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Proceeding Paper

Water-Saving Techniques and Practices for On-Farm Surface Irrigation Systems †

by
José Manuel Gonçalves
1,2,*,
Qingfeng Miao
3,*,
Isabel Maria Duarte
1,4 and
Haibin Shi
3
1
Instituto Politécnico de Coimbra, Escola Superior Agrária, 3045-601 Coimbra, Portugal
2
LEAF-Linking Landscape, Environment, Agriculture and Food, Institute of Agronomy, University of Lisbon, 1349-017 Lisboa, Portugal
3
College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
4
CERNAS-Research Centre for Natural Resources, Environment and Society, 3045-601 Coimbra, Portugal
*
Authors to whom correspondence should be addressed.
Presented at the 1st International Electronic Conference on Agronomy, 3–17 May 2021. Available online: https://iecag2021.sciforum.net/.
Biol. Life Sci. Forum 2021, 3(1), 46; https://doi.org/10.3390/IECAG2021-09675
Published: 1 May 2021
(This article belongs to the Proceedings of The 1st International Electronic Conference on Agronomy)

Abstract

:
As surface irrigation systems are one of the most used water management techniques in the world, often working with high water losses, there is an urgency to their improvement. Modern methods that provide water savings and labour reduction require adequate design, management knowledge, and technical decision support. This study aims to improve water saving techniques for on-farm systems through an example of the decision-aid process being applied as a methodology based on the field experimentation and modelling of modern surface irrigation technologies in the Hetao (China) and Lower Mondego (Portugal) case studies. The performance indicators of irrigation water productivity (IWP) and the economic water productivity ratio (EWPR) were applied to compare the performance of several project solutions. The results obtained include a complete description of the latest irrigation solutions at the level basin, graded border, and graded furrows, which are adaptable to those case studies. The results revealed the benefits of a level-basin solution. For example, in Hetao, replacing the traditional system with a 100 m or 200 m long level basin, resulted in an increase in IWP by 40% and 3%, respectively, and in EWPR by 23% and 67%, respectively. The effect of a longer basin enables the reduction in operative costs, with a slight increase in distribution uniformity. In Lower Mondego, the IWP increased by 65% and the EWPR increased by 82%, by adopting graded furrows with 1.0‰ slopes and 200 m lengths. The main drivers of development of these surface irrigation systems were determined, namely, the runoff reuse and the system design and management. The issue that the local markets with equipment and consulting services should be available for farmers is also relevant to the development. It was proven that the effectiveness of modern surface water systems must adapt the solutions of the projects to the local characteristics of plot size and slope, soil type, and water supply.

1. Introduction

The increasing demand for water by multiple non-agricultural users and climate variability affect the provision of water resources, leading society to seek ways to save water in irrigated agriculture, particularly in water scarce areas [1]. However, water savings in agriculture must be compatible with the technical knowledge of farmers and the economic sustainability of their farms. This task is complex because the severe reduction of water supplies for irrigation has serious social, economic, and environmental impacts, particularly for poor peasant farmers [2]. Consequently, the water use in agriculture requires an assessment of its value to completely understand the results of any change. It is also necessary to search out technical solutions associated with agronomic and irrigation practices that may adapt these systems to deal with water scarcity and climate change [3].
Surface irrigation systems are characterized by the water application to at least one or more places at the top of the field with a free flow over the soil surface and by using gravity to infiltrate into the root zones of the crops [4]. These systems are adopted worldwide, representing more than 83% of the world’s irrigated area, mainly in Asia, for both rice paddies and field crops [5]. The benefits of adopting surface irrigation include the simplicity of its application at farms in flat areas with low infiltration rates, namely, when water conveyance and distribution are performed with canals or low-pressure pipe systems, low capital investment, and low energy consumption [4,6,7]. Its most significant limitations include high soil infiltration and high variability of infiltration throughout the field, land-leveling requirements, the need for control of a constant inflow rate, difficulties in matching irrigation time duration with soil water deficits at the time of irrigation, and difficult access to equipment for mechanized and automated water application and distribution [8,9].
In response to the challenges posed by the assorted problems identified within the practice of irrigation, the knowledge of surface irrigation has advanced with new developments. Regarding modelling issues, progress was made in predicting the advance and recession curves [10], infiltration, and soil roughness [11,12,13]. Concerning the irrigation processes, the advances in basins [14], border [15], and furrows [13,16] have been recorded. As for the optimization of the on-farm system management, progress has been made, both in conventional methodologies [8,15,17,18] and within the application of artificial intelligence resources [19,20,21]. Presently, the problem of the water savings in surface irrigation systems focuses on the challenges of the practical implementation of innovative measures; aiming for economic and environmental sustainability is a priority.
The objective of this study is to contribute to water savings by comparing the performance gains of surface irrigation projects in traditional and modern systems. The research was carried out in two areas, Hetao (China) and Lower Mondego (Portugal), with arid continental monsoon and Mediterranean climate regions, respectively, to illustrate the practical issues of modernizing irrigation and its impacts on irrigation water use and the economy. The most relevant measures and practices to use in the design and operations of the systems are presented to cope with the priority issues of climate change, environmental impact, water-use efficiency, and irrigation technology and innovation.

