Machine learning-based potential loss assessment of maize and rice production due to flash flood in Himachal Pradesh, India.

Flash floods in mountainous regions like the Himalayas are considered to be common natural calamities. Their consequences often are more dangerous than any flood event in the plains. These hazards not only put human lives at threat but also cause economic deflation due to the loss of lands, properties, and agricultural production. Hence, assessing the impact of such hazards in the existing agricultural system is of utmost importance to understand the probable crop loss. In this paper, we studied the efficiency of the remotely sensed microwave data to map the croplands affected by the flash flood that occurred in July 2023 in Himachal Pradesh, a mountainous state in the Indian Himalayan Region. The Una, Hamirpur, Kangra, and Sirmaur districts were identified as the most affected areas, with about 9%, 6%, 5.74%, and 3.61% of the respective districts' total geographical area under flood. Further, four machine learning algorithms (random forest, support vector regressor, k-nearest neighbor, and extreme gradient boosting) were evaluated to forecast maize and rice crop production and potential loss during the Kharif season in 2023. A regression algorithm with ten predictor variables consisting of the cropland area, two vegetation indices, and seven climatic parameters was applied to forecast the maize and rice production in the state. Amongst the four algorithms, random forest showed outstanding performance compared to others. The random forest regressor estimated the production of maize and rice with R2 more than 0.8 in most districts. The mean absolute error and the root mean squared error obtained from the random forest regressor were also minimal compared to the others. The maximum production loss of maize is estimated for Solan (54.13%), followed by Una (11.06%), and of rice in Kangra (19.1%), Una (18.8%) and Kinnaur (18.5%) districts. This indicated the utility of the proposed approach for a quick in-season forecast on crop production loss due to climatic hazards.

Characterization of putative calcium-dependent protein kinase-1 (TaCPK-1) gene: hubs in signalling and tolerance network of wheat under terminal heat.

Calcium-dependent protein kinase (CDPK) is member of one of the most important signalling cascades operating inside the plant system due to its peculiar role as thermo-sensor. Here, we identified 28 full length putative CDPKs from wheat designated as TaCDPK (1-28). Based on digital gene expression, we cloned full length TaCPK-1 gene of 1691 nucleotides with open reading frame (ORF) of 548 amino acids (accession number OP125853). The expression of TaCPK-1 was observed maximum (3.1-fold) in leaf of wheat cv. HD2985 (thermotolerant) under T2 (38 ± 3 °C, 2 h), as compared to control. A positive correlation was observed between the expression of TaCPK-1 and other stress-associated genes (MAPK6, CDPK4, HSFA6e, HSF3, HSP17, HSP70, SOD and CAT) involved in thermotolerance. Global protein kinase assay showed maximum activity in leaves, as compared to root, stem and spike under heat stress. Immunoblot analysis showed abundance of CDPK protein in wheat cv. HD2985 (thermotolerant) in response to T2 (38 ± 3 °C, 2 h), as compared to HD2329 (thermosusceptible). Calcium ion (Ca2+), being inducer of CDPK, showed strong Ca-signature in the leaf tissue (Ca-622 ppm) of thermotolerant wheat cv. under heat stress, whereas it was minimum (Ca-201 ppm) in spike tissue. We observed significant variations in the ionome of wheat under HS. To conclude, TaCPK-1 plays important role in triggering signaling network and in modulation of HS-tolerance in wheat. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-024-03989-6.

Determination of optimal daily light integral (DLI) for indoor cultivation of iceberg lettuce in an indigenous vertical hydroponic system.

The indoor cultivation of lettuce in a vertical hydroponic system (VHS) under artificial lighting is an energy-intensive process incurring a high energy cost. This study determines the optimal daily light integral (DLI) as a function of photoperiod on the physiological, morphological, and nutritional parameters, as well as the resource use efficiency of iceberg lettuce (cv. Glendana) grown in an indoor VHS. Seedlings were grown in a photoperiod of 12 h, 16 h, and 20 h with a photosynthetic photon flux density (PPFD) of 200 µmol m-2 s-1 using white LED lights. The results obtained were compared with VHS without artificial lights inside the greenhouse. The DLI values for 12 h, 16 h, and 20 h were 8.64, 11.5, and 14.4 mol m-2 day-1, respectively. The shoot fresh weight at harvest increased from 275.5 to 393 g as the DLI increased from 8.64 to 11.5 mol m-2 day-1. DLI of 14.4 mol m-2 day-1 had a negative impact on fresh weight, dry weight, and leaf area. The transition from VHS without artificial lights to VHS with artificial lights resulted in a 60% increase in fresh weight. Significantly higher water use efficiency of 71 g FW/L and energy use efficiency of 206.31 g FW/kWh were observed under a DLI of 11.5 mol m-2 day-1. The study recommends an optimal DLI of 11.5 mol m-2 day-1 for iceberg lettuce grown in an indoor vertical hydroponic system.

