Abstract
The aim of this study is to develop an integrated approach to soil quality and fertility assessment in high-yielding rice agro-ecosystems threatened due to overexploitation of soil resources by intensive agriculture. The proposed approach is implemented considering representative pilot fields allocated throughout a study area based on the assumption that soils of similar general properties present a similar nutritional status due to common long-term management practices. The analysis includes (a) object-based image analysis for land zonation, (b) hot-spot analysis for sampling scheme evaluation, (c) setting of critical thresholds in soil parameters for detecting nutrient deficiencies and soil quality problems, and (d) Redundancy Analysis, TITAN analysis, and multiple regression for identifying individual or combined effects of general soil properties (e.g., organic matter, soil texture, pH, salinity) or non-soil parameters (e.g., topographic parameters) on soil nutrients. The approach was applied using as a case study the large rice agro-ecosystem of Thessaloniki plain in Greece considering some site specificities (e.g., high rice yields, calcareous soils) when setting the critical thresholds in soil parameters. The results showed that (a) 62.5% of the pilot fields’ coverage has a simultaneous deficiency in Zn, Mn, and B, (b) organic matter (OM) was the most significant descriptor of nutrients’ variance, and its cold spots (clustered regions of low OM values) showed important overlapping with the cold spots of K, Mg, Zn, Mn, Cu, and B, (c) a higher rate of availability increase in P, K, Mg, Mn, Zn, Fe, Cu, and B was observed when the OM ranged between 2 and 3%, and (d) the multiple regression models that assess K and P concentrations based on general soil properties showed an adequate performance, allowing their use for general assessment of their soil concentrations in the fields of the whole agro-ecosystem.
Highlights
The green revolution and the continuous introduction of advanced agricultural equipment and genetically improved rice varieties for achieving higher yields has substantially increased the pressures on natural resources, such as water and soil [1,2,3]
The high probability of soil quality degradation due to intensive agriculture may inhibit the increase of yields, and this had been already indicated more than two decades ago by Cassman and Pingali [9,10], which presented long-term experiments (20–25 years) of irrigated rice systems in different environments and soils that started during the 1960s and 1970s
The conditional effect of a descriptor variable is equal to the additional amount of variance in the assemblages of target variables explained by the corresponding descriptor variable at the time that was included into the model during a selection procedure [74,76]
Summary
The green revolution and the continuous introduction of advanced agricultural equipment and genetically improved rice varieties for achieving higher yields has substantially increased the pressures on natural resources, such as water and soil [1,2,3]. The high probability of soil quality degradation due to intensive agriculture may inhibit the increase of yields, and this had been already indicated more than two decades ago by Cassman and Pingali [9,10], which presented long-term experiments (20–25 years) of irrigated rice systems in different environments and soils that started during the 1960s and 1970s. Even though the rice varieties used in their experiments were regularly replaced with the highest-yielding and the most resistant germplasm to diseases and insects available at each point in time, negative yield trends were observed, probably due to soil quality degradation. The current yield trends are insufficient to achieve this goal [11], and there is general evidence that the relative yield gain decreases over time, while in many cases yield plateaus or abrupt decreases in the rate of yield gain have been observed, including rice in eastern Asia and wheat in Northwest Europe, which share a very large portion of total global rice and wheat production [12]
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