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2025 Vol.67, Issue 1 Preview Page
Optimizing Irrigation and Water Productivity for Maize Based on Soil Moisture Deficit and Crop Water Stress
aGraduate Student, Department of Food Security and Agricultural Development, Kyungpook National University
bAssociate Professor, Department of Agricultural Civil Engineering, Kyungpook National University
†Corresponding author Tel.: +82-53-950-5734 Fax: +82-53-950-6752 E-mail: ycshin@knu.ac.kr
2025. pp. 59-76
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Abstract

Erratic rainfall due to climate change in the Kenya’s Arid and Semi-Arid Lands, particularly Makueni County, threatens rain-fed maize production and food security. This study explores deficit irrigation to optimize water use, aiming to improve water productivity, stabilize yields and enhance resilience, aligning with Sustainable Development Goals on water management and climate action. The Soil-Water-Atmosphere-Plant (SWAP) model is used for estimating soil moisture/transpiration and managing irrigation and drainage across diverse climatic and environmental contexts from 2013 to 2022. We derived the Soil Moisture Deficit Index (SMDI) and Crop Water Stress Index CWSI to assess drought conditions and optimize irrigation schedules. Several numerical experiments were conducted to validate and test SWAP in balancing water use and productivity at the field-scale. The SMDI-based irrigation interval (7-day) consistently outperformed in achieving water productivity up to 25.2 kg ha-1 mm-1 compared to the results (22.5 kg ha-1 mm-1) of CWSI. SMDI showed better crop yields and water savings, especially in the dry years, while CWSI performed better in the wetter years but consumed more water. The 7-day interval with SMDI offered the best balance between water efficiency and productivity. Although SMDI requires advanced soil moisture monitoring, it proved more efficient than CWSI, which may be suitable for less-resourced areas.

References

REFERENCES

1
M. H. Ali, M. R. Hoque, A. A. Hassan and A. Khair, Agricultural Water Management, Effects of deficit irrigation on yield, water productivity, and economic returns of wheat, 232; 106033 (2020)
2
R. S. Ali and H. K. Mohammad, Plant Production Science, Alternate furrow irrigation with different irrigation intervals for maize (Zea mays L.), 8(5); 592-600 (2005)10.1626/pps.8.592
3
E. Amiri, Communications in Soil Science and Plant Analysis, Evaluation of water schemes for maize under arid area in Iran using the SWAP model, 48(16); 1963-1976 (2017)10.1080/00103624.2017.1408817
4
J. Bellvert, P. J. Zarco-Tejada, J. Girona and E. Fereres, Precision Agriculture, Mapping crop water stress index in a ‘Pinot-noir’ vineyard: Comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle, 15(4); 361-376 (2014)10.1007/s11119-013-9334-5
5
J. J. E. Bessembinder, P. A. Leffelaar, A. S. Dhindwal and T. C. Ponsioen, Agricultural Water Management, Which crop and which drop, and the scope for improvement of water productivity, 73(2); 113-130 (2005)10.1016/j.agwat.2004.10.004
6
R. Çakir, Field Crops Research, Effect of water stress at different development stages on vegetative and reproductive growth of corn, 89(1); 1-16 (2004)10.1016/j.fcr.2004.01.005
7
Q. Chai, Y. Gan, C. Zhao, H. L. Xu, R. M. Waskom, Y. Niu and K. H. Siddique, Agronomy for Sustainable Development, Regulated deficit irrigation for crop production under drought stress: A review, 36; 1-21 (2016)10.1007/s13593-015-0338-6
8
T. Chai and R. R. Draxler, Geoscientific Model Development, Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature, 7(3); 1247-1250 (2014)10.5194/gmd-7-1247-2014
9
S. Dabach, N. Lazarovitch, J. Šimůnek and U. Shani, Irrigation Science, Numerical investigation of irrigation scheduling based on soil water status, 31(1); 27-36 (2013)10.1007/s00271-011-0289-x
10
P. Droogers, Journal of Hydrology, Estimating actual evapotranspiration using a detailed agro-hydrological model, 229(1-2); 50-58 (2000)10.1016/S0022-1694(99)00198-5
11
T. Du, S. Kang, J. Sun, X. Zhang and J. Zhang, Agricultural Water Management, An improved water use efficiency of cereals under temporal and spatial deficit irrigation in north China, 97(1); 66-74 (2010)10.1016/j.agwat.2009.08.011
12