2. Materials and Methods

For this study, two distinct geographic areas were selected, the Hetao irrigation district, China (Hetao) and the Lower Mondego irrigation district, Portugal (Lower-Mondego). These areas have in common the maize crop irrigated by surface methods as the most representative crop, with high economic and social regional relevance. The main soil characteristics are presented in Table 1.
The field measurements included the inflow rates, cut-off, advance and recession times, soil moisture prior and after the irrigation, and maize crop development, as previously described [4]. Field infiltration tests were performed, providing a primary estimation of the Kostiakov infiltration equation parameters (Equation (1)):
I ( τ ) = K · τ a + f 0 · τ ,
I (m3·m−2) the cumulative infiltration, K (m3·m−1·min−a), a (dimensionless), and f0 (m3·m−1·min−1) empirical parameters were later optimized using field advance and recession observations through the inverse method with the model SIRMOD [22]. The irrigation scheduling was determined for the complete irrigation of maize by applying the water balance method and was according to the methodology (Table 2) by Allen et al. [23]. The Hetao irrigation scheduling applies to low salinity soil, with a silty, loamy texture [24], and the Lower-Mondego irrigation scheduling applies to loamy soil [25].
The procedure for creating the required design alternatives and for their evaluation and ranking followed the various steps, as described by Gonçalves and Pereira [25] in which hydraulic simulations were performed with the model SIRMOD. The wheat irrigation application in Hetao is presented in [27]. It allows us to determine the performance indicators of design alternatives (Equations (2) to (6)) that are useful to assess and compare alternatives. The input data refers to the crop data (Table 2), infiltration parameters and Manning’s hydraulic roughness (Table 3), and other technical and economic parameters (Table 4).
A set of irrigation projects were selected based on the authors’ long field experience, while applying a medium value of inflow rate per furrow, and were defined with respect to the plot length. Accordingly, for Hetao, the furrowed level basin (LB) was designed with lengths of 50 m, 100 m, and 200 m, and the furrowed graded basin (GB) had longitudinal slopes of 0.05% and 0.10% for the lengths of 100 m and 200 m, respectively. The Lower Mondego furrowed level basin (LB) was designed with 100 m and 200 m lengths, and the graded furrows had longitudinal slopes of 0.05% and 0.10% (GF) with 100 m, 200 m, and 265 m lengths. The on-farm water distribution system used in Hetao was the non-lined canal equipped with modern field gates, well-adjusted to the high charge of sediments of irrigation water, which does not allow for a pipe distribution system; whereas Lower Mondego had the lay-flat tubing with manual valves to adjust each single gate.
The irrigation performance indicators adopted are described below [28]:
The beneficial water use fraction (BWUF, %), expressing the efficiency of water application in the field, is defined as:
BWUF = { Z avg D × 100 ; Z lq > Z req Z lq D × 100 ; Z lq < Z r ,
where Zavg is the average depth of water infiltrated within the whole irrigated field (mm), Zlq is the average low quarter depth of water infiltrated (mm), and D is the average water depth (mm) applied to the field. The two equations are used to distinguish the cases of over-irrigation (Zlq > Zreq) and under-irrigation (Zlq < Zreq).
Distribution uniformity (DU, %), which expresses the standard of the irrigation system to uniformly infiltrate the water spatially, is defined as:
DU = Z lq Z avg × 100 ,
Irrigation water productivity (IWP, kg·m−3), which expresses the amount of physical production obtained per unit of irrigation water applied, is defined as:
IWP = Y a IWU ,
where Ya is the actual crop yield, and IWU is the irrigation water use.
The economic water productivity ratio (EWPR) is defined as:
EWPR = Value ( Yield ) TIC ,
where Value (Yield) is the value of the yield, and TIC is the total irrigation cost, which expresses the economical production obtained per unit of cost relative to the irrigation water applied.
Total irrigation cost (TIC, € ha−1) is defined as:
TIC = PLLC + IWC + ILC + DSC ,
where PLLC is the precise land-levelling cost, IWC is the irrigation water cost, ILC is the irrigation labour cost, and DSC is the distribution system cost.

