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Remote Sensing
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Impacts of Climate Change on Agriculture
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Impacts of Climate Change on Agriculture

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (15 November 2022) | Viewed by 10546

Special Issue Editors

Prof. Dr. Conghe Song

E-Mail Website
Guest Editor
Department of Geography, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3220, USA
Interests: remote sensing of environment; land-cover/land-use change; ecosystem carbon and water exchange with atmosphere; human–environment interactions
Special Issues, Collections and Topics in MDPI journals
Dr. Josh Gray

E-Mail Website
Guest Editor
Department of Forestry and Environmental Resources, North Carolina State Univerisity, Raleigh, NC 27695, USA
Interests: remote sensing of vegetation dynamics; phenology; agriculture; land-cover/land-use change; climate change

Special Issue Information

Dear Colleagues,

Agriculture is the foundation for the stability of human society and economic development. Given the world population has passed 7 billion and is expected to continue to grow in the next a few decades, the world will need 70–100% more food to feed the population by 2050. Thus, food security is a major concern around the world. Climate change has added to the uncertainty of food supply. Creative agricultural management practices and associated new agricultural policies are urgently needed to mitigate the impacts of climate change on agriculture and adapt to the new normal of a warmer climate with more frequent and intense extreme weather events, such as droughts and floods. Remote sensing scientists are in a unique position to inform such policy making and development of new agricultural practices by contributing key scientific knowledge toward understanding the impacts of climate change on agriculture in the past and their projection into the future. This Special Issue calls for papers on understanding the impacts of climate change on agriculture using remote sensing and associated climate variables as well as other environmental factors with empirical or process-based models. Specifically, we invite contributions concerning the following topics:

  1. Estimation of crop yield, including both cereal crops and crops in orchards and specific extreme weather events, such as hurricanes/typhoons/cyclones, droughts, etc.
  2. Quantification of how extreme weather conditions affect crop growth and yield.
  3. Mapping of crop phenology change under a changing climate and its impacts on crop yield.
  4. Projection of crop yield into the future with models informed by remotely sensed data.
  5. Estimation to agricultural water usage under a warmer climate and its implications for cereal and perennial crop yields.
  6. Mapping of agriculture degradation as a result of climate change.
  7. Quantification of crop yield change due to changes in disease, insects, and pollinators driven by climate change.
  8. Estimation of climate impacts on smallholder agriculture, which is extremely vulnerable to climate change.

Prof. Dr. Conghe Song
Dr. Josh Gray
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. Remote Sensing 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 2700 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

  • crop yields
  • agriculture degradation
  • cropland phenology
  • climate change
  • extreme weather events

Published Papers (4 papers)