M. D. Dukes and J. M. Scholberg, Applied Engineering in Agriculture, Soil moisture controlled subsurface drip irrigation on sandy soils, 21(1); 89-101 (2005)10.13031/2013.17916
13
J. Fang and Y. Su, Scientific Reports, Effects of soils and irrigation volume on maize yield, irrigation water productivity, and nitrogen uptake, 9(1); 7740 (2019)10.1038/s41598-019-41447-z31123262PMC6533300
14
FAO, The State of Food and Agriculture, 2020 (2020)10.1787/a0848ac0-es38483583PMC10965744
15
K. Fattahi, H. Babazadeh, P. Najafi and H. Sedghi, Applied Ecology & Environmental Research, Scheduling maize irrigation based on Crop Water Stress Index (CWSI), 16(6); 7535-7549 (2018)10.15666/aeer/1606_75357549
16
E. Fereres and M. A. Soriano, Journal of Experimental Botany, Deficit irrigation for reducing agricultural water use, 58(2); 147-159 (2007)10.1093/jxb/erl16517088360
17
S. Fuentes, J. Trejo-Alonso, A. Quevedo, C. Fuentes and C. Chávez, Mathematics, Modeling soil water redistribution under gravity irrigation with the Richards equation, 8(9); 1581 (2020)10.3390/math8091581
18
S. Geerts and D. Raes, Agricultural Water Management, Deficit irrigation as an on-farm strategy to maximize crop water productivity in dry areas, 96(9); 1275-1284 (2009)10.1016/j.agwat.2009.04.009
19
GOK, National climate change action plan 2018-2022 (2018)
20
GOK, Agricultural sector transformation and growth strategy 2019-2029 (2019)
21
V. González-Dugo, P. J. Zarco-Tejada, E. Nicolás and M. V. Cuevas, Remote Sensing, Monitoring of evapotranspiration and crop water stress with thermal and optical remote sensing from unmanned aerial vehicles, 7(5); 5160-5185 (2019)10.3390/rs70505160.
22
R. J. Hijmans, Database of Global Administrative Areas, https://gadm.org (2009)
23
S. B. Idso, R. D. Jackson, P. J. Pinter Jr, R. J. Reginato and J. L. Hatfield, Agricultural Meteorology, Normalizing the stress-degree-day parameter for environmental variability, 24; 45-55 (1981)10.1016/0002-1571(81)90032-7
24
S. Irmak, D. Haman and R. Bastug, Agronomy Journal, Determination of crop water stress index for irrigation timing and yield estimation of corn, 92; 1221-1227 (2000)10.2134/agronj2000.9261221x
25
J. A. M. Janssen, M. Van der Velde, T. Tóth, F. G. A. Verheijen and R. J. A. Jones, Climate Research, Assessment of future irrigation water requirements, availability, and water use efficiency in the European Union under climate change, 71(1); 19-34 (2017)10.103354/cr01431
26
R. A. I. Jeyasingh, S. Murugan, M. S. Devakumari and R. I. Manuel, International Journal on Environment and Climate Change, Influence of CWSI-based irrigation scheduling on agronomic traits (Zea mays L.) and sustainable water use in maize, 13(8); 384-390 (2023)10.9734/ijecc/2023/v13i81964
27
S. Khan, A. Rasool, S. Irshad, M. B. Hafeez, M. Ali, M. Saddique and A. Al-Hashimi, Journal of Water and Climate Change, Potential soil moisture deficit: A useful approach to save water with enhanced growth and productivity of wheat crop, 12(6); 2515-2525 (2021)10.2166/wcc.2021.356
28
M. J. Khanjani, A. Shahidi and M. Raeini-Sarjaz, Journal of Water and Hydraulic Research, Evaluation of SMDI drought index changes using data and output of SWAP model under climate change conditions, 2(4); 1-14 (2023)
29
K. Il Ko, M. H. Lee and H. Yoe, International Conference on Artificial Intelligence in Information and Communication (ICAIIC), CWSI-based smart irrigation system design; 37-40 (2023)10.1109/icaiic57133.2023.10066983
30
J. G. Kroes, J. C. van Dam, J. Huygen and R. W. Vervoort, Alterra, Green World Research, User’s guide of SWAP Version 2.0; simulation of water flow, solute transport and plant growth in the soil-water-atmosphere-plant environment (Issue Alterra-rapport 610) (2002)
31
J. G. Kroes, J. C. Van Dam, R. P. Bartholomeus, P. Groenendijk, M. Heinen, R. F. A. Hendriks, H. M. Mulder, I. Supit and P. E. V. Van Walsum, Swap.Wur.Nl, SWAP version 4 (2017)10.18174/416321
32
J. G. Kroes, J. G. Wesseling and J. C. Van Dam, Hydrological Processes, Integrated modelling of the soil-water-atmosphere-plant system using the model SWAP 2.0: An overview of theory and an application, 14(11-12); 1993-2002 (2000)10.1002/1099-1085(20000815/30)
33
J. Lipiec and R. Hatano, Geoderma, Quantification of compaction effects on soil physical properties and crop growth, 116; 107-136 (2003)10.1016/s0016-7061(03)00097-1
