IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i17p6822-d402661.html
   My bibliography  Save this article

Simulating Soybean–Rice Rotation and Irrigation Strategies in Arkansas, USA Using APEX

Author

Listed:
  • Sam R. Carroll

    (Department of Biological and Agricultural Engineering, College of Engineering, University of Arkansas, Fayetteville, AR 72701, USA)

  • Kieu Ngoc Le

    (Department of Biological and Agricultural Engineering, College of Engineering, University of Arkansas, Fayetteville, AR 72701, USA
    Department of Water Resources, College of Environment and Natural Resources, Can Tho University, Can Tho 900100, Vietnam)

  • Beatriz Moreno-García

    (Department of Biological and Agricultural Engineering, College of Engineering, University of Arkansas, Fayetteville, AR 72701, USA)

  • Benjamin R. K. Runkle

    (Department of Biological and Agricultural Engineering, College of Engineering, University of Arkansas, Fayetteville, AR 72701, USA)

Abstract

With population growth and resource depletion, maximizing the efficiency of soybean ( Glycine max [L.] Merr.) and rice ( Oryza sativa L.) cropping systems is urgently needed. The goal of this study was to shed light on precise irrigation amounts and optimal agronomic practices via simulating rice–rice and soybean–rice crop rotations in the Agricultural Policy/Environmental eXtender (APEX) model. The APEX model was calibrated using observations from five fields under soybean–rice rotation in Arkansas from 2017 to 2019 and remote sensing leaf area index (LAI) values to assess modeled vegetation growth. Different irrigation practices were assessed, including conventional flooding (CVF), known as cascade, multiple inlet rice irrigation with polypipe (MIRI), and furrow irrigation (FIR). The amount of water used differed between fields, following each field’s measured or estimated input. Moreover, fields were managed with either continuous flooding (CF) or alternate wetting and drying (AWD) irrigation. Two 20-year scenarios were simulated to test yield changes: (1) between rice–rice and soybean–rice rotation and (2) under reduced irrigation amounts. After calibration with crop yield and LAI, the modeled LAI correlated to the observations with R 2 values greater than 0.66, and the percent bias (PBIAS) values were within 32%. The PBIAS and percent difference for modeled versus observed yield were within 2.5% for rice and 15% for soybean. Contrary to expectation, the rice–rice and soybean–rice rotation yields were not statistically significant. The results of the reduced irrigation scenario differed by field, but reducing irrigation beyond 20% from the original amount input by the farmers significantly reduced yields in all fields, except for one field that was over-irrigated.

