skip to main content
10.1145/3674829.3675085acmconferencesArticle/Chapter ViewAbstractPublication PagescompassConference Proceedingsconference-collections
short-paper
Open access

Assessing the impact of farm ponds on agricultural productivity in Northern India

Published: 28 August 2024 Publication History

Abstract

Government welfare schemes such as the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) in India fund the creation of assets for natural resource management in rural villages to support farmers for their agricultural and livelihoods-based needs. With most agriculture in India being rain-fed, structures such as farm ponds, checkdams, trenches and bunds play a crucial role in supporting groundwater recharge and providing critical lifesaving irrigation in times of dry spells and droughts. In this study, we investigate the impact of farm ponds built under the MGNREGA scheme in Northern India as a source of protective irrigation for cropping areas in their immediate neighbourhood. We assess the impact of farm ponds on the following aspects: (i) we study their impact on agricultural productivity for up to five years since their construction, (ii) we separately study their impact in drought years during this period, (iii) we study the extent to which they are able to to reduce the sensitivity to droughts of sites having farm ponds. A causal analysis framework was designed by identifying control sites that did not have farm ponds, and the treatment effect of having farm ponds was computed using the difference-in-differences approach. Remote sensing data was processed to compute changes in vegetation indices around the treated and control locations before and after the construction of farm ponds. Our results indicate that farm ponds were instrumental in improving the agricultural productivity during the monsoon season in general. The impact during the monsoon season in drought years is also positive and significant. Furthermore, farm ponds also facilitated in reducing drought sensitivity during the monsoon season. The impact during the post-monsoon season was found to be lower, and the impact during the summer agricultural season was found to be the least.