3. Results

3.1. Hetao Study Site

The modern irrigation methods considered for Hetao were the LB and the GB with a longitudinal slope of 0.5‰, with precise land levelling. The medium inflow rates were fixed in line with the land parcel sizes of 50 m, 100 m, and 200 m. The most relevant results achieved (vd. Figure 1) can be summarised as follows:
  • The modern systems show a significant increase of performance in comparison with the traditional system, with a DU and BWUF of about 60%, because of irregular land levelling and therefore, the practice of over-irrigation.
  • Precise land-levelled basins (LB) allow for a very high DU (up to 91%), which in turn allows for achieving a high BWUF for basin lengths from 50 m to 100 m (88–90%). GB with a slope of 0.5‰ is additionally an appropriate choice for the lengths of 100 m and 200 m, with a BWUF of 71% to 84%. However, these results assume that farmers apply an appropriate irrigation scheduling and cut-off time.
  • Focusing on the rise of water productivity, the results indicate that the modern LB systems with 50 m and 100 m allow the utmost IWP of 2.4 kg·m−3; for LB-200 m, IWP is 1.7 kg·m−3, although with the highest EWPR of 11.8. These values of water productivity for maize agree with other published data [29,30,31].
  • The performance of those modern systems requires that farmers apply an appropriate irrigation scheduling and cut-off time.

3.2. Lower Mondego Study Site

The modern irrigation methods analysed were the furrowed LB with 100 m and 200 m lengths, and the graded furrows had longitudinal slopes of 1.0‰ (GF-1) with 100 m, 200 m, and 265 m lengths. Precise land levelling was always carried out. A medium value of inflow rate per furrow was defined in relation to the furrow’s length. The on-farm water distribution system considered the lay-flat tubing with manual valves to adjust each single gate. The results to highlight (vd. Figure 2) are the following:
  • Precise land-levelled basins (LB) are a good alternative for short fields with a length of about 100 m, DU of 88%; and basins of 200 m. The advance is not so fast, and the DU is close to 80%, identical to the traditional systems (75%).
  • Graded furrows with 1.0‰ slope (GB-1) show the best performance, particularly for longer fields with lengths of 200 m and 265 m, DU of about 90%, and BWUF higher than 85%.
  • The traditional system with a BWUF of about 70% and a DU of 75% should be replaced by the modernized systems because these allow for the increase of IWP and EWPR.
  • Focusing on the increase of water productivity, the results point out that the modern LB systems with lengths of 100 m and GB-1 with 200 m or 265 m allow for the maximum IWP of 2.0 to 2.3 kg·m−3, although the GB-1 with 200 m or 265 m allow for the highest EWPR of 9.1–9.3 because with longer fields there are irrigation cost savings, namely for the distribution system and labour. These values of water productivity for maize agree with other published data [29,30,31].

