Foro Global sobre Seguridad Alimentaria y Nutrición (Foro FSN)

Dear All, 

One major concern of poor nutrient use efficnecy has been how we have addressed hisatorically the nutrient applications. Whether we have sound mechanisms to measure good soil health indicators.  I quote from my recent research paper as

Why concern about soil – health?

          Our soil is continuing to undergo increasing stress from alarming pace of population growth, of vegetation line, soil degradation, increasing concentration of population in soil, climate change and loss of bio-diversity. There is extreme  change in the global climate on one side of the country there is drought and in other part unseasonable and erratic precipitation adversely affect the crop production resulting in serious impact in our existing agricultural growth. Modernization of agriculture has considerably affected the physical and chemical constraints like formation of traffic soil pan, soil crusting, soil structure deterioration due to decline in organic matter, soil nutrient depletion, non-judicious water management. All these constraints will badly reflect soil health and crop production.

Major factors affecting soil quality

          The major causes of poor soil quality are :

1. Wide gap between nutrient demand and supply
2. High nutrient turnover in soil plant system coupled with low and imbalanced fertilizeruse.
3. Emerging deficiency of secondary and micronutrients due to improper use of inputs such as water, fertilizers, pesticides, etc.
4. Insufficient use of organic inputs
5. Acidificaiton and Al3+ toxicity
6. Development of adverse soil conditions such as heavy metal toxicity
7. Disproportionate growth of microbial population responsible for soil sickness and erosion, deforestation due to rapid urbanization and industrialization.

Quantitative assessment of soil quality

          There are mainy approaches that are used for soil quality evaluation per se soil health. In all these cases minimum data set (MDS) can be used. Among them, the following approaches are  important to assess soil health condition.

1. Comparative assessment: Here the performance of a system can be evaluated in relation to alternatives at a given time only. For example, after 15-25 years of cultivation, some quality parameters get changed over initial soil quality attributes due to land-use-management practices, particularly in long term soil fertility experiments under different cropping system.

1. Dynamic assessment: In this case, a performance of a system can be evaluated in relation to alternative across time. The dynamic assessment approach should includes many steps viz., explicit identification of the desired outputs of management (productivity, erodability, human, animal health etc.). Then assessment of design of the system to determine if it will produce the desire output. Identification of soil quality parameters, establishment of starting point, historical record of the site should be maintained and stabilization of a system process that is out-of-control. A stable system of variation is one of which the variation is solely a result of the system in place, and there are no special causes of variation.

1. Common statistical approach – Regression analysis: Here multiple linear regressions can be used to calculate soil quality index of crop productivity using soil attributes as important determining factors.
2. Pedotransfer functions: This is a mathematical function that relates soil characteristics and properties with one another using minimum data set for evaluation of soil quality. Many pedotransfer functions occur in the literature and are statistical or empirical in nature. Some selected PTSs are may be cation exchange capacity (CEC = a OC + bC relationship, bulk density, Db = f (OC+clay), change in organic matter, C = a + b OC and soil productivity, P1 – f (Db +AWHC +pH + EC +ARE).

1. Standardize scoring function based on threshold limits and base line values: Scoring functions are based on threshold limits and base line values (Karlen and Scott. 1994). These functions are used to transform the measured indicator values into performance-based score for soil quality index. In this approach of Relative Soil Quality Index (RSQI), for example 9 indicators were combined into an RSQI. The equation for calculating RSQI value is given below:

RSQI = (SQI/SQIm) x 100, here SQI = soil quality index; SQIm = Maximum    value of SQI

The maximum value of SQI for soil is 400 and the minimum value 100 (Wang and Gong 1998). SQI is calculated from the equation:

As SQI = ∑ WiIi ; Wi = Weights of indicators; Ii = the marks of the indicators classes as shown in 

1. Principal component analysis (PCA): The principal component analysis (PCA) is a useful multivariate statistical tool that has the advantage of generating relationships among many correlated variables into a few principle components (PCs). These can be classified as soil quality indicators with respect to the specific soil functions. Changes in the properties or soil attributes associated with a PC can be used to classify soil quality as aggrading, degrading or stable. In this method, four steps are followed: (i) define the goal, (ii) select a minimum data set (MDS) of indicators that best represent soil function, (iii) score the MDS based on performance of soil function and (iv)integrate the indicators scores into a comparative index of soil quality.

          Read my paper for more contents and clarity related to how developing a model soil health indicator can lead to better nutrient use efficency ultimately enhancing the fertilizer use efficiency.