References

[1]
2019. Yield prediction in wheat (Triticum aestivum L.) using spectral reflectance indices. Current Science 116, 2 (2019), pp. 272–278. https://www.jstor.org/stable/27137836
[2]
V. Tare A. K. Prasad, R. P. Singh and M. Kafatos. 2007. Use of vegetation index and meteorological parameters for the prediction of crop yield in India. International Journal of Remote Sensing 28, 23 (2007), 5207–5235. https://doi.org/10.1080/01431160601105843 arXiv:https://doi.org/10.1080/01431160601105843
[3]
Earth Observation Research Center Japan Aerospace Exploration Agency. 2024. Global Precipitation Measurement. https://www.eorc.jaxa.jp/GPM/en/index.html
[4]
K. Ajith, V. Geethalakshmi, K.P. Ragunat, S. Pazhanivelan, and Ga. Dheebakaran1. 2017. Rice Yield Prediction Using MODIS - NDVI (MOD13Q1) and Land Based Observations. International Journal of Current Microbiology and Applied Sciences 6, 12 (2017). https://www.ijcmas.com/6-12-2017/K.%20Ajith, %20et%20al.pdf
[5]
Rodrigo A. Arriagada, Paul J. Ferraro, Erin O. Sills, Subhrendu K. Pattanayak, and Silvia Cordero-Sancho. 2012. Do Payments for Environmental Services Affect Forest Cover? A Farm-Level Evaluation from Costa Rica. Land Economics 88, 2 (2012), 382–399. http://www.jstor.org/stable/23272587
[6]
Ehsan Asmar, Mohammad H. Vahidnia, Mojtaba Rezaei, and Ebrahim Amiri. 2024. Remote sensing-based paddy yield estimation using physical and FCNN deep learning models in Gilan province, Iran. Remote Sensing Applications: Society and Environment 34 (2024), 101199. https://doi.org/10.1016/j.rsase.2024.101199
[7]
Chahat Bansal, Hari Om Ahlawat, Mayank Jain, Om Prakash, Shivani A Mehta, Deepanshu Singh, Harshavardhansushil Baheti, Suyash Singh, and Aaditeshwar Seth. 2021. IndiaSat: A Pixel-Level Dataset for Land-Cover Classification on Three Satellite Systems - Landsat-7, Landsat-8, and Sentinel-2. In Proceedings of the 4th ACM SIGCAS Conference on Computing and Sustainable Societies (Virtual Event, Australia) (COMPASS ’21). Association for Computing Machinery, New York, NY, USA, 147–155. https://doi.org/10.1145/3460112.3471953
[8]
Mohamed Belmahi, Mohamed Hanchane, Nir Y. Krakauer, Ridouane Kessabi, Hind Bouayad, Aziz Mahjoub, and Driss Zouhri. 2023. Analysis of Relationship between Grain Yield and NDVI from MODIS in the Fez-Meknes Region, Morocco. Remote Sensing 15, 11 (2023). https://doi.org/10.3390/rs15112707
[9]
Christopher F. Brown, Steven P. Brumby, Brookie Guzder-Williams, Tanya Birch, Samantha Brooks Hyde, Joseph Mazzariello, Wanda Czerwinski, Valerie J. Pasquarella, Robert Haertel, Simon Ilyushchenko, Kurt Schwehr, Mikaela Weisse, Fred Stolle, Craig Hanson, Oliver Guinan, Rebecca Moore, and Alexander M. Tait. 2022. Dynamic World, Near real-time global 10 m land use land cover mapping. Scientific Data 9, 1 (09 Jun 2022), 251. https://doi.org/10.1038/s41597-022-01307-4
[10]
Jovanović Dušan, Sabo Filip, Govedarica Miro, and Marinković Branko. [n. d.]. Crop yield estimation in 2014 for Vojvodina using methods of remote sensing. Ratarstvo i Povrtarstvo 51, 3 ([n. d.]). https://doi.org/10.5937/ratpov51-6712
[11]
Sarvarbek Eltazarov, Ihtiyor Bobojonov, Lena Kuhn, and Thomas Glauben. 2023. The role of crop classification in detecting wheat yield variation for index-based agricultural insurance in arid and semiarid environments. Environmental and Sustainability Indicators 18 (2023), 100250. https://doi.org/10.1016/j.indic.2023.100250
[12]
FAO. 2024. Harmonized World Soil Database v 1.2. https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v12/en/
[13]
Food and Agriculture Organization of the United Nations. 2024. India at a glance. Retrieved February 22, 2024 from https://www.fao.org/india/fao-in-india/india-at-a-glance/en/
[14]
OSM Foundation. 2024. Geofabrik by OpenStreetMap. http://www.geofabrik.de/
[15]
Berlin Freie Universität. [n. d.]. What is a watershed?Retrieved February 22, 2024 from https://www.geo.fu-berlin.de/en/v/iwm-network/learning_content/introduction_iwm/definition-watershed//index.html
[16]
Google. 2024. Google Earth Engine. https://earthengine.google.com/
[17]
World Resources Institute Google. 2024. Dynamic World: A near realtime land cover dataset for our constantly changing planet.https://dynamicworld.app/
[18]
ISRO. 2024. Indian Geo Platform of ISRO. https://bhuvan.nrsc.gov.in/home/index.php
[19]
Xue Jinru and Baofeng Su. 2017. Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications. Journal of Sensors 2017 (05 2017), 1–17. https://doi.org/10.1155/2017/1353691
[20]
David M. Johnson, Arthur Rosales, Richard Mueller, Curt Reynolds, Ronald Frantz, Assaf Anyamba, Ed Pak, and Compton Tucker. 2021. USA Crop Yield Estimation with MODIS NDVI: Are Remotely Sensed Models Better than Simple Trend Analyses?Remote Sensing 13, 21 (2021). https://doi.org/10.3390/rs13214227
[21]
Murali Krishnan and Rabindra Padaria. 2017. Chemical Science Review and Letters Impact and constraints of farm pond based watershed development in the Hassan district of Karnataka. Chemical Science Review and Letters 6, 21 (01 2017).
[22]
David B. Lobell, David Thau, Christopher Seifert, Eric Engle, and Bertis Little. 2015. A scalable satellite-based crop yield mapper. Remote Sensing of Environment 164 (2015), 324–333. https://doi.org/10.1016/j.rse.2015.04.021
[23]
Department of Agriculture Cooperation & Farmers Welfare Ministry of Agriculture & Farmers Welfare, Government of India. 2016. Manual for Drought Management. Retrieved February 22, 2024 from https://sdma.cg.gov.in/ManualDrought2016.pdf
[24]