4. Discussion

Our results highlight that there is high potential for surface irrigation development in these studied cases. Similar results are reported in the recent bibliography. As surface irrigation is used in precarious and inefficient ways in various regions of the world, there is a significant margin for progress regarding water savings and water productivity improvement [29]. The traditional systems are often degraded, and their development and modernization represent a major technical, economic, and social challenge [2,4]. The systems located in water-scarce areas have great potential for water saving because the technologies that allow for an increase of water productivity can be quite effective [1]. Even in modernized systems, there are several factors that can be adjusted to increase the efficiency [8,14,32].
In Hetao, if the traditional system is replaced by the level basin with the 100 m length, the IWP would increase 40% and the EWPR would increase 23%. If instead, it was to be replaced by the 200 m length, the increases would be 3% on the IWP and 67% on the EWPR. The effect of a longer basin (200 m) impacts the operative cost reduction with a mild increase on distribution uniformity. This study showed that the size of the plots has a significant impact on irrigation development, namely that small-sized fields constrain the on-farm water distribution and the pathway to the irrigation automation to reduce labour requirements, a problem also discussed by Araujo et al. [33]. The level basins or the graded borders with the lengths of 200 m allowed the highest IWP and EWPR, but these solutions require land reparcelling.
In Lower Mondego, adopting graded furrows with 1.0‰ and 200 m length, the IWP increased 65% and the EWPR increased 82%. This performance is similar to the pressurized systems with a higher EWPR due to a reduced energy consumption, which agrees with the results of several authors [6,7,16].
Improving the irrigation performance requires a variety of measures and practices that act on the system design and operations, which provide for reducing the water use, increasing land and water productivity, and enabling a higher farmer income. These main aspects are further developed below.
  • Precision land levelling—Laser-assisted precision land-levelling control is a technology applied in modern surface irrigation systems that allows for a fast and efficient operation, as these equipment are available worldwide [34,35]. The precise land levelling reduces the time and the volume of water needed to complete the advance while improving the infiltration uniformity and allowing for higher yields and prevents ponding and improves drainage [36].
  • Level basin—The equipment to control the inflow should allow for adjustments according to the basin length and slope to obtain high distribution uniformity and labour savings [37]. Irrigation performance can be improved through the shortening of the basin length or the reducing of their width, allowing for shortened time and higher distribution uniformity when the inflow rate is small or variable [14].
  • Rice paddy—Irrigation should reduce the surface water depths in basins to minimize seepage and percolation losses while aiming for higher yields and better water productivity [38]. Alternate wetting and drying is a method that allows for percolation losses and methane emission reductions [39].
  • Graded border—The modernization follows the procedures of basin irrigation, namely adopting precise land-levelling, and analogous equipment for inflow rate control [8,40]. Outflow can be controlled by an anticipated cut-off when the borders are open with the runoff reuse. A recent trend is the conversion of the border method to the basin irrigation, which adopts a ridge-furrow system for row crops when land slopes are small [9,15].
  • Graded furrows—The inflow rate control may be achieved with different types of equipment [41], namely gated pipes, including lay-flat tubes, which supply water to each furrow under automated valve control, as well as different techniques, such as surge flow, irrigation with cutback, cablegation, and irrigation by alternate furrows [15,17].
  • Reuse of tail water runoff—The drainage reuse system of runoff, either through a pumping system to the parcel head side or by gravity to other fields, can be used when the systems have a tail end open, mainly a graded border and furrows. The reuse systems could be integrated in automatic on-farm distribution systems, allowing for water and labour savings [42,43].
  • Irrigation systems design—Advances in the design method are supported on simulation modelling and computing, which provide good tools for new systems, as well as for the modernization of traditional ones. These design models integrate the hydraulics simulation modelling with irrigation scheduling, land levelling, water delivery and distribution systems [8,32,41], and cost and environmental analysis [25].
  • Irrigation systems real-time management—The control of the inflow rate and cut-off time allow for the coping of the effects of soil infiltration variability in time and space. The utilization of sensors for water-advancing monitoring in furrows or borders and the use of wireless transmission and controllers equipped with receivers and specific operational algorithms open perspectives for higher irrigation performance and system automation [44,45].
  • Water distribution on irrigation district scale—The improvement of the quality of the off-farm distribution system is a determinant to the performance of the on-farm irrigation performance and district scale water savings. Some examples are the improvement of water management through better maintenance and conservation of hydraulic infrastructure, the implementation of optimal operational plans to adjust the water demand with distribution, and the water reuse from drainage ditches [18,41,46,47].