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Research

20 pages, 3718 KiB  
Article
Impacts of Extreme Temperature and Precipitation on Crops during the Growing Season in South Asia
by Xinyi Fan, Duoping Zhu, Xiaofang Sun, Junbang Wang, Meng Wang, Shaoqiang Wang and Alan E. Watson
Remote Sens. 2022, 14(23), 6093; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14236093 - 01 Dec 2022
Cited by 4 | Viewed by 1990
Abstract
South Asia, one of the most important food producing regions in the world, is facing a significant threat to food grain production under the influence of extreme high temperatures. Furthermore, the probability of simultaneous trends in extreme precipitation patterns and extreme heat conditions, [...] Read more.
South Asia, one of the most important food producing regions in the world, is facing a significant threat to food grain production under the influence of extreme high temperatures. Furthermore, the probability of simultaneous trends in extreme precipitation patterns and extreme heat conditions, which can have compounding effects on crops, is a likelihood in South Asia. In this study, we found complex relationships between extreme heat and precipitation patterns, as well as compound effects on major crops (rice and wheat) in South Asia. We also employed event coincidence analysis (ECA) to quantify the likelihood of simultaneous temperature and crop extremes. We used the Enhanced Vegetation Index (EVI) as the primary data to evaluate the distinct responses of major crops to weather extremes. Our results suggest that while the probability of simultaneous extreme events is small, most regions of South Asia (more than half) have experienced extreme events. The regulatory effect of precipitation on heat stress is very unevenly distributed in South Asia. The harm caused by a wet year at high temperature is far greater than that during a dry year, although the probability of a dry year is greater than that of a wet year. For the growing seasons, the highest significant event coincidence rates at a low EVI were found for both high- and low-temperature extremes. The regions that responded positively to EVI at extreme temperatures were mainly concentrated in irrigated farmland, and the regions that responded negatively to EVI at extreme temperatures were mostly in the mountains and other high-altitude regions. Implications can guide crop adaptation interventions in response to these climate influences. Full article
(This article belongs to the Special Issue Impacts of Climate Change on Agriculture)
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12 pages, 5643 KiB  
Article
Identifying the Impact of Regional Meteorological Parameters on US Crop Yield at Various Spatial Scales Using Remote Sensing Data
by Cheolhee Yoo, Daehyun Kang and Seonyoung Park
Remote Sens. 2022, 14(15), 3508; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153508 - 22 Jul 2022
Cited by 2 | Viewed by 1731
Abstract
This study investigates the influence of meteorological parameters such as temperature and precipitation on gross primary production (GPP) in the continental United States (CONUS) during boreal summer using satellite-based temperature and precipitation indices and GPP data at various scales (i.e., pixel, county, and [...] Read more.
This study investigates the influence of meteorological parameters such as temperature and precipitation on gross primary production (GPP) in the continental United States (CONUS) during boreal summer using satellite-based temperature and precipitation indices and GPP data at various scales (i.e., pixel, county, and state levels). The strong linear relationship between temperature and precipitation indices is presented around the central United States, particularly in the Great Plains, where the year-to-year variation of GPP is very sensitive to meteorological conditions. This sensitive GPP variation is mostly attributable to the semi-arid climate in the Great Plains, where crop productivity and temperature are closely related. The more specific information for the regionality of the relationships across the variables manifests itself at higher resolutions. The impact of the summer meteorological condition on the annual crop yield is particularly significant. Maize and soybean yields show a strong correlation with both Temperature Condition Index (TCI) and Precipitation Condition Index (PCI) in the Great Plains, with a relatively higher relationship with TCI than PCI, which is consistent with the relationship compared with GPP. This study suggests that in-depth investigations into the relationship between maize and soybean yields and the climate are required. The region-dependent relationship between GPP and meteorological conditions in our study would guide agricultural decision making in the future climate. Full article
(This article belongs to the Special Issue Impacts of Climate Change on Agriculture)
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20 pages, 6675 KiB  
Article
Recent Changes in Drought Events over South Asia and Their Possible Linkages with Climatic and Dynamic Factors
by Irfan Ullah, Xieyao Ma, Guoyu Ren, Jun Yin, Vedaste Iyakaremye, Sidra Syed, Kaidong Lu, Yun Xing and Vijay P. Singh
Remote Sens. 2022, 14(13), 3219; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14133219 - 04 Jul 2022
Cited by 12 | Viewed by 2487
Abstract
South Asia is home to one of the fastest-growing populations in Asia, and human activities are leaving indelible marks on the land surface. Yet the likelihood of successive observed droughts in South Asia (SA) and its four subregions (R-1: semi-arid, R-2: arid, R-3: [...] Read more.