34
B. Lubajo, Effect of deficit irrigation on growth and yield of maize (Zea mays) in Kiboko Research Station, http://erepository.uonbi.ac.ke/handle/11295/161838, Makueni County, Kenya. (2022)
35
M. Mö Ller, V. Alchanatis, Y. Cohen, M. Meron, J. Tsipris, A. Naor, V. Ostrovsky, M. Sprintsin and S. Cohen, Citeseer, Use of thermal and visible imagery for estimating crop water status of irrigated grapevine (2007)10.1093/jxb/erl11516968884
36
S. Mohanty, L. Das, S. Patnaik, R. Pradhan and S. Jena, Journal of Climate Impact and Adaptation Strategies, Assessment of the impact of climate change on water availability and crop yields using the SWAP model in India, 12(4); 345-359 (2015)
37
D. Molden, Accounting for water use and productivity (No. 42) (1997)
38
Y. Mualem, Water Resources Research, A new model for predicting the hydraulic conductivity of unsaturated porous media, 12(3); 513-522 (1976)10.1029/wr012i003p00513
39
B. Narasimhan and R. Srinivasan, Agricultural and Forest Meteorology, Development and evaluation of Soil Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI) for agricultural drought monitoring, 133(1-4); 69-88 (2005)10.1016/j.agrformet.2005.07.012
40
NASA, POWER Data Access Viewer (DAV) and Application Programming Interface (API), https://power.larc.nasa.gov/docs/methodology/data/ (2021)
41
A. Nemes, M. Schaap and F. L. Laboratory, The UNSODA unsaturated soil hydraulic database Version 2.0. Data.Nal.Usda.Gov, https://data.nal.usda.gov/system/files/UNSODA%202.0%20README.pdf (1999)
42
S. K. Ng’ang’a, P. M. Kibetu, G. M. Thumbi, G. O. Okwadha and M. O. Nyadawa, Journal of International Academic Research for Multidisciplinary, Groundwater mapping of Makueni County, eastern Kenya using remote sensing and geographical information system (GIS) technologies, 3(8); 266-277 (2015)
43
S. A. O’Shaughnessy and S. R. Evett, Agricultural Water Management, Canopy temperature-based system effectively schedules and controls center pivot irrigation of cotton, 97(9); 1310-1316 (2010)10.1016/j.agwat.2010.03.012
44
N. Oguge and F. Oremo, Rainwater-Smart Agriculture in Arid and Semi-Arid Areas: Fostering the Use of Rainwater for Food Security, Poverty Alleviation, Landscape Restoration and Climate Resilience, Fostering the use of rainwater for off-season small-scale irrigation in arid and semi-arid areas of Kenya; 159-174 (2018)10.1007/978-3-319-66239-8_9
45
N. N. Omoyo, J. Wakhungu and S. Oteng’i, Agriculture & Food Security, Effects of climate variability on maize yield in the arid and semi-arid lands of lower eastern Kenya, 4; 1-13 (2015)10.1186/s40066-015-0028-2
46
H.-I. Park, Communications for Statistical Applications and Methods, Tests of equality of several variances with the likelihood ratio principle, 25(4); 329-337 (2018)10.29220/csam.2018.25.4.329
47
U. R. Sangakkara, P. Amarasekera and P. Stamp, Plant Soil and Environment, Irrigation regimes affect early root development, shoot growth, and yields of maize (Zea mays) in tropical minor seasons, 56(5); 228-234 (2010)10.17221/217/2009-pse
48
M. G. Schaap, F. J. Leij and M. T. Van Genuchten, Soil Science Society of America Journal, Neural network analysis for hierarchical prediction of soil hydraulic properties, 62(4); 847-855 (1998)10.2136/sssaj1998.03615995006200040001x
49
M. R. Shaheb, R. Venkatesh and S. A. Shearer, Journal of Biosystem Engineering, A review on the effect of soil compaction and its management for sustainable crop production, 46; 417-439 (2021)10.1007/s42853-021-00117-7
50
Y. Shin and Y. Jung, Journal of Irrigation and Drainage Engineering, Development of irrigation water management model for reducing drought severity using remotely sensed soil moisture footprints, 140(7); 04014021 (2014)10.1061/(asce)ir.1943-4774.000073629515898PMC5830787
51
Y. Shin, B. P. Mohanty and A. V. Ines, Vadose Zone Journal, Estimating effective soil hydraulic properties using spatially distributed soil moisture and evapotranspiration, 12(3), vzj2012-0094 (2013)10.2136/vzj2012.0094
52
State Department for Crops Development, Report on food crop data validation in Makueni County (2022)
53
L. Taparauskiene and L. K. Heng, Agricultural Water Management, Adaptation of the SWAP model for simulating soil moisture dynamics in a strawberry field, 95(8); 892-902 (2008)10.1016/j.agwat.2008.02.004.