5. Conclusions

This study concludes for the Hetao and Lower-Mondego study cases that, if properly designed, the modern surface irrigation systems have significant positive impacts on water productivity when compared with the traditional ones. The surface irrigation systems can be sustainable and optimised for each case. Generally, the requirements are the inflows are controlled, precise land-levelling is adopted, systems are properly designed, irrigation scheduling is appropriate, fertilizer management is adequate, and crop management, including the control of pests and weeds, is appropriate.
There is a large variety of surface irrigation methods that reveal their adaptability to climate, crops, landforms, and cropping techniques and contribute to the resilience of agricultural systems to global change. Their very low energy demand and the low investment requirements also contribute to irrigation sustainability. The local markets with equipment and consulting services available for farmers play an important role on development. However, adaptation to a new water resource paradigm implies a great harFmonized effort among farmers and technicians, as well as incentives for farmers and the support of extension services adapted to each case.

Supplementary Materials

The video presentation can be downloaded at: https://www.mdpi.com/article/10.3390/IECAG2021-09675/s1.

Author Contributions

Conceptualization, J.M.G., Q.M., and H.S.; methodology, J.M.G. and Q.M.; data analysis, J.M.G., Q.M., and I.M.D.; writing–original draft preparation, J.M.G. and Q.M; writing—review and editing, J.M.G., Q.M., and I.M.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation, grant number 52009056, 51839006, 51539005; Inner Mongolia Natural Science Foundation grant number 2019BS05015; and projects of Inner Mongolia Agricultural University, grant number NDSC2018- 11; 2017XQG-4, NDYB2016-23.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Roldán-Cañas, J.; Moreno-Pérez, M.F. Water and Irrigation Management in Arid and Semiarid Zones. Water 2021, 13, 2446. [Google Scholar] [CrossRef]
  2. Lenton, R. Irrigation in the twenty-first century: Reflections on science, policy and society. Irrig. Drain. 2014, 63, 154–157. [Google Scholar] [CrossRef]
  3. Pereira, L.S.; Cordery, I.; Iacovides, I. Coping with Water Scarcity, Addressing and Challenges; Springer: Berlin/Heidelberg, Germany, 2009. [Google Scholar]
  4. Pereira, L.S.; Gonçalves, J.M. Surface irrigation. In Oxford Encyclopedia of Agriculture and the Environment, Subject: Sustainability and Solutions, Agriculture and the Environment; Oxford University Press: Oxford, UK, 2018. [Google Scholar]
  5. AQUASTAT—FAO’s Global Information System on Water and Agriculture. Water Use. Available online: www.fao.org/nr/water/aquastat/water_use/index.stm (accessed on 15 December 2021).
  6. Spencer, G.D.; Krutz, L.J.; Falconer, L.L.; Henry, W.B.; Henry, C.G.; Larson, E.J.; Pringle, H.C.; Bryant, C.J.; Atwill, R.L. Irrigation Water Management Technologies for Furrow-Irrigated Corn that Decrease Water Use and Improve Yield and On-Farm Profitability. Crop. Forage Turfgrass Manag. 2019, 5, 1–8. [Google Scholar] [CrossRef] [Green Version]
  7. Reba, M.L.; Massey, J.H. Surface Irrigation in the Lower Mississippi River Basin: Trends and Innovations. Trans. ASABE 2020, 63, 1305–1314. [Google Scholar] [CrossRef]