South Asia is home to one of the fastest-growing populations in Asia, and human activities are leaving indelible marks on the land surface. Yet the likelihood of successive observed droughts in South Asia (SA) and its four subregions (R-1: semi-arid, R-2: arid, R-3: subtropical wet, and R-4: tropical wet and dry) remains poorly understood. Using the state-of-the-art self-calibrated Palmer Drought Severity Index (scPDSI), we examined the impact of different natural ocean variability modes on the evolution, severity, and magnitude of observed droughts across the four subregions that have distinct precipitation seasonality and cover key breadbaskets and highly vulnerable populations. The study revealed that dryness had significantly increased in R-1, R-2, and R-4 during 1981–2020. Temporal analysis revealed an increase in drought intensity for R-1 and R-4 since the 2000s, while a mixed behavior was observed in R-2 and R-3. Moreover, most of the sub-regions witnessed a substantial upsurge in annual precipitation, but a significant decrease in vapor pressure deficit (VPD) during 1981–2020. The increase in precipitation and the decline in VPD partially contributed to a significant rise in Normalized Difference Vegetation Index (NDVI) and a decrease in dryness. In contrast, a strong positive correlation was found between drought index and precipitation, and NDVI across R-1, R-2, and R-4, whereas temperature and VPD exhibited a negative correlation over these regions. No obvious link was detected with El-Niño Southern Oscillation (ENSO) events, or Indian Ocean Dipole (IOD) and drought evolution, as explored for certain regions of SA. The findings showed the possibility that the precipitation changes over these regions had an insignificant relationship with ENSO, IOD, and drought onset. Thus, the study results highlight the need for considering interactions within the longer climate system in describing observed drought risks rather than aiming at drivers from an individual perspective. Full article
(This article belongs to the Special Issue Impacts of Climate Change on Agriculture)
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14 pages, 26566 KiB  
Article
Modeling the Impact of Climate Changes on Crop Yield: Irrigated vs. Non-Irrigated Zones in Mississippi
by Sadia Alam Shammi and Qingmin Meng
Remote Sens. 2021, 13(12), 2249; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122249 - 09 Jun 2021
Cited by 3 | Viewed by 2521
Abstract
Climate change and its impact on agriculture are challenging issues regarding food production and food security. Many researchers have been trying to show the direct and indirect impacts of climate change on agriculture using different methods. In this study, we used linear regression [...] Read more.
Climate change and its impact on agriculture are challenging issues regarding food production and food security. Many researchers have been trying to show the direct and indirect impacts of climate change on agriculture using different methods. In this study, we used linear regression models to assess the impact of climate on crop yield spatially and temporally by managing irrigated and non-irrigated crop fields. The climate data used in this study are Tmax (maximum temperature), Tmean (mean temperature), Tmin (minimum temperature), precipitation, and soybean annual yields, at county scale for Mississippi, USA, from 1980 to 2019. We fit a series of linear models that were evaluated based on statistical measurements of adjusted R-square, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). According to the statistical model evaluation, the 1980–1992 model Y[Tmax,Tmin,Precipitation]92i (BIC = 120.2) for irrigated zones and the 1993–2002 model Y[Tmax,Tmean,Precipitation]02ni (BIC = 1128.9) for non-irrigated zones showed the best fit for the 10-year period of climatic impacts on crop yields. These models showed about 2 to 7% significant negative impact of Tmax increase on the crop yield for irrigated and non-irrigated regions. Besides, the models for different agricultural districts also explained the changes of Tmax, Tmean, Tmin, and precipitation in the irrigated (adjusted R-square: 13–28%) and non-irrigated zones (adjusted R-square: 8–73%). About 2–10% negative impact of Tmax was estimated across different agricultural districts, whereas about −2 to +17% impacts of precipitation were observed for different districts. The modeling of 40-year periods of the whole state of Mississippi estimated a negative impact of Tmax (about 2.7 to 8.34%) but a positive impact of Tmean (+8.9%) on crop yield during the crop growing season, for both irrigated and non-irrigated regions. Overall, we assessed that crop yields were negatively affected (about 2–8%) by the increase of Tmax during the growing season, for both irrigated and non-irrigated zones. Both positive and negative impacts on crop yields were observed for the increases of Tmean, Tmin, and precipitation, respectively, for irrigated and non-irrigated zones. This study showed the pattern and extent of Tmax, Tmean, Tmin, and precipitation and their impacts on soybean yield at local and regional scales. The methods and the models proposed in this study could be helpful to quantify the climate change impacts on crop yields by considering irrigation conditions for different regions and periods. Full article
(This article belongs to the Special Issue Impacts of Climate Change on Agriculture)
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