Investigating the Biochemical Responses in Wheat Cultivars Exposed to Thermal Power Plant Emission.

The current study aimed to assess how high concentrations of ozone (O3) and suspended particulate matter (SPM) alter biochemical properties of high yielding wheat cultivars (i.e., HD3086 and HD2967) grown under 10 km radius in 8 villages, located around Thermal Power Plant (TPP), Auraiya, Uttar Pradesh, India. Significant foliar damage was brought on by O3 and SPM exposure in both wheat cultivars and noted for consecutive 2 years as per emission patterns, air movement and biochemical defense capabilities. The detected air pollutants at the chosen experimental site ranged from 34 to 46 ppb O3 and 139-189 µg/m3 SPM. Range of biochemical parameter for both cultivars are as pH 6.6-7.1, relative water content (RWC) 44-62%, chlorophyll 0.23-0.35 mg/g, ascorbic acid (AA) 54-68 mg/g and air pollution tolerance index (APTI) 47-72. It has been observed that SPM deposition had a meaningful impact (P-value = 0.05) on the chlorophyll, pH, RWC and APTI.

Characterizing the putative mitogen-activated protein kinase (MAPK) and their protective role in oxidative stress tolerance and carbon assimilation in wheat under terminal heat stress.

Wheat, being sensitive to terminal heat, causes drastic reduction in grain quality and yield. MAPK cascade regulates the network of defense mechanism operated inside plant system. Here, we have identified 21 novel MAPKs through gel-based proteomics and RNA-seq data analysis. Based on digital gene expression, two transcripts (transcript_2834 and transcript_8242) showing homology with MAPK were cloned and characterized from wheat (acc. nos. MK854806 and KT835664). Transcript_2834 was cloned in pET28a vector and recombinant MAPK protein of ∼40.3 kDa was isolated and characterized to have very high in-vitro kinase activity under HS. Native MAPK showed positive correlation with the expression of TFs, HSPs, genes linked with antioxidant enzyme (SOD, CAT, GPX), photosynthesis and starch biosynthesis pathways in wheat under HS. Wheat cv. HD3086 (thermotolerant) having higher expression and activity of MAPK under HS showed significant increase in accumulation of proline, H2O2, starch, and granule integrity, compared with BT-Schomburgk (thermosusceptible).

Causes of variation among rice models in yield response to CO2 examined with Free-Air CO2 Enrichment and growth chamber experiments.

The CO2 fertilization effect is a major source of uncertainty in crop models for future yield forecasts, but coordinated efforts to determine the mechanisms of this uncertainty have been lacking. Here, we studied causes of uncertainty among 16 crop models in predicting rice yield in response to elevated [CO2] (E-[CO2]) by comparison to free-air CO2 enrichment (FACE) and chamber experiments. The model ensemble reproduced the experimental results well. However, yield prediction in response to E-[CO2] varied significantly among the rice models. The variation was not random: models that overestimated at one experiment simulated greater yield enhancements at the others. The variation was not associated with model structure or magnitude of photosynthetic response to E-[CO2] but was significantly associated with the predictions of leaf area. This suggests that modelled secondary effects of E-[CO2] on morphological development, primarily leaf area, are the sources of model uncertainty. Rice morphological development is conservative to carbon acquisition. Uncertainty will be reduced by incorporating this conservative nature of the morphological response to E-[CO2] into the models. Nitrogen levels, particularly under limited situations, make the prediction more uncertain. Improving models to account for [CO2] × N interactions is necessary to better evaluate management practices under climate change.

Author Correction: The uncertainty of crop yield projections is reduced by improved temperature response functions.

Nature Plants 3, 17102 (2017); published online 17 July 2017; corrected online 27 September 2017.

Erratum: The uncertainty of crop yield projections is reduced by improved temperature response functions.

This corrects the article DOI: 10.1038/nplants.2017.102.

Erratum: The uncertainty of crop yield projections is reduced by improved temperature response functions.

This corrects the article DOI: 10.1038/nplants.2017.102.

Multimodel ensembles of wheat growth: many models are better than one.

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.