Digital soil assessment for quantifying soil constraints to crop production: a case study for rice in Punjab, India

• Published in: Soil use and management Vol. 34; no. 4; pp. 533 - 541
• Main Authors: Okonkwo, E. I., Corstanje, R., Kirk, G. J. D.
• Format: Journal Article
• Language: English
• Published: Bedfordshire Wiley Subscription Services, Inc 01-12-2018
• Subjects: Agricultural production; Alkalinity; Case studies; Computer simulation; Criteria; Crop production; Digital mapping; Digital soil mapping; Electrical conductivity; Electrical resistivity; Flexibility; Food production; Land capability; Monte Carlo simulation; problem soils; Rice; Salinity; Salinity effects; Soil; Soil dynamics; Soil mapping; Soil properties; Soil salinity; soil spatial variability; Spatial variations; Statistical methods; Uncertainty; Punjab India; India
• ISSN: 0266-0032; 1475-2743
• DOI: 10.1111/sum.12446

Assessments of land capability for particular functions such as food production need to allow for uncertainties both in the criteria used to specify the function and in information on relevant soil properties. In this paper, we evaluate the use of digital soil assessment (DSA) for dynamic assessment of soil capability allowing for both uncertainties and spatial variability in soil properties and flexibility in the values of assessment criteria. We do this for soil constraints to rice production in the state of Punjab, India, where soil salinity and alkalinity are potentially important constraints to cropping. In DSA, spatial predictions of soil properties and associated uncertainties made with digital soil mapping (DSM) are used to assess soil functions. We use a combination of DSM and Monte Carlo simulation methods to estimate the spatial variation in soil electrical conductivity (ECe) and pH to 20 cm depth in soils across Punjab. We then use the estimates and associated uncertainties to assess the likelihood that soil salinity or alkalinity or both could constrain rice production. Results show that allowing for prediction uncertainties of soil attributes results in far smaller areas affected by salinity (1.2 vs. 2.0 Mha) and alkalinity (3.0 vs. 3.2 Mha). Results also show the importance of correctly setting threshold values for constraint criteria and the flexibility of the DSA approach for setting thresholds.