S Nalgire and P Chinnasamy. 2022. Index-based impact monitoring of water infrastructures in climate change mitigation projects: A case study of MGNREGA-IWMP projects in Maharashtra. Frontiers in Water 2, 12 (2022).
[25]
NASA. 2024. Oak Ridge National Laboratory Distributed Active Archive Center. https://www.earthdata.nasa.gov/eosdis/daacs/ornl
[26]
NASA. 2024. Shuttle Radar Topography Mission. https://www.earthdata.nasa.gov/sensors/srtm
[27]
USGS (United States Geological Survey NASA. 2024. Landsat Science. https://landsat.gsfc.nasa.gov/
[28]
Government of India. 2024. India Water Resources Information System. https://indiawris.gov.in/wris/#/
[29]
ICAR-National Bureau of Soil Survey and Land Use Planning. 2015. ICAR-NBSS&LUP Serving Science and Society. ICAR-NBSSLUP, 21–22. https://nbsslup.icar.gov.in/wp-content/uploads/2021/E-book/E_BOOK_NBSS&LUP_Part1.pdf
[30]
Manoj Panda, Brajesh Jha, Amit Mandal, Vivek Pal, Aakanksha Sharma, Atrayee Choudhury, and Deepak Kumar. 2018. Rapid assessment of natural resource management component under MGNREGA and its impact on sustainable livelihoods. Technical Report. Ministry of Rural Development, Government of India.
[31]
Ewa Panek and Dariusz Gozdowski. 2020. Analysis of relationship between cereal yield and NDVI for selected regions of Central Europe based on MODIS satellite data. Remote Sensing Applications: Society and Environment (2020), 10028online6. https://doi.org/10.1016/j.rsase.2019.100286
[32]
Pooja Prasad, Om P. Damani, and Milind Sohoni. 2022. How can resource-level thresholds guide sustainable intensification of agriculture at farm level? A system dynamics study of farm-pond based intensification. Agricultural Water Management 264 (2022), 107385. https://doi.org/10.1016/j.agwat.2021.107385
[33]
Carlos Ramirez-Reyes, Katharine R. E. Sims, Peter Potapov, and Volker C. Radeloff. 2018. Payments for ecosystem services in Mexico reduce forest fragmentation. Ecological Applications 28, 8 (2018), 1982–1997. https://doi.org/10.1002/eap.1753 arXiv:https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1002/eap.1753
[34]
P.V.R.M. Reddy, M. Girija Shankar, B. Janardhan Reddy, Y. Shankar Naik, Yerra Eswara Prasad, and D.V.S.R.L. Rekha. 2020. Farm Pond Impact Analysis of PMKSY-Watersheds Project in Srikakulam District of Andhra Pradesh. International Journal of Current Microbiology and Applied Sciences 9, 12 (2020).
[35]
V. K. Sehgal, C. V. S. Sastri, N. Kalra, and V. K. Dadhwal. 2005. Farm-Level Yield mapping for precision crop management by linking remote sensing inputs and a crop simulation model. Journal of the Indian Society of Remote Sensing 33 (2005). https://link.springer.com/article/10.1007/BF02990002
[36]
Bhaskar Sinha, Deep Narayan Singh, Anoma Basu, and Mili Ghosh. 2017. Application of Remote Sensing in Assessing the Impacts of Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA), in Ratlam District, Madhya Pradesh, India. Springer International Publishing, Cham, 87–95. https://doi.org/10.1007/978-3-319-46010-9_6
[37]
Sreedevi TK, Suhas Wani, Raghavendra Sudi, Patel MS, Jayesh T, Singh SN, and Tushar Shah. 2006. On-site and Off-site Impact of Watershed Development: A Case Study of Rajasamadhiyala, Gujarat, India. On-site and Off-site Impact of Watershed Development: A Case Study of Rajasamadhiyala, Gujarat, India. Global Theme on Agroecosystems Report no. 20, Patancheru 502 324, Andhra Pradesh, India: International Crops Research Institute for the Semi-Arid Tropic 2 (08 2006).
[38]
Reinhard Uehleke, Martin Petrick, and Silke Hüttel. 2022. Evaluations of agri-environmental schemes based on observational farm data: The importance of covariate selection. Land Use Policy 114 (2022), 105950. https://doi.org/10.1016/j.landusepol.2021.105950
[39]
Juan Von Thaden, Robert H. Manson, Russell G. Congalton, Fabiola López-Barrera, and Kelly W. Jones. 2021. Evaluating the environmental effectiveness of payments for hydrological services in Veracruz, México: A landscape approach. Land Use Policy 100 (2021), 105055. https://doi.org/10.1016/j.landusepol.2020.105055
[40]
Coady Wing, Kosali Simon, and Ricardo A. Bello-Gomez. 2018. Designing Difference in Difference Studies: Best Practices for Public Health Policy Research. Annual Review of Public Health 39, 1 (2018), 453–469. https://doi.org/10.1146/annurev-publhealth-040617-013507 arXiv:https://doi.org/10.1146/annurev-publhealth-040617-013507PMID: 29328877.

Cited By

View all
  • (2024)Tarımda Sulama Göletlerinin İklim Üzerine Etkilerinin Uydu Görüntüleri ve Meteorolojik Verilerle Karşılaştırmalı Olarak İncelenmesiTurkish Journal of Remote Sensing10.51489/tuzal.1551019Online publication date: 28-Nov-2024

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
COMPASS '24: Proceedings of the 7th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies
July 2024
354 pages
This work is licensed under a Creative Commons Attribution International 4.0 License.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 August 2024

Check for updates

Qualifiers

  • Short-paper
  • Research
  • Refereed limited

Conference

COMPASS '24
Sponsor:

Acceptance Rates

Overall Acceptance Rate 25 of 50 submissions, 50%

Upcoming Conference

COMPASS '25

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)210
  • Downloads (Last 6 weeks)49
Reflects downloads up to 26 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Tarımda Sulama Göletlerinin İklim Üzerine Etkilerinin Uydu Görüntüleri ve Meteorolojik Verilerle Karşılaştırmalı Olarak İncelenmesiTurkish Journal of Remote Sensing10.51489/tuzal.1551019Online publication date: 28-Nov-2024

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Login options

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media