Suggested Citation

  • Sam R. Carroll & Kieu Ngoc Le & Beatriz Moreno-García & Benjamin R. K. Runkle, 2020. "Simulating Soybean–Rice Rotation and Irrigation Strategies in Arkansas, USA Using APEX," Sustainability, MDPI, vol. 12(17), pages 1-17, August.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:17:p:6822-:d:402661
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/17/6822/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/12/17/6822/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yang, J.M. & Yang, J.Y. & Liu, S. & Hoogenboom, G., 2014. "An evaluation of the statistical methods for testing the performance of crop models with observed data," Agricultural Systems, Elsevier, vol. 127(C), pages 81-89.
    2. Pandey, Sushil & Byerlee, Derek R. & Dawe, David & Dobermann, Achim & Mohanty, Samarendu & Rozelle, (ed.), 2010. "Rice in the Global Economy: Strategic Research and Policy Issues for Food Security," IRRI Books, International Rice Research Institute (IRRI), number 164488.
    3. Trombetta, Andrea & Iacobellis, Vito & Tarantino, Eufemia & Gentile, Francesco, 2016. "Calibration of the AquaCrop model for winter wheat using MODIS LAI images," Agricultural Water Management, Elsevier, vol. 164(P2), pages 304-316.
    4. Le, Kieu N. & Jeong, Jaehak & Reyes, Manuel R. & Jha, Manoj K. & Gassman, Philip W. & Doro, Luca & Hok, Lyda & Boulakia, Stéphane, 2018. "Evaluation of the performance of the EPIC model for yield and biomass simulation under conservation systems in Cambodia," Agricultural Systems, Elsevier, vol. 166(C), pages 90-100.
    5. Zhang, Bangbang & Feng, Gary & Read, John J. & Kong, Xiangbin & Ouyang, Ying & Adeli, Ardeshir & Jenkins, Johnie N., 2016. "Simulating soybean productivity under rainfed conditions for major soil types using APEX model in East Central Mississippi," Agricultural Water Management, Elsevier, vol. 177(C), pages 379-391.
    6. Rejesus, Roderick M. & Palis, Florencia G. & Rodriguez, Divina Gracia P. & Lampayan, Ruben M. & Bouman, Bas A.M., 2011. "Impact of the alternate wetting and drying (AWD) water-saving irrigation technique: Evidence from rice producers in the Philippines," Food Policy, Elsevier, vol. 36(2), pages 280-288, April.
    7. Philip W. Gassman & Jimmy R. Williams & Xiuying Wang & Ali Saleh & Edward Osei & Larry M. Hauck & R. César Izaurralde & Joan D. Flowers, 2009. "Agricultural Policy Environmental EXtender (APEX) Model: An Emerging Tool for Landscape and Watershed Environmental Analyses, The," Center for Agricultural and Rural Development (CARD) Publications 09-tr49, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhao, Xueyin & Chen, Mengting & Xie, Hua & Luo, Wanqi & Wei, Guangfei & Zheng, Shizong & Wu, Conglin & Khan, Shahbaz & Cui, Yuanlai & Luo, Yufeng, 2023. "Analysis of irrigation demands of rice: Irrigation decision-making needs to consider future rainfall," Agricultural Water Management, Elsevier, vol. 280(C).
    2. Edward Osei & Syed H. Jafri & Ali Saleh & Philip W. Gassman & Oscar Gallego, 2023. "Simulated Climate Change Impacts on Corn and Soybean Yields in Buchanan County, Iowa," Agriculture, MDPI, vol. 13(2), pages 1-21, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tewodros Assefa & Manoj Jha & Manuel Reyes & Abeyou W. Worqlul, 2018. "Modeling the Impacts of Conservation Agriculture with a Drip Irrigation System on the Hydrology and Water Management in Sub-Saharan Africa," Sustainability, MDPI, vol. 10(12), pages 1-19, December.
    2. Li, Runwei & Wei, Chenyang & Afroz, Mahnaz Dil & Lyu, Jun & Chen, Gang, 2021. "A GIS-based framework for local agricultural decision-making and regional crop yield simulation," Agricultural Systems, Elsevier, vol. 193(C).
    3. Hao Xu & Niu Niu & Dongmei Li & Chengjie Wang, 2024. "A Dynamic Evolutionary Analysis of the Vulnerability of Global Food Trade Networks," Sustainability, MDPI, vol. 16(10), pages 1-17, May.
    4. Nasca, J.A. & Feldkamp, C.R. & Arroquy, J.I. & Colombatto, D., 2015. "Efficiency and stability in subtropical beef cattle grazing systems in the northwest of Argentina," Agricultural Systems, Elsevier, vol. 133(C), pages 85-96.
    5. Aaron Michael Shew & Alvaro Durand‐Morat & Lawton Lanier Nalley & Karen Ann‐Kuenzel Moldenhauer, 2018. "Estimating the benefits of public plant breeding: beyond profits," Agricultural Economics, International Association of Agricultural Economists, vol. 49(6), pages 753-764, November.
    6. Ye, Qing & Yang, Xiaoguang & Dai, Shuwei & Chen, Guangsheng & Li, Yong & Zhang, Caixia, 2015. "Effects of climate change on suitable rice cropping areas, cropping systems and crop water requirements in southern China," Agricultural Water Management, Elsevier, vol. 159(C), pages 35-44.
    7. Amouzou, Kokou Adambounou & Naab, Jesse B. & Lamers, John P.A. & Borgemeister, Christian & Becker, Mathias & Vlek, Paul L.G., 2018. "CROPGRO-Cotton model for determining climate change impacts on yield, water- and N- use efficiencies of cotton in the Dry Savanna of West Africa," Agricultural Systems, Elsevier, vol. 165(C), pages 85-96.
    8. Shi, Xinrui & Batchelor, William D. & Liang, Hao & Li, Sien & Li, Baoguo & Hu, Kelin, 2020. "Determining optimal water and nitrogen management under different initial soil mineral nitrogen levels in northwest China based on a model approach," Agricultural Water Management, Elsevier, vol. 234(C).
    9. Garbero, Alessandra & Songsermsawas, Tisorn, 2016. "Impact of modern irrigation on household production and welfare outcomes: Evidence from the PASIDP project in Ethiopia," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235949, Agricultural and Applied Economics Association.
    10. Marrou, Hélène & Ghanem, Michel Edmond & Amri, Moez & Maalouf, Fouad & Ben Sadoun, Sarah & Kibbou, Fatimaezzhara & Sinclair, Thomas R., 2021. "Restrictive irrigation improves yield and reduces risk for faba bean across the Middle East and North Africa: A modeling study," Agricultural Systems, Elsevier, vol. 189(C).
    11. Liang, Hao & Lv, Haofeng & Batchelor, William D. & Lian, Xiaojuan & Wang, Zhengxiang & Lin, Shan & Hu, Kelin, 2020. "Simulating nitrate and DON leaching to optimize water and N management practices for greenhouse vegetable production systems," Agricultural Water Management, Elsevier, vol. 241(C).
    12. Zhang, Bangbang & Li, Xian & Chen, Haibin & Niu, Wenhao & Kong, Xiangbin & Yu, Qiang & Zhao, Minjuan & Xia, Xianli, 2022. "Identifying opportunities to close yield gaps in China by use of certificated cultivars to estimate potential productivity," Land Use Policy, Elsevier, vol. 117(C).
    13. Pitak Ngammuangtueng & Napat Jakrawatana & Pariyapat Nilsalab & Shabbir H. Gheewala, 2019. "Water, Energy and Food Nexus in Rice Production in Thailand," Sustainability, MDPI, vol. 11(20), pages 1-21, October.
    14. Kamini Yadav & Hatim M. E. Geli, 2021. "Prediction of Crop Yield for New Mexico Based on Climate and Remote Sensing Data for the 1920–2019 Period," Land, MDPI, vol. 10(12), pages 1-27, December.
    15. Lv, Yuping & Xu, Junzeng & Yang, Shihong & Liu, Xiaoyin & Zhang, Jiangang & Wang, Yijiang, 2018. "Inter-seasonal and cross-treatment variability in single-crop coefficients for rice evapotranspiration estimation and their validation under drying-wetting cycle conditions," Agricultural Water Management, Elsevier, vol. 196(C), pages 154-161.
    16. Ribaudo, Marc & Savage, Jeffrey, 2014. "Controlling non-additional credits from nutrient management in water quality trading programs through eligibility baseline stringency," Ecological Economics, Elsevier, vol. 105(C), pages 233-239.
    17. Tsiboe, Francis & Nalley, Lawton Lanier & Durand, Alvaro & Thoma, Greg & Shew, Aaron, 2017. "The Economic and Environmental Benefits of Sheath Blight Resistance in Rice," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 42(2), May.
    18. Paudel, G. & Krishna, V. & McDonald, A., 2018. "Why some inferior technologies succeed? Examining the diffusion and impacts of rotavator tillage in Nepal Terai," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277149, International Association of Agricultural Economists.
    19. Keijiro Otsuka & Yanyan Liu & Futoshi Yamauchi, 2016. "The future of small farms in Asia," Development Policy Review, Overseas Development Institute, vol. 34(3), pages 441-461, May.
    20. Silva, João Vasco & Pede, Valerien O. & Radanielson, Ando M. & Kodama, Wataru & Duarte, Ary & de Guia, Annalyn H. & Malabayabas, Arelene Julia B. & Pustika, Arlyna Budi & Argosubekti, Nuning & Vithoon, 2022. "Revisiting yield gaps and the scope for sustainable intensification for irrigated lowland rice in Southeast Asia," Agricultural Systems, Elsevier, vol. 198(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:17:p:6822-:d:402661. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.