In agriculture, plant growth promoting bacteria (PGPB) are increasingly used for reducing environmental stress‐related crop losses through mutualistic actions of these microorganisms, activating physiological and biochemical responses, building tolerances within their hosts. Here we report the use of radioactive carbon‐11 (
Climate change is a defining challenge of the 21st century, and this decade is a critical time for action to mitigate the worst effects on human populations and ecosystems. Plant science can play an important role in developing crops with enhanced resilience to harsh conditions (e.g. heat, drought, salt stress, flooding, disease outbreaks) and engineering efficient carbon-capturing and carbon-sequestering plants. Here, we present examples of research being conducted in these areas and discuss challenges and open questions as a call to action for the plant science community.
more » « less- NSF-PAR ID:
- 10389081
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- The Plant Cell
- Volume:
- 35
- Issue:
- 1
- ISSN:
- 1040-4651
- Format(s):
- Medium: X Size: p. 24-66
- Size(s):
- ["p. 24-66"]
- Sponsoring Org:
- National Science Foundation
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Summary Few studies have systematically investigated the effects of subsetting strategies on soil modelling or explored the potential of emergent methods from other fields not previously applied to pedometrics. This study considers smallholder agricultural villages in southern India that have been understudied in terms of chemometric modelling intended to support soil health, fertility and management. Therefore, the objective was to investigate the application of visible near‐infrared spectroscopy and chemometrics to predict soil properties in this setting. In addition, this study evaluated the effects of methods of calibration subsetting and new parametric models on the prediction of soil properties. These novel methods were transferred from the genomics field to soil science. Three strategic subsetting methods were used to produce calibration subsets that consider the variation in the soil properties, the spectra and both together; this is in addition to standard random calibration subsetting. Partial least squares regression (PLSR) and two methods from genomics that impose variable reduction were used for modelling; the latter were sparse PLSR (SPLSR) and the heteroscedastic effects model (HEM). Soil samples were collected from two villages and analysed for texture, soil carbon and available macro‐ and micro‐nutrients. The results showed that soil texture and carbon could be predicted moderately to strongly, whereas plant nutrient properties were predicted poorly to moderately. Random subsetting and subsetting by property distribution were more appropriate when spectra varied less overall, whereas subsetting that incorporates variation in spectra and properties improved results when spectral variation increased. The SPLSR and HEM models improved results over PLSR in some cases, or at least maintained prediction strength while using fewer predictors. Subsetting methods improved prediction results in 75% of cases. This study filled an important research gap by systematically studying local subsetting behaviour under different degrees of spectral and attribute variation.
Highlights Explored new calibration subsetting methods and chemometric models in soil spectral modelling.
Compared the methods and models for 17 soil properties in an understudied area of India.
Random subsetting was not always optimal; subsetting matters and depends on data characteristics.
Sparse models from genomics performed better in 75% of cases than a standard method.
