An Integrated Approach to Assessing the Soil Quality and Nutritional Status of Large and Long-Term Cultivated Rice Agro-Ecosystems
Abstract
:1. Introduction
- rice was the first crop to be completely sequenced [18], providing availability of high-quality genomes and long evolutionary trajectories that contain a virtually untapped reservoir of genes and traits that can be used for breeding a new generation of sustainable rice cultivars, known as Green Super Rice [19]. The new era of rice varieties may reduce the risk of yield failure due to adverse environmental conditions but at the same time could be the reason for soil degradation through excessive nutrient depletion by high yields.
2. Materials and Methods
2.1. Study Area
2.2. Image Analysis
2.3. Soil and Topographic Parameters
2.4. Soil Quality and Nutrient Deficiency Thresholds
2.4.1. Soil Texture and Organic Matter
2.4.2. Soil EC, pH, and CaCO3
2.4.3. Nitrogen and Phosphorus
- The specific fields are continuously flooded with a large amount of nitrogen losses through water losses by surface and subsurface drainage [34] in comparison to other non-flooded cereal crops (e.g., maize) for which the aforementioned concentration ranges are mostly applied. High drainage rates are also applied as a strategy to avoid salinity build up though evapo-concentration [35,60].
- The total N uptake of rice for grain yields above 10 Mg ha−1 exceeds 200 kg ha−1 [51], and, considering the aforementioned indirect losses in the specific rice agro-ecosystems and their high yields, a higher critical N threshold of serious nitrogen deficiency is required.
2.4.4. Κ, Ca, and Mg
2.4.5. Fe, Zn, Mn, Cu, and B
2.5. Spatial Patterns of Soil Properties
2.6. Gradient Analysis
- Multiple gradients analysis: for assessing multiple inter-relations between multiple descriptor variables and multiple target variables.
- Single gradient analysis: for assessing the effect of a single descriptor variable on multiple target variables.
- Multiple regression: for assessing the effect of multiple descriptor variables on a single target variable.
2.6.1. Multiple Gradients Analysis: Ordination Methods and Variance Partitioning
2.6.2. Single Gradient Analysis: TITAN Method
2.6.3. Multiple Linear Regression
3. Results
3.1. Spatial Patterns of Soil Parameters and Quality/Nutritional Status of Rice Soils
- The regions of simultaneous deficiency in Zn, Mn, and B cover 62.5% of the total area of pilot fields.
- The regions of simultaneous pH > 7.5 and CaCO3 > 7.5% cover 39.7% of the total area of pilot fields.
- The regions of simultaneous deficiency in N, P, and K cover 22.5% of the total area of pilot fields.
3.2. Multiple Interrelations between Descriptor Variables and Target Variables: RDA Analysis
- C and OM values are negatively correlated with dist and NO3N values and positively correlated with P, K, Fe, Mg, Mn, Cu, Zn, and B. Thus, soils closer to the coastline are more likely to have higher C and higher OM, while they also have a lower probability to present P, K, Fe, Mg, Mn, Cu, Zn, and B deficiency. On the other hand, the soils with lower OM and C values that are far from the coastline are more likely to show higher values of NO3N and a higher probability to present P, K, Fe, Mg, Mn, Cu, Zn, and B deficiency.
- EC is negatively correlated with pH and positively correlated with P. Thus, soils of higher EC are more likely to show higher concentrations of P and lower pH values.
3.3. Single Gradient Analysis: Association of a Single Descriptor Variable with Multiple Target Variables
3.4. Regression Models
4. Discussion
4.1. The Role of Each Type of Analysis in Developing Efficient Integrated Interventions for Improving Soil Quality and Productivity
4.2. Interesting Observations Made in the Context of This Case Study
4.3. Uncertainties Regarding Quality/Deficiency Thresholds
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Parameter | Unit | Transform. | Abbrev. | Min | Max | Mean | St.dev. | Group |
---|---|---|---|---|---|---|---|---|
Longitude (WGS84) | Dec. degrees | log(x + 1) | long | 22.5500 | 22.8447 | 22.7379 | 0.0573 | 1 |
Latitude (WGS84) | Dec. degrees | log(x + 1) | lat | 40.5249 | 40.7107 | 40.6242 | 0.0444 | 1 |
Altitude | m a.s.l. | log(x + 1) | alt | 0 | 34 | 6.52 | 4.55 | 1 |
Distance from the shore | Km | log(x + 1) | dist | 0 | 12 | 4.85 | 3.28 | 1 |
Sand | % | arcsin(x/100)0.5 | S | 4 | 84 | 25.09 | 14.87 | 1 |
Silt | % | arcsin(x/100)0.5 | Si | 2 | 88 | 44.27 | 9.99 | 1 |
Clay | % | arcsin(x/100)0.5 | C | 2 | 90 | 30.64 | 15.63 | 1 |
Organic matter 1 | % | arcsin(x/100)0.5 | OM | 0.98 | 7.73 | 2.37 | 0.74 | 1 |
CaCO3 | % | arcsin(x/100)0.5 | CO3 | 1.3 | 17.3 | 8.11 | 2.76 | 1 |
pH | - | none | pH | 7.12 | 8.52 | 7.92 | 0.20 | 1 |
Electrical conductivity | mS cm−1 | log(x + 1) | EC | 0.367 | 9.038 | 1.42 | 0.99 | 1 |
Nitrate nitrogen | ppm | log(x + 1) | NO3N | 0.18 | 69.57 | 6.85 | 5.47 | 2 |
Olsen Phosphorus | ppm | log(x + 1) | P | 1.69 | 166.96 | 25.31 | 33.90 | 2 |
Calcium exchangeable 2 | ppm | - | Ca | >2000 | >2000 | >2000 | >2000 | 2 |
Potassium exchangeable | ppm | log(x + 1) | K | 51.00 | 1762.00 | 329.47 | 222.46 | 2 |
Magnesium exchangeable | ppm | log(x + 1) | Mg | 83.00 | 1613.00 | 597.82 | 286.96 | 2 |
Iron | ppm | log(x + 1) | Fe | 17.88 | 211.73 | 72.54 | 32.78 | 2 |
Zinc | ppm | log(x + 1) | Zn | 0.21 | 8.10 | 1.28 | 0.84 | 2 |
Manganese | ppm | log(x + 1) | Mn | 3.88 | 60.65 | 16.90 | 9.49 | 2 |
Copper | ppm | log(x + 1) | Cu | 1.14 | 19.81 | 7.38 | 2.69 | 2 |
Boron | ppm | log(x + 1) | B | 0.10 | 2.17 | 0.54 | 0.30 | 2 |
Parameter | Critical Threshold | % of Total Area of Pilot Fields * |
---|---|---|
Sand | >50% | 5.8% |
Clay | <20% | 26.7% |
Organic matter | <2% | 26.5% |
Calcium carbonate | >7.5% | 53.5% |
pH | >7.5 | 96.6% |
Electrical conductivity | >2 mS cm−1 | 24.6% |
Nitrate Nitrogen | <40 ppm | 99.6% |
Olsen Phosphorus | <25 ppm | 73.9% |
Potassium exchangeable | <175 ppm | 24.0% |
Calcium exchangeable | <200 ppm | 0.0% |
Magnesium exchangeable | <120 ppm | 0.1% |
Iron | <5 ppm | 0.0% |
Zinc | <2 ppm | 85.3% |
Manganese | <20 ppm | 78.6% |
Copper | <0.9 ppm | 0.0% |
Boron | <0.7 ppm | 80.5% |
Target var. → | NO3N | P | K | |||
Descriptor var. ↓ | Coef. | 95% HPDD | Coef. | 95% HPDD | Coef. | 95% HPDD |
Constant | 0.941 ** | (0.787, 1.087) | 3.544 ** | (2.305, 5.078) | 2.882 ** | (2.173, 3.753) |
dist | 0.169 ** | (0.112, 0.225) | 0.16 ** | (0.074, 0.234) | −0.109 ** | (−0.154, −0.065) |
C | −0.181 ** | (−0.315, −0.042) | 0.374 ** | (0.172, 0.547) | 0.852 ** | (0.732, 0.980) |
OM | - | - | 2.769 ** | (1.384, 4.248) | 3.054 ** | (2.180, 4.025) |
CO3 | −0.435 * | (−0.781, −0.106) | −1.166 ** | (−1.691, -0.63) | 0.846 ** | (0.565, 1.195) |
pH | - | - | −0.397 ** | (−0.572, −0.243) | −0.203 ** | (−0.306, −0.116) |
EC | - | - | 1.177 ** | (0.943, 1.402) | 0.193 ** | (0.084, 0.289) |
F-ratio | 29.26 | 90.09 | 222.52 | |||
P-value | <0.001 | <0.001 | <0.001 | |||
R2adj.df | 0.137 | 0.500 | 0.713 | |||
Target var. → | Mg | Fe | Zn | |||
Descriptor var. ↓ | Coef. | 95% HPDD | Coef. | 95% HPDD | Coef. | 95% HPDD |
Constant | 1.664 ** | (1.582, 1.746) | 3.096 ** | (2.332, 3.913) | 0.601 ** | (0.141, 1.116) |
dist | - | - | −0.071 ** | (−0.108, −0.031) | −0.058 ** | (−0.092, −0.022) |
C | 0.839 ** | (0.728, 0.957) | - | - | - | |
OM | 1.909 ** | (1.205, 2.623) | 3.013 ** | (2.188, 3.750) | 2.101 ** | (1.484, 2.721) |
CO3 | 1.126 ** | (0.912, 1.361) | - | - | - | - |
pH | - | - | −0.223 ** | (−0.315, −0.136) | −0.069 ** | (−0.126, −0.016) |
EC | - | - | 0.25 ** | (0.117, 0.386) | - | - |
F-ratio | 331.57 | 103.62 | 60.03 | |||
P-value | <0.001 | <0.001 | <0.001 | |||
R2adj.df | 0.649 | 0.434 | 0.248 | |||
Target var. → | Mn | Cu | B | |||
Descriptor var. ↓ | Coef. | 95% HPDD | Coef. | 95% HPDD | Coef. | 95% HPDD |
Constant | 0.673 ** | (0.527, 0.808) | 0.435 ** | (0.324, 0.541) | 0.057 * | (0.004, 0.113) |
dist | −0.16 ** | (−0.205, −0.115) | −0.076 ** | (−0.106, −0.047) | −0.038 ** | (−0.058, −0.017) |
C | 0.384 ** | (0.278, 0.490) | 0.219 ** | (0.147, 0.294) | - | |
OM | 1.823 ** | (1.056, 2.559) | 3.192 ** | (2.596, 3.789) | 0.687 ** | (0.367, 1.010) |
CO3 | 0.301 * | (0.009, 0.619) | −0.313 * | (−0.510, −0.094) | - | - |
pH | - | - | - | - | - | - |
EC | 0.191 ** | (0.085, 0.296) | - | - | 0.121 ** | (0.074, 0.171) |
F-ratio | 95.88 | 148.42 | 40.61 | |||
P-value | <0.001 | <0.001 | <0.001 | |||
R2adj.df | 0.470 | 0.524 | 0.181 |
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Aschonitis, V.; Karydas, C.G.; Iatrou, M.; Mourelatos, S.; Metaxa, I.; Tziachris, P.; Iatrou, G. An Integrated Approach to Assessing the Soil Quality and Nutritional Status of Large and Long-Term Cultivated Rice Agro-Ecosystems. Agriculture 2019, 9, 80. https://doi.org/10.3390/agriculture9040080
Aschonitis V, Karydas CG, Iatrou M, Mourelatos S, Metaxa I, Tziachris P, Iatrou G. An Integrated Approach to Assessing the Soil Quality and Nutritional Status of Large and Long-Term Cultivated Rice Agro-Ecosystems. Agriculture. 2019; 9(4):80. https://doi.org/10.3390/agriculture9040080
Chicago/Turabian StyleAschonitis, Vassilis, Christos G. Karydas, Miltos Iatrou, Spiros Mourelatos, Irini Metaxa, Panagiotis Tziachris, and George Iatrou. 2019. "An Integrated Approach to Assessing the Soil Quality and Nutritional Status of Large and Long-Term Cultivated Rice Agro-Ecosystems" Agriculture 9, no. 4: 80. https://doi.org/10.3390/agriculture9040080