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Climate Smart Agriculture: Climate Change, Responses of Crop Plants and...
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Climate Smart Agriculture: Climate Change, Responses of Crop Plants and Evolving Mitigation and Adaptation Strategies

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Crop Production".

Deadline for manuscript submissions: closed (25 April 2023) | Viewed by 22143

Special Issue Editors

Dr. Raveendran Muthurajan

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Guest Editor
Department of Plant Biotechnology, Tamil Nadu Agricultural University, Coimbatore 641003, Tamil Nadu, India
Interests: plant biotechnology; molecular breeding; functional genomics
Dr. Vellingiri Geethalakshmi

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Guest Editor
Water Technology Centre, Tamil Nadu Agricultural University, Coimbatore 641003, India
Interests: climate change; environment impact assessment; crop modeling; water resource management; weather advisory
Dr. Viswanathan Chinnusamy

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Guest Editor
Indian Council of Agricultural Research—Indian Agricultural Research Institute, Pusa, New Delhi 110012, India
Interests: crop physiology; hormone signaling; water use efficiency in plants; crop phenomics; genomics
Dr. Madavalam S. Sheshshayee

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Guest Editor
Department of Crop Physiology, University of Agricultural Sciences, Bangalore 560065, India
Interests: molecular physiology; water use efficiency; temperature stresses

Special Issue Information

Dear Colleagues,

Global predictions suggest that an expanding population requires a 60–70% increase in agricultural production by 2050, a target which is threatened by shrinking land, labor, and water resources and a changing climate. Increased frequency in the occurrence of drought, salinity, flood, heat/cold stresses, and altered pattern of pest/disease occurrence are expected under a changing climate. Limited knowledge regarding the impacts of climate change on growth and the development of crops and changing frequency of pest/disease occurrence raise serious concerns. The generation of improved knowledge on the above areas will empower scientists in formulating effecting mitigation/adaptation strategies so as to pave the way for the sustained growth of agriculture under changing climatic conditions. Advances in sensor technologies for precise recording of weather events, availability of big data analysis and weather prediction tools, availability of phenomics and genomics platforms for recording crop responses against changing climate, and innovative breeding/genomic platforms for accelerated crop improvement have empowered scientists to fight against climate change.

In this Special Issue, original research papers and reviews from global experts will be published after peer review. The readers will benefit from a wide array of information on the effect of a changing climate on agriculture and status of mitigation/adaptation strategies developed to sustain agriculture under a changing climate.

Dr. Raveendran Muthurajan
Dr. Vellingiri Geethalakshmi
Dr. Viswanathan Chinnusamy
Dr. Madavalam S. Sheshshayee
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agriculture is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • agriculture
  • climate change
  • extreme weather events
  • biotic/abiotic stresses
  • crop responses
  • mitigation strategies
  • crop improvement

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Published Papers (4 papers)

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Research

17 pages, 2903 KiB  
Article
Mitigating Methane Emission from the Rice Ecosystem through Organic Amendments
by Kandasamy Senthilraja, Subramanian Venkatesan, Dhandayuthapani Udhaya Nandhini, Manickam Dhasarathan, Balasubramaniam Prabha, Kovilpillai Boomiraj, Shanmugam Mohan Kumar, Kulanthaivel Bhuvaneswari, Muthurajan Raveendran and Vellingiri Geethalakshmi
Agriculture 2023, 13(5), 1037; https://doi.org/10.3390/agriculture13051037 - 10 May 2023
Cited by 4 | Viewed by 5272
Abstract
Tamil Nadu in particular is a key rice-producing region in peninsular India. Hydrochemistry, viz., redox potential (Rh), soil temperature and dissolved oxygen (DO), of rice soils can determine the production of greenhouse gas methane (CH4). In recent decades, the cultivation of [...] Read more.
Tamil Nadu in particular is a key rice-producing region in peninsular India. Hydrochemistry, viz., redox potential (Rh), soil temperature and dissolved oxygen (DO), of rice soils can determine the production of greenhouse gas methane (CH4). In recent decades, the cultivation of crops organically became a viable option for mitigating climate change. Hence, this study aimed to investigate the effects of different organic amendments on CH4 emission, Rh, DO, and soil and water temperature (T) in relation to the yield of paddy. The treatments composed of viz., control, blue-green algae (BGA), Azolla, farm yard manure (FYM), green leaf manure (GLM), blue-green algae + Azolla, FYM + GLM, BGA + Azolla + FYM + GLM, vermicompost and decomposed livestock manure. With the addition of BGA + Azolla, the highest reduction in CH4 emission was 37.9% over the control followed by BGA. However, the same treatment had a 50% and 43% increase in Rh and DO, respectively, over the control. Established Pearson correlation analyses showed that the CH4 emission had a positive correlation with soil (r = 0.880 **) and water T (r = 0.888 **) and negative correlations with Rh (r = −0.987 **) and DO (r = −0.963 **). The higher grain yield of 26.5% was associated with BGA + Azolla + FYM + GLM application. Our findings showed that there are significant differences in CH4 emissions between different organic amendments and that hydro-parameters may be a more important controlling factor for methane emissions than temperature. The conclusion has been drawn based on valid research findings that bio-fertilization using BGA and Azolla is an efficient and feasible approach to combat climate change, as it assists in reducing methane emissions while simultaneously boosting crop yield by fixing nitrogen into the soil in the studied agro-climatic zone. Full article
(This article belongs to the Special Issue Climate Smart Agriculture: Climate Change, Responses of Crop Plants and Evolving Mitigation and Adaptation Strategies)
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22 pages, 3860 KiB  
Article
Imaging Sensor-Based High-Throughput Measurement of Biomass Using Machine Learning Models in Rice
by Allimuthu Elangovan, Nguyen Trung Duc, Dhandapani Raju, Sudhir Kumar, Biswabiplab Singh, Chandrapal Vishwakarma, Subbaiyan Gopala Krishnan, Ranjith Kumar Ellur, Monika Dalal, Padmini Swain, Sushanta Kumar Dash, Madan Pal Singh, Rabi Narayan Sahoo, Govindaraj Kamalam Dinesh, Poonam Gupta and Viswanathan Chinnusamy
Agriculture 2023, 13(4), 852; https://doi.org/10.3390/agriculture13040852 - 12 Apr 2023
Cited by 10 | Viewed by 3889
Abstract
Phenomics technologies have advanced rapidly in the recent past for precision phenotyping of diverse crop plants. High-throughput phenotyping using imaging sensors has been proven to fetch more informative data from a large population of genotypes than the traditional destructive phenotyping methodologies. It provides [...] Read more.
Phenomics technologies have advanced rapidly in the recent past for precision phenotyping of diverse crop plants. High-throughput phenotyping using imaging sensors has been proven to fetch more informative data from a large population of genotypes than the traditional destructive phenotyping methodologies. It provides accurate, high-dimensional phenome-wide big data at an ultra-super spatial and temporal resolution. Biomass is an important plant phenotypic trait that can reflect the agronomic performance of crop plants in terms of growth and yield. Several image-derived features such as area, projected shoot area, projected shoot area with height constant, estimated bio-volume, etc., and machine learning models (single or multivariate analysis) are reported in the literature for use in the non-invasive prediction of biomass in diverse crop plants. However, no studies have reported the best suitable image-derived features for accurate biomass prediction, particularly for fully grown rice plants (70DAS). In this present study, we analyzed a subset of rice recombinant inbred lines (RILs) which were developed from a cross between rice varieties BVD109 × IR20 and grown in sufficient (control) and deficient soil nitrogen (N stress) conditions. Images of plants were acquired using three different sensors (RGB, IR, and NIR) just before destructive plant sampling for the quantitative estimation of fresh (FW) and dry weight (DW). A total of 67 image-derived traits were extracted and classified into four groups, viz., geometric-, color-, IR- and NIR-related traits. We identified a multimodal trait feature, the ratio of PSA and NIR grey intensity as estimated from RGB and NIR sensors, as a novel trait for predicting biomass in rice. Among the 16 machine learning models tested for predicting biomass, the Bayesian regularized neural network (BRNN) model showed the maximum predictive power (R2 = 0.96 and 0.95 for FW and DW of biomass, respectively) with the lowest prediction error (RMSE and bias value) in both control and N stress environments. Thus, biomass can be accurately predicted by measuring novel image-based parameters and neural network-based machine learning models in rice. Full article
(This article belongs to the Special Issue Climate Smart Agriculture: Climate Change, Responses of Crop Plants and Evolving Mitigation and Adaptation Strategies)
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13 pages, 5973 KiB  
Article
Developing Early Morning Flowering Version of Rice Variety CO 51 to Mitigate the Heat-Induced Yield Loss
by Bharathi Ayyenar, Rohit Kambale, Sudhakar Duraialagaraja, Sudha Manickam, Vignesh Mohanavel, Priyanka Shanmugavel, Senthil Alagarsamy, Tsutomu Ishimaru, S.V. Krishna Jagadish, Geethalakshmi Vellingiri and Raveendran Muthurajan
Agriculture 2023, 13(3), 553; https://doi.org/10.3390/agriculture13030553 - 24 Feb 2023
Cited by 5 | Viewed by 7970
Abstract
By 2050, the rice production needs to be increased by at least 50% in order to meet the growing food demands of the global population. Among various yield limiting factors, high temperature is fast becoming a major threat to sustain rice yields due [...] Read more.
By 2050, the rice production needs to be increased by at least 50% in order to meet the growing food demands of the global population. Among various yield limiting factors, high temperature is fast becoming a major threat to sustain rice yields due to its increased frequency of occurrence and severity of stress events. The development of heat-resilient rice cultivars has been slow due to the lack of relevant donors for heat tolerance traits and limited information regarding the genetic basis of these component traits. The early morning flowering (EMF) trait, contributing to heat escape by promoting flowering/anthesis during cooler hours in the morning is demonstrated to offer protection against high-temperature-induced failure of pollination and fertilization. In this study, evaluation of CO 51, IR64 and IR64-qEMF3 (NIL of IR64 harboring QTL promoting EMF revealed that qEMF3 promoted early morning flowering in IR64-qEMF3 (1½ to 2 h earlier than IR64) and thereby reduced the sterility by about 8.15%. Attempts through marker-assisted backcross breeding led to development of advanced backcross progenies (NILs) of CO 51, harboring qEMF3. Evaluation of 88 BC3F2 progenies identified 19 progenies harboring qEMF3 under homozygous conditions. Evaluation of NILs of CO 51 harboring qEMF3 during summer 2019 revealed that the NILs exhibited early (7.30 a.m.) onset of anthesis by 1½ h and completed its peak anthesis well around cooler hours (9.30 a.m.) of the day and thereby recorded reduced spikelet sterility (7.8–9.0%) than their recurrent parent CO 51 (19.2%). The current study clearly demonstrated the efficacy of early morning flowering in the mitigation of yield losses under high-temperature conditions in a farmer preferred rice variety. Full article
(This article belongs to the Special Issue Climate Smart Agriculture: Climate Change, Responses of Crop Plants and Evolving Mitigation and Adaptation Strategies)
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20 pages, 5694 KiB  
Article
How Far Will Climate Change Affect Future Food Security? An Inquiry into the Irrigated Rice System of Peninsular India
by Tamilarasu Arivelarasan, V. S. Manivasagam, Vellingiri Geethalakshmi, Kulanthaivel Bhuvaneswari, Kiruthika Natarajan, Mohan Balasubramanian, Ramasamy Gowtham and Raveendran Muthurajan
Agriculture 2023, 13(3), 551; https://doi.org/10.3390/agriculture13030551 - 24 Feb 2023
Cited by 7 | Viewed by 4034
Abstract
Climate change poses a great challenge to food security, particularly in developing nations where important food crops such as rice and wheat have been grown in large quantities. The study investigates food security using an integrated approach, which comprises forecasting future rice production [...] Read more.
Climate change poses a great challenge to food security, particularly in developing nations where important food crops such as rice and wheat have been grown in large quantities. The study investigates food security using an integrated approach, which comprises forecasting future rice production using the AquaCrop model and demand for rice using an economic model. The proposed approach was evaluated in the Cauvery delta zone in the eastern part of Tamil Nadu, which is a major rice-growing hotspot in peninsular India. Our results showed that the future rice productivity of the Cauvery delta region would be reduced by 35% between 2021 and 2040 and by 16% between 2041 and 2050. However, the supply–demand gap addressing food security in the Cauvery delta zone is positive for the future, as evidenced by the availability of surplus rice of 0.39 million tonnes for the period 2021–2030 and 0.23 million tonnes and 0.35 million tonnes for the periods 2031–2040 and 2041–2050, respectively. Nevertheless, as the neighboring regions are relying on rice production from the Cauvery delta, this surplus rice production is potentially not sufficient to meet the demand of the state as a whole, which suggests climate change may pose a severe threat to the food security of the Tamil Nadu State. These findings emphasize the necessity of performing regional-level food security assessments with a focus on developing location-specific policy options to mitigate the adverse effects of climate-induced anomalies on food security. Full article
(This article belongs to the Special Issue Climate Smart Agriculture: Climate Change, Responses of Crop Plants and Evolving Mitigation and Adaptation Strategies)
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