June 2005, except where noted. Means and standard errors of soil properties for individual years are listed in
*Measured in June 2005 only.
Measured in June 2004 only.
Organically managed surface soils also supported significantly greater microbial biomass (159.4% more), microbial carbon as a percent of total carbon (66.2% greater), readily mineralizable carbon (25.5% more), and microbial carbon to mineralizable carbon ratio (86.0% greater) (
To quantify soil microbial gene presence and diversity, we used a gene array termed GeoChip containing more than 24,000 oligonucleotide (50-mer) probes and covering 10,000 genes involved in nitrogen, carbon, sulfur, and phosphorus transformations and cycling, metal reduction and resistance, and organic xenobiotic degradation
Mean DNA microarray signal intensity of total detected genes was significantly greater in organically managed soils than in conventionally managed soils (
Each of the 1711 data points represents average gene SI from eight organically farmed soils against eight matched conventionally farmed soils. The SIs of more than 32% (553) of 1711 individual genes detected were significantly higher in organically managed soils, while not one was significantly higher in conventionally managed soils.
Soil Functional Group or Organism Group | Signal Intensity (103) | Diversity (Simpson's Reciprocal Index) | ||||
ORG | CON | ORG | CON | |||
Total detected genes | 13479±874 | 9350±1003 | 0.008 | 656±31 | 504±34 | 0.015 |
N fixation | 744±59 | 547±76 | 0.018 | 44±1 | 38±2 | 0.034 |
Nitrification | 262±12 | 201±13 | 0.004 | 7±0.3 | 6±0.3 | 0.012 |
Denitrification | 552±46 | 405±59 | 0.029 | 33±2 | 26±3 | 0.010 |
Sulfite reduction | 529±36 | 368±40 | 0.009 | 37±1 | 31±1 | 0.004 |
Pesticide degradation | 1970±131 | 1322±143 | 0.006 | 104±4 | 81±4 | 0.004 |
Other organic xeno-biotic degradation | 3999±253 | 2819±296 | 0.008 | 193±10 | 146±11 | 0.012 |
Metal reduction and resistance | 2580±164 | 1750±164 | 0.008 | 112±5 | 84±5 | 0.010 |
Dehydrogenase | 171±9 | 118±11 | 0.004 | 7±0.3 | 6±0.3 | 0.245 |
Urease | 621±48 | 394±59 | 0.008 | 36±3 | 28±3 | 0.031 |
Cellulase | 819±60 | 569±65 | 0.012 | 51±2 | 41±2 | 0.012 |
Chitinase | 347±25 | 240±23 | 0.016 | 16±1 | 12±0.7 | 0.024 |
Fungi | 164±9 | 108±10 | 0.003 | 17±4 | 13±3 | 0.025 |
Prokaryotes | 12818±837 | 9088±964 | 0.008 | 624±29 | 487±33 | 0.011 |
Fungi/Prokaryotes Ratio | 0.013±0.000 | 0.012±0.000 | 0.323 | 0.027±0.007 | 0.027±0.006 | 0.343 |
Functional groups from Reeve et al.
Organically managed soils exhibited significantly more endemic genes (
Some of the 11 functional groups addressed on the GeoChip are purely prokaryotic functions (e.g., nitrogen fixation, nitrification, and denitrification), while others are characteristics of both prokaryotes and eukaryotes (mainly fungi). To ensure that neither group biased the signal intensity and diversity results for either the organic or conventional farming systems, we separated out fungal- and prokaryotic-derived genes into their respective groups and calculated the ratio of fungi to prokaryotes for gene signal intensity and diversity in the two agroecosystems. Not only are the fungi numbers higher for both signal intensity and diversity in the organic agroecosystems, but so are the prokaryote numbers, too (
The large differences in soil microbial properties and soil functional gene abundance and diversity between the organically and conventionally farmed soils are most likely due to a combination of factors: chemical fumigation with methyl bromide of the conventionally farmed soils, lack of synthetic pesticide use on the organic fields, and double the application rates of compost to the organic fields compared to the conventional fields (
Our study, in which soil samples were taken about 5 to 6 months after fumigation, was conducted on organic and conventional fields with longer histories (at least 5 years) of both organic and conventional (with fumigation) management, likely contributing to the detection of some persistent effects on the microbial population. The organic fields also received 20.2–24.6 Mg compost ha−1, almost twice the rate of the conventional fields at 11.2–13.4 Mg compost ha−1 (
In summary, the organic strawberries and their soils were of higher quality compared to their conventional counterparts. Specifically, the organic strawberries, while having lower concentrations of phosphorus and potassium, had higher antioxidant activity and concentrations of ascorbic acid and phenolic compounds, longer shelf life, greater dry matter, and, for ‘Diamante’, better taste and appearance. The organically farmed soils had more carbon and nitrogen, greater microbial biomass and activity, and greater functional gene abundance and diversity. This study demonstrates that soil DNA analyses using microarray technology can be used as an additional measurement of soil quality. Our sustainability study also demonstrates the benefits of using an interdisciplinary methodology that comprehensively and quantitatively compares numerous indices of fruit and soil quality from multiple, commercial organic and conventional farms, multiple varieties and soils, and multiple sampling times.
Thirteen pairs of side-by-side commercial organic and conventional strawberry farm fields were selected in the Watsonville area, the dominant strawberry growing region of California, USA. In turn, California is the leading producer in the U.S., accounting for 87% of the nation's strawberry production (29). The Watsonville area annually grows strawberries on about 5,000 hectares, accounting for about 40% of the strawberry acreage in the state (29).
The selection of 13 field pairs (5 in 2004 and 8 in 2005) from commercial strawberry farms was made on the basis of grower interviews and on-farm field examinations to ensure that all soil-forming factors, except management, were the same for each field pair
Strawberry field pairs in 2004 were different from those in 2005 because both organic and conventional farmers grew strawberries in alternate years using a similar two-year rotation. More specifically, all farmers in the study grew strawberries on constructed, 30-cm high mounded rows covered with plastic mulch for only one year, preceded by a different crop, such as broccoli, lettuce, or a cover crop, grown on flat ground (without mounds) the previous year. Growing strawberries as annuals using this “raised-bed plasticulture” system is typical of organic and conventional strawberry growers in California
The organic fields had been certified organic (USDA) for at least 5 years, providing sufficient time for the organic farming practices to influence soil properties. The organic fields relied only on organically certified fertilizers and pesticides and no soil fumigation (
Strawberry (
A subsample of the collected fruit, as well as leaf samples, taken in April 2005 and June 2004 and 2005, were sent to Soiltest Farm Consultants Inc. in Moses Lake, WA, where fruit and leaf samples were analyzed for N, P, K, Ca, Mg, B, and Zn, plus S, Mn, Cu, and Fe for leaf samples only, according to standard methods
Strawberry fruit from each field and sampling time were subsampled for fresh analysis within two days of receipt at Washington State University in Pullman, WA, with another subsample stored at −80°C for later biochemical analysis. On each sample time, 20 fruit from each field were weighed fresh, ten of which were dried in an oven at 80°C and reweighed to determine dry weight, while the other 10 fruit were left at room temperature (∼20°C) for two days and reweighed to estimate weight loss. Fruit firmness was measured as maximum penetration force (N) on opposite sides of another 25 fruit from each field with an automated penetrometer (Model GS-20 Fruit Texture Analyzer, Güss Manufacturing Ltd., Strand, South Africa) fitted with a 5-mm diameter convex cylinder set to a trigger threshold of 1.11 N and 6-mm depth. On each of these fruit, two external (on opposing shoulders) and two internal (adjacent to central cavity) color measurements (Model CR-300 Chroma Meter, Minolta Camera Co., Ltd., Ramsey, NJ) were taken using the L*a*b* color space expressed as lightness (L*), chroma (C*, [(a*)2+(b*)2]1/2) and hue angle (hab, tan−1[b*/a*])
In order to estimate the susceptibility of the strawberries to fungal rots, a subsample of 72 fresh fruit from each field and sampling time were placed in individual cells (6.7 cm×5.9 cm×5.7 cm deep) of plastic greenhouse inserts (Model IKN3601, ITML Traditional Series Inserts, Hummert International, Earth City, MO). Two inserts with 36 berries in each were placed in trays with dampened paper to maintain a saturated atmosphere and in sealed, black plastic bags. For both sample months in 2004, fruit were incubated at 15.5°C for 9–10 days, with the number of rotted berries counted each day. All rotted fruit were removed from their cells until all fruit had rotted. The principal fungal rot observed on the berries was gray mold (
For analysis of antioxidant activity, ascorbic acid, total phenolics, anthocyanins, and total and reducing sugars, chemicals and enzymes were purchased from Sigma-Aldrich Corp. (St. Louis, MO), unless otherwise noted. Spectrophotometric measurements were made using a UV-visible spectrophotometer (Model HP8453, Hewlett-Packard Co., Palo Alto, CA) with UV-Visible ChemStation software [Rev. A.08.03(71), Agilent Technologies, Inc., Santa Clara, CA]. All solutions were made up using ultrapure water (NANOpure DIamond Analytical, Barnstead International, Dubuque, IO). Centrifugation was performed in a Eppendorf 5417 R microcentrifuge (Engelsdorf, Germany). There were 3–5 separate replicates of pooled tissue from a minimum of five fruit analyzed in each biochemical assay, with duplicate instrument measurements made on each replicate. Outlying data were discarded and the tissue reanalyzed.
Antioxidant activity of hydrophilic and lipophilic fractions
Total ascorbic acid (reduced AsA plus dehydroascorbic acid, DHA) in the berries was measured as originally described by Foyer
Total phenolic compounds in the berries were measured with the Folin-Ciocalteu (F–C) phenol reagent (2 N) according to revised methods of Singleton
For anthocyanins, 0.5 g of powdered, frozen berry tissue was extracted in 1 mL 1% (v/v) HCl-methanol. After storage for 24 h at −20°C, sample tubes were centrifuged at 14 K rpm for 10 min at 4°C. Extraction with HCl-methanol was repeated 2×. Following centrifugation on day four, supernatants were decanted into 15 mL plastic tubes and made up to 3-mL volumes with HCl-methanol. Anthocyanin concentrations were determined by measuring absorbance of 250 mL extract in 750 mL 1% (v/v) HCl-methanol in 1.4 mL quartz cuvettes at 515 nm with a UV-visible spectrophotometer
Specific polyphenolic compounds were extracted by grinding 0.1 g frozen, powdered fruit tissue in 1.5 mL pure methanol. Concentrations of aglycones of ellagic acid, quercetin, kaempferol, phloretin, and naringenin enantiomers were determined, as well as the total aglycone plus glycoside polyphenols, following enzymatic hydrolysis with β-glucuronidasefrom
Reducing and total sugars were measured by the Nelson-Somogyi micro-colorimetric method
We also conducted consumer-sensory analyses of strawberries, including flavor, sweetness, appearance, juiciness, tartness, and overall acceptance. Strawberries were evaluated by consumer-sensory panels at four different sampling dates (20 panelists per field pair in Sept 2004 and 25 panelists per field pair in April, June, and Sept 2005) at WSU's Food Science and Human Nutrition Sensory Laboratory. Panelists were recruited using advertising from the Washington State University community based on their availability. A minimum amount of information on the nature of the study was provided in order to reduce potential bias. All participants signed an Informed Consent Form per project approval by the WSU Institutional Review Board.
Each panelist completed a demographic questionnaire prior to the start of the panel. Fifty-eight percent of the panelists were females. The age distribution of the panelists was 31% 18–25 years old, 41% 26–35 years old, 10% 36–45 years old, 13% 46–55 years old, and <5% over 55 years old. Over 70% of the panelists ate fresh strawberries every two weeks to every month, with 19% eating fresh strawberries every week. The majority of panelists (59%) preferred fresh strawberries that tasted more sweet than tart and another 36% preferred them at least equally sweet and tart. Less than 5% of the panelists preferred them more tart than sweet or had no preference.
Each consumer received organic and conventional berries from two, matched field pairs. Consumers were presented with two strawberry halves from two individual strawberries. Each sample was presented in a monadic, randomized serving order with assigned three-digit codes. Each panelist was also provided with deionized, filtered water and unsalted crackers for cleansing the palate between samples.
Consumers evaluated each strawberry sample for overall acceptance, as well as perceived intensity of flavor, juiciness, sweetness, and sourness using a discrete 9-point, bipolar hedonic/intensity scale, where 1 = dislike extremely/extremely low intensity and 9 = like extremely/extremely high intensity, according to ISO standards for quantitative response scales
Mixed model analyses of variance were used to test for differences in response variable means, except where noted, due to varieties (‘Diamante’, ‘Lanai’, and ‘San Juan’), treatments (organic and conventional), and months (April, June, and September). A split plot model pooled over two years was selected with variety as the whole plot factor, treatment as the subplot factor, and month as a repeated measure (SAS Proc Mixed, SAS Institute, 1999). Transformations were used to improve normality and homogeneity of variances where necessary. When data were transformed, LS means were reported in original units. When significant interactions were identified, differences in simple effect means were identified using Fisher's least significant differences. The same mixed model analysis of variance was applied to examine sensory data by using the average panel score for each attribute. The Kaplan-Meier (Product Limit) method was used to model the survival function and estimate mean survival time, that is, days to rotting (SAS Proc Lifetest, SAS Institute, 1999). The generalized Savage (Log-Rank) test for equality of survival functions was used to test for differences in time to rotting for organic versus conventional conditions
Soils were sampled from 30-cm raised mounds at 0–10 cm and 20–30 cm depths in June 2004 and June 2005 and at 0–10 cm in April 2005. All samples were a composite of 10–15 subsamples taken at random from within 8 to 15 rows and were always a minimum of 20 m from the boundary within each field pair to avoid edge effects. Samples from the June sampling dates were shipped for chemical analyses to Soiltest Farm Consultants and for biological analyses to Washington State University by overnight mail. Samples from the April 2005 sampling were shipped to Oak Ridge National Lab for microarray analyses and stored at −20°C. Raw microarray data are in
At Soiltest Farm Consultants, soil samples were passed through a 2-mm sieve, stored at 4°C, and then analyzed for the following properties according to recommended soil-testing methods by Gavlak et al.
At Washington State University, we analyzed total C and N by combustion using a Leco CNS 2000 (Leco Corporation, St. Joseph, MI). Readily mineralizable carbon (MinC), basal microbial respiration, and active microbial biomass (MicC) by substrate-induced respiration were measured according to Anderson and Domsch
June comparisons of soil under organic and conventional management were analyzed as a randomized complete block design with split plot. Year served as whole plot and treatment as subplot. The two depth intervals were analyzed separately. All statistics were analyzed using the SAS system for Windows version 9.1 ANOVA and LS means (SAS Institute, 1999). Data were checked for model assumptions and transformed as necessary. When data were transformed, LS means were reported in original units.
Soil community DNA was extracted using an SDS-based method
Thirty to 150 ng purified DNA from each soil was randomly amplified using rolling circle PCR with a GenomiPhi DNA amplification kit (GE Healthcare, Piscataway, NJ)
Microarray slide images were converted to TIFF files and hybridized DNA quantified using ImaGene software 6.0 (Biodiscovery Inc., Los Angeles, CA)
Data were analyzed using a randomized complete block design, with field pair as block. Average signal intensity for each of the 1711 detected genes, sum of signal intensities for all 1711 detected genes, and sum of signal intensities for each of the 11 functional groups from the eight organically farmed soils and the eight matched conventionally farmed soils were analyzed by paired t-tests. Gene diversity was calculated overall and for each functional group using a modified version of Simpson's Reciprocal Index [D = 1/[∑n(n−1)/N(N−1)], where n = signal intensity of a single gene with an SNR>2 and N = sum of all signal intensities with an SNR>2 on the entire slide]. Diversity values were then analyzed by a paired t-test. Detected endemic genes were counted based on treatment means. Proportion comparison z tests were used to compare proportion of detected endemic genes in each management system.
Two gene sequences endemic to conventionally managed field soils and 233 sequences endemic to organically managed field soils, and the organisms from which probes were designed.
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Agrichemical inputs (insecticides, fungicides, herbicides, molluscides, adjuvants, fumigants, and fertilizers) applied to 26 strawberry fields during the 2004 and 2005 growing seasons.
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Strawberry varieties, soil sampling dates, soil types, and soil classification for field pairs in the study.
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Fruit, leaf, and sensory properties (mean ± standard error) for ‘Diamante’ and ‘San Juan’ strawberry cultivars from organic (ORG) and conventional (CON) farms in June and September 2004 and April, June, and September 2005.
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Soil properties (mean ± standard error) at two depths (0–10 cm and 20–30 cm) from organic and conventional strawberry farms in June 2004 and June 2005.
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Raw data for all slides.
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Data by functional groups (SNR>2.0).
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Normalized data by functional groups.
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Normalized data for diversity analysis.
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We thank Tom Sjulin of Driscoll's Strawberry Associates and Larry Eddings of Pacific Gold Farms for assistance with farm selection and fruit processing. We thank Linda Klein, Amit Dhingra, Patricia Ericsson, David Huggins, Jeff Smith, and two anonymous reviewers for comments on drafts of this manuscript. We thank Jan Dasgupta, Marc Evans, Gregory Peck, Sean Swezey, Carolina Torres, Canming Xiao, and Jaime Yáñez for technical assistance.