  8. Nie, W.-B.; Li, Y.-B.; Zhang, F.; Ma, X.-Y. Optimal discharge for closed-end border irrigation under soil infiltration variability. Agric. Water Manag. 2019, 221, 58–65. [Google Scholar] [CrossRef]
  9. Liu, K.; Jiao, X.; Guo, W.; An, Y.; Salahou, M.K. Improving border irrigation performance with predesigned varied-discharge. PLoS ONE 2020, 15, e0232751. [Google Scholar] [CrossRef]
  10. Chari, M.M.; Davary, K.; Ghahraman, B.; Ziaei, A.N. General equation for advance and recession of water in border irri-gation. Irrig. Drain. 2019, 68, 476–487. [Google Scholar] [CrossRef]
  11. Salahou, M.K.; Jiao, X.; Lü, H. Assessment of Empirical and Semi-Empirical Models for Estimating a Soil Infiltration Function. Trans. ASABE 2020, 63, 833–845. [Google Scholar] [CrossRef]
  12. Githui, F.; Hussain, A.; Morris, M. Incorporating infiltration in the two-dimensional ANUGA model for surface irrigation simulation. Irrig. Sci. 2020, 38, 373–387. [Google Scholar] [CrossRef]
  13. Mazarei, R.; Mohammadi, A.S.; Ebrahimian, H.; Naseri, A.A. Temporal variability of infiltration and roughness coefficients and furrow irrigation performance under different inflow rates. Agric. Water Manag. 2021, 245, 106465. [Google Scholar] [CrossRef]
  14. Smith, R.; Uddin, M. Selection of flow rate and irrigation duration for high performance bay irrigation. Agric. Water Manag. 2020, 228, 105850. [Google Scholar] [CrossRef]
  15. Xu, J.; Cai, H.; Saddique, Q.; Wang, X.; Li, L.; Ma, C.; Lu, Y. Evaluation and optimization of border irrigation in different irrigation seasons based on temporal variation of infiltration and roughness. Agric. Water Manag. 2019, 214, 64–77. [Google Scholar] [CrossRef]
  16. Ebrahimian, H.; Ghaffari, P.; Ghameshlou, A.N.; Tabatabaei, S.-H.; Dizaj, A.A. Extensive comparison of various infiltration estimation methods for furrow irrigation under different field conditions. Agric. Water Manag. 2019, 230, 105960. [Google Scholar] [CrossRef]
  17. Saberi, E.; Siuki, A.K.; Pourreza-Bilondi, M.; Shahidi, A. Development of a simulation–optimization model with a multi-objective framework for automatic design of a furrow irrigation system. Irrig. Drain. 2020, 69, 603–617. [Google Scholar] [CrossRef]
  18. Nie, W.-B.; Dong, S.-X.; Li, Y.-B.; Ma, X.-Y. Optimization of the border size on the irrigation district scale—Example of the Hetao irrigation district. Agric. Water Manag. 2021, 248, 106768. [Google Scholar] [CrossRef]
  19. Pazouki, E. A practical surface irrigation design based on fuzzy logic and meta-heuristic algorithms. Agric. Water Manag. 2021, 256, 107069. [Google Scholar] [CrossRef]
  20. Hoseini, Y. Use fuzzy interface systems to optimize land suitability evaluation for surface and trickle irrigation. Inf. Process. Agric. 2019, 6, 11–19. [Google Scholar] [CrossRef]
  21. Emamgholizadeh, S.; Seyedzadeh, A.; Sanikhani, H.; Maroufpoor, E.; Karami, G. Numerical and artificial intelligence models for predicting the water advance in border irrigation. Environ. Dev. Sustain. 2021, 24, 1–18. [Google Scholar] [CrossRef]
  22. Walker, W.R. SIRMOD III: Surface Irrigation Simulation, Evaluation and Design—Guide and Technical Documentation; Utah State University: Logan, UT, USA, 2003. [Google Scholar]
  23. Allen, R.G.; Pereira, L.S.; Raes, D.; Smith, M. Crop Evapotranspiration. In Guidelines for Computing Crop Water Requirements; FAO Irrigation and Drainage Paper 56; FAO: Rome, Italy, 1998; 300p. [Google Scholar]
  24. Miao, Q.; Rosa, R.D.; Shi, H.; Paredes, P.; Zhu, L.; Dai, J.; Gonçalves, J.M.; Pereira, L.S. Modeling water use, transpiration and soil evaporation of spring wheat–maize and spring wheat–sunflower relay intercropping using the dual crop coefficient approach. Agric. Water Manag. 2016, 165, 211–229. [Google Scholar] [CrossRef]
  25. Gonçalves, J.M.; Pereira, L.S. Decision Support System for Surface Irrigation Design. J. Irrig. Drain. Eng. 2009, 135, 343–356. [Google Scholar] [CrossRef] [Green Version]
  26. Miao, Q.; Gonçalves, J.M.; Li, R.; Gonçalves, D.; Levita, T.; Shi, H. Assessment of Precise Land Levelling on Surface Irrigation Development. Impacts on Maize Water Productivity and Economics. Sustainability 2021, 13, 1191. [Google Scholar] [CrossRef]
  27. Miao, Q.; Shi, H.; Gonçalves, J.M.; Pereira, L.S. Basin Irrigation Design with Multi-Criteria Analysis Focusing on Water Saving and Economic Returns: Application to Wheat in Hetao, Yellow River Basin. Water 2018, 10, 67. [Google Scholar] [CrossRef] [Green Version]
  28. Pereira, L.S.; Cordery, I.; Iacovides, I. Improved indicators of water use performance and productivity for sustainable water conservation and saving. Agric. Water Manag. 2012, 108, 39–51. [Google Scholar] [CrossRef]
  29. Molden, D.; Oweis, T.; Steduto, P.; Bindraban, P.; Hanjra, M.A.; Kijne, J. Improving agricultural water productivity: Between optimism and caution. Agric. Water Manag. 2010, 97, 528–535. [Google Scholar] [CrossRef]
  30. Ali, M.; Talukder, M. Increasing water productivity in crop production—A synthesis. Agric. Water Manag. 2008, 95, 1201–1213. [Google Scholar] [CrossRef]
  31. Zwart, S.J.; Bastiaanssen, W.G.M. Review of measured crop water productivity values for irrigated wheat, rice, cotton and maize. Agric. Water Manag. 2004, 69, 115–133. [Google Scholar] [CrossRef]
  32. Fuentes, C.; Chávez, C. Analytic Representation of the Optimal Flow for Gravity Irrigation. Water 2020, 12, 2710. [Google Scholar] [CrossRef]
  33. Araujo, D.F.; Costa, R.N.; Mateos, L. Pros and cons of furrow irrigation on smallholdings in northeast Brazil. Agric. Water Manag. 2019, 221, 25–33. [Google Scholar] [CrossRef]
  34. Dedrick, A.R.; Gaddis, R.J.; Clark, A.W.; Moore, A.W. Land Forming for Irrigation. In Design and Operation of Farm Irrigation Systems, 2nd ed.; Hoffman, G.J., Evans, R.G., Jensen, M.E., Martin, D.L., Elliot, R.L., Eds.; ASABE: St. Joseph, MI, USA, 2007; pp. 320–346. [Google Scholar]
  35. Evangelista, G.K.M. Land Gradient Effects on Water Productivity in Rice Production; Crop Science, University of Philippines: Los Banos, Philippines, 2019. [Google Scholar]
  36. Devkota, K.P.; Yadav, S.; Humphreys, E.; Kumar, A.; Kumar, P.; Kumar, V.; Malik, R.; Srivastava, A.K. Land gradient and configuration effects on yield, irrigation amount and irrigation water productivity in rice-wheat and maize-wheat cropping systems in Eastern India. Agric. Water Manag. 2021, 255, 107036. [Google Scholar] [CrossRef]
  37. Reddy, J.M. Design of Level Basin Irrigation Systems for Robust Performance. J. Irrig. Drain. Eng. 2013, 139, 254–260. [Google Scholar] [CrossRef]
  38. Samoy-Pascual, K.; Yadav, S.; Evangelista, G.; Burac, M.A.; Rafael, M.; Cabangon, R.; Tokida, T.; Mizoguchi, M.; Regalado, M.J. Determinants in the Adoption of Alternate Wetting and Drying Technique for Rice Production in a Gravity Surface Irrigation System in the Philippines. Water 2021, 14, 5. [Google Scholar] [CrossRef]
  39. Carrijo, D.; Lundy, M.E.; Linquist, B.A. Rice yields and water use under alternate wetting and drying irrigation: A meta-analysis. Field Crop. Res. 2017, 203, 173–180. [Google Scholar] [CrossRef]
  40. Morris, M.R.; Hussain, A.; Gillies, M.H.; O’Halloran, N.J. Inflow rate and border irrigation performance. Agric. Water Manag. 2015, 155, 76–86. [Google Scholar] [CrossRef]
  41. Replogle, J.A.; Kruse, E.G. Chapter Delivery and Distribution Systems. In Design and Operation of Farm Irrigation Systems, 2nd ed.; Hoffman, G.J., Evans, R.G., Jensen, M.E., Martin, D.L., Elliot, R.L., Eds.; ASABE: St. Joseph, MI, USA, 2007; pp. 499–531. [Google Scholar]
  42. American Society of Agricultural and Biological Engineers (ASABE). Surface Irrigation Runoff Reuse Systems; Standards EP408.3; ASAE: St. Joseph, MI, USA, 2014. [Google Scholar]
  43. Dayer, E.; Pazira, E.; Kashkuli, H.A.; Sedghi, H. Changing Furrow Irrigation to Increase Efficiency and Feasibility Study of Reusing Surface Runoff. Civ. Eng. J. 2018, 3, 1278. [Google Scholar] [CrossRef] [Green Version]
  44. Koech, R.K.; Smith, R.J.; Gillies, M. A real-time optimisation system for automation of furrow irrigation. Irrig. Sci. 2014, 32, 319–327. [Google Scholar] [CrossRef]
  45. Arnold, B.J.; Upadhyaya, S.K.; Wallender, W.W.; Grismer, M.E. Sensor-Based Cutoff Strategy for Border Check–Irrigated Fields. J. Irrig. Drain. Eng. 2015, 141, 04014081. [Google Scholar] [CrossRef]
  46. Chavez, C.; Fuentes, C. Design and evaluation of surface irrigation systems applying an analytical formula in the irrigation district 085, La Begoña, Mexico. Agric. Water Manag. 2019, 221, 279–285. [Google Scholar] [CrossRef]
  47. Gonçalves, J.M.; Ferreira, S.; Nunes, M.; Eugénio, R.; Amador, P.; Filipe, O.; Duarte, I.M.; Teixeira, M.; Vasconcelos, T.; Oliveira, F.; et al. Developing Irrigation Management at District Scale Based on Water Monitoring: Study on Lis Valley, Portugal. AgriEngineering 2020, 2, 78–95. [Google Scholar] [CrossRef] [Green Version]
Figure 1. Indicators of Hetao case study for maize crop: (a) beneficial water use fraction (BWUF) and distribution uniformity (DU); (b) irrigation water productivity (IWP) and economic water productivity ratio (EWPR) relative to the traditional practice and five improved irrigation conditions using precise land levelling: LB-50, LB-100, and LB-200, level basin method with field lengths of 50 m, 100 m, and 200 m, respectively; GB-0.5-100 and GB-0.5-200, graded border method with a longitudinal slope of 0.5‰ and field lengths of 100 m and 200 m, respectively.
Figure 1. Indicators of Hetao case study for maize crop: (a) beneficial water use fraction (BWUF) and distribution uniformity (DU); (b) irrigation water productivity (IWP) and economic water productivity ratio (EWPR) relative to the traditional practice and five improved irrigation conditions using precise land levelling: LB-50, LB-100, and LB-200, level basin method with field lengths of 50 m, 100 m, and 200 m, respectively; GB-0.5-100 and GB-0.5-200, graded border method with a longitudinal slope of 0.5‰ and field lengths of 100 m and 200 m, respectively.
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Figure 2. Indicators of Lower Mondego irrigation district case study for maize crop: (a) beneficial water use fraction (BWUF) and distribution uniformity (DU); (b) irrigation water productivity (IWP) