Status of Vulnerability in Wheat and Barley Producing States of India

Sendhil, R; Jha, Ankita; Kumar, Anuj; Singh, S. V.; Kharub, Ajit Singh

doi:10.25174/2249-4065/2017/70082

Cited by 8 publications

(10 citation statements)

References 2 publications

(2 reference statements)

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“…For each of these test cases, the best chemometric model and the best transformation of spectra and individual analytes were selected based on the R 2 , RMSE, bias and RPD values. We also classified both the observed and estimated soil parameters using a three‐tier STCR rating scheme generally used for Indian soils (Sendhil et al, 2018). The classification accuracy for a given parameter in the specific district and in the pooled dataset was estimated by calculating the percentage of locations for which estimated and observed values of a parameter yielded identical STCR ratings.…”

Section: Methodsmentioning

confidence: 99%

“…Based on a meta-analysis of soil spectroscopic studies conducted between 1990 and 2019, Ahmadi et al (2021) have observed that about 69.6% of research papers used PLSR (62.3%) and modified PLSR models followed by SVR (9.9%) models. Modified PLSR models such as PLSRFS (Sarathjith et al, 2016) and PLSRLW (Gupta et al, 2018) have also been known to perform better than PLSR models (Ng et al, 2018;Dorantes et al, 2022). Therefore, we used the SVR and PLSRFS models in the remainder of this study to estimate 14 nutrient contents and corresponding SQI values.…”

Section: Collection Of Soil Spectra and Chemometric Modellingmentioning

confidence: 99%

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Diffuse reflectance spectroscopy (DRS) for rapid soil testing and soil quality assessment in smallholder farms

Majeed

Garg

Venkataradha

et al. 2023

European J Soil Science

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Rapid soil testing and soil quality assessment are essential to address soil degradation and low farm incomes in smallholder farms. With the objective of testing diffuse reflectance spectroscopy (DRS) to rapidly assess soil chemical properties, nutrient content and a soil quality index (SQI), samples of surface soil were collected from 1113 smallholder farms in seven districts in Bundelkhand region of Uttar Pradesh, India. A minimum dataset (MDS) approach was followed to estimate SQI using the three chemical parameters of soil pH, electrical conductivity (EC) and soil organic carbon (SOC), and 11 different soil nutrients. Principal component and correlation analyses showed that soil pH, SOC content and three available nutrients À copper (Cu), iron (Fe) and sulphur (S) À may constitute the MDS. Estimated SQI values showed strong positive correlation with crop yields. Results of chemometric modelling showed that the DRS approach could yield the coefficient of determination (R 2 ) values in the validation datasets ranging from 0.79 to 0.94 for exchangeable calcium (Ca) followed by 0.67-0.88 for exchangeable potassium (K), 0.52-0.86 for SOC and 0.53-0.81 for available boron (B) content. Except in one district, the DRS approach could be used to estimate SQI values with R 2 values in the range of 0.63-0.81; an R 2 value of 0.71 was obtained in the pooled dataset. We also estimated the three-tier soil test crop response (STCR) ratings to compare DRS and wet chemistry soil testing approaches. Similar STCR ratings were obtained for both these approaches in more than 86% of the samples. Parameters for which both the methods yielded similar ratings in more than 80% of the samples were EC (>98%), pH and exchangeable Ca (>81%) and available B (>89%). With similar ratings, these results suggest that the DRS approach may safely be used for farmers' fields, replacing the traditional wet analysis approach of soil testing.

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Section: Methodsmentioning

confidence: 99%

Section: Collection Of Soil Spectra and Chemometric Modellingmentioning

confidence: 99%

Diffuse reflectance spectroscopy (DRS) for rapid soil testing and soil quality assessment in smallholder farms

Majeed

Garg

Venkataradha

et al. 2023

European J Soil Science

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“…The Indo-Gangetic plains (IGP) comprise north western plains zone (NWPZ) and north eastern plains zone (NEPZ) which is the major wheat tract covering around 21 million hectares, followed by central zone (CZ), peninsular zone (PZ) and northern hills zones (NHZ). The whole cropped area under wheat in India experiences heat stress under changing climatic scenario and production especially gets affected in high vulnerable central region of India (Sendhil et al, 2018(Sendhil et al, & 2017b. The CZ and PZ experiences heat stress all through the crop season and significant parts of NWPZ and NEPZ experiences terminal heat (Tiwari et al, 2017).…”

Section: Study Regionmentioning

confidence: 99%

Identification of climate change induced heat stress sensitive environments and prediction for diverse representative concentration pathways – A novel approach for tracking hotspots

Sendhil

Ghimire

Rinki

et al. 2021

Preprint

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Climate change is unequivocal across economies and India owing to its distinct geography has been exposed to several climatic risks, especially in agriculture. Heat stress is a serious environmental problem posed by climate change, wherein mean temperature is expected to increase relatively more during wheat growing season affecting production and food security. In the milieu , the investigation is pioneered to predict heat stress sensitive wheat growing environments in India for research prioritization using a long term (30 years) historical daily data. The study has developed a methodological approach by integrating statistical downscaling of climate information and principal component analysis for computing heat stress intensity index (HSII) for 17 experiment locations across wheat growing environments. HSII were estimated for existing locations post testing for Levene’s homogeneity of variance, followed by prediction for three periods’ viz., early-future (2026-2050), mid-future (2051-2075) and far-future (2076-2100) under two emission scenarios namely RCP4.5 and RCP8.5. The results alarmed a radical shift in HSII of experiment locations from one period to another in both scenarios. Experiment locations with high index values for the existing environment has moved almost to lower category in the early future and subsequently shifted to higher position in the mid-future and far-future. The investigation also found that under projected RCP4.5, trial locations in peninsular zone need more emphasis, whereas in RCP8.5, peninsular zone coupled with central zone and north eastern plains zone have to be focused. Overall, the study develops a pragmatic approach in location prioritization across predicted periods which can be replicated to other regions. On policy front, rational allocation of research funds has been suggested to carry out field trials on climate change induced heat stress sensitive environments for sustaining the national wheat production apart from developing micro-level adaptation strategies to counter adverse effects of climate change.

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“…Uttar Pradesh has the largest share ofthe area with 9.75 million hectares (32%), followed by Madhya Pradesh (18.75%), Punjab (11.48%), Rajasthan (9.74%), Haryana (8.36%) and Bihar (6.82%). However, a major expansion in the wheat area was observed in the states such as Jharkhand (51%), Madhya Pradesh (27%), and Rajasthan (13%) Sendhil et al (2012). State-wise comparison of area and production for 2017-2018 showsthat Uttar Pradesh, Punjab, Madhya Pradesh, and Haryana were the major contributors to the national production.…”

Section: Introductionmentioning

confidence: 99%

Effect of Humic acid on the Growth Attributes of Wheat (Triticum aestivum L.) Crop in sandy loam soil of Gwalior region of Madhya Pradesh, India

Sharma¹,

Tripathi²,

Tyagi³

et al. 2023

EEC

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A field experiment was conducted during Rabi season 2019-2020 to study the Effect of humic acid on the growth and yield attributes of wheat crop in sandy loam soil of Gwalior region of Madhya Pradesh, on Crop Research Centre, School of Agriculture (SoAg.), ITM University Gwalior M.P., India. The experiment was laid out in a Factorial Randomized Block design (F-RBD) replicated three times with three levels of Recommended Dose of Fertilizer (RDF), i.e. (RDF @ 0, 50 and 100 %) and two levels of Humic Acid (HA) (HA @ 500 ppm and 1250 ppm) thereby making eight treatment combinations with 24 plots. Treatments were; T1- Absolute control, T2 -RDF @ 100 %, T3-RDF @ 0 % +HA1 @ 500 ppm, T4-RDF @ 0 % + HA2 @ 1250 ppm, T5-RDF @ 50 % +HA1 @ 500 ppm, T6-RDF @ 50 % +HA2 @ 1250 ppm, T7-RDF @ 100 % +HA1 @ 500 ppm and T8-RDF @ 100 % + HA2 @ 1250 ppm. Among the different levels of Recommended Dose of Fertilizer (RDF) and humic acid were recorded the highest values for all parameters viz; plant population m-2, plant height (cm), numbers of tillers per m-2, numbers of leaves per culm-2, root length (cm), dry weight (g) and fresh weight (g) of the plant under the treatments T8- RDF @ 100 % + HA2 @ 1250 ppm concentration. The means value for all parameters are; plant population m-2 (16.86 and 16.00 at 30 and 60 DAS), plant height (10.03, 58.05 and 76.08 cm at 30, 60 and 90 DAS), numbers of tillers per m-2 (5.88 and 7.02 at 30 and 60 DAS), numbers of leaves per culm-2 (3.90, 4.94 and 3.59 at 30, 60 and 90 DAS), Root length -7.01, 9.00, 11.06 and 12.03 cm, Dry weight of plant-1 0.39, 5.31 and 37.54 g and Fresh weight of plant-1 4.01, 40.01 and 350.71 g in 30, 60 90 DAS and harvest stage.

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Status of Vulnerability in Wheat and Barley Producing States of India

Abstract: Global warming is unequivocal (IPCC, 2007) and developing countries are likely to be affected adversely by the impact of climate change (henceforth 'CC').

Cited by 8 publications

References 2 publications

Diffuse reflectance spectroscopy (DRS) for rapid soil testing and soil quality assessment in smallholder farms

Diffuse reflectance spectroscopy (DRS) for rapid soil testing and soil quality assessment in smallholder farms

Identification of climate change induced heat stress sensitive environments and prediction for diverse representative concentration pathways – A novel approach for tracking hotspots

Effect of Humic acid on the Growth Attributes of Wheat (Triticum aestivum L.) Crop in sandy loam soil of Gwalior region of Madhya Pradesh, India

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