54
J. C. Van Dam, J. Huygen, J. G. Wesseling, R. A. Feddes, P. Kabat, P. E. V. Van Walsum, W. P. Groenendijk, C. A. Van Diepen and D. Report, Theory of SWAP version 2.0; simulation of water flow, solute transport and plant growth in the soil-water-atmosphere-plant environment, https://library.wur.nl/WebQuery/wurpubs/fulltext/222782 (1997)
55
M. T. Van Genuchten, Soil Science Society of America Journal, A closed‐form equation for predicting the hydraulic conductivity of unsaturated soils, 44(5); 892-898 (1980)10.2136/sssaj1980.03615995004400050002x
56
R. Viji and P. R. Prasanna, Journal of Environmental Research and Development, Assessment of water holding capacity of major soil series of Lalgudi, Trichy, India, 7(1A); 393-398 (2012)
57
C. Wang, J. Zhao, Y. Feng, M. Shang, X. Bo, Z. Gao and Q. Chu, Agricultural Water Management, Optimizing tillage method and irrigation schedule for greenhouse gas mitigation, yield improvement, and water conservation in wheat-maize cropping systems, 248; 106762 (2021)10.1016/j.agwat.2021.106762
58
J. Wu, R. Zhang and S. Gui, Plant and Soil, Modeling soil water movement with water uptake by roots, 215(1); 7-17 (1999)10.1023/a:1004702807951
59
J. Yi, H. Li, Y. Zhao, M. Shao, H. Zhang and M. Liu, Agricultural Water Management, Assessing soil water balance to optimize irrigation schedules of flood-irrigated maize fields with different cultivation histories in the arid region, 265; 107543 (2022)10.1016/j.agwat.2022.107543
60
X. Yin, M. Jabloun, J. E. Olesen, I. Öztürk, M. Wang and F. Chen, The Journal of Agricultural Science, Effects of climatic factors, drought risk, and irrigation requirement on maize yield in the Northeast Farming Region of China, 154(7); 1171-1189 (2016)10.1017/s0021859616000150
61
X. Zhang, Y. Wang, H. Sun, S. Chen and L. Shao, Agricultural Water Management, Improving water productivity and reducing water use through soil moisture deficit index-based irrigation scheduling for wheat crops, 210; 36-45 (2018)10.1016/j.agwat.2018.07.004
62
Y. Zhang, J. Li, M. Huang, Y. Yang, L. Li and F. Wang, Agricultural Water Management, Response of maize to deficit irrigation under different soil and climatic conditions in China: Meta-analysis; 416-424 (2019)10.1016/j.agwat.2019.05.016
63
Y. Zhao, X. Mao and M. K. Shukla, Agricultural and Forest Meteorology, A modified SWAP model for soil water and heat dynamics and seed-maize growth under film mulching; 292-293, 108127 (2020)10.1016/j.agrformet.2020.108127
64
S. J. Zwart and W. G. M. Bastiaanssen, Agricultural Water Management, Review of measured crop water productivity values for irrigated wheat, rice, cotton, and maize, 69(2); 115-133 (2004)10.1016/j.agwat.2004.04.007
Information
  • Publisher :The Korean Society of Agricultural Engineers
  • Publisher(Ko) :한국농공학회
  • Journal Title :Journal of Korean Society of Agricultural Engineers
  • Journal Title(Ko) :한국농공학회논문집
  • Volume : 67
  • No :1
  • Pages :59-76
  • Received Date : 2024-11-18
  • Revised Date : 2024-12-14
  • Accepted Date : 2024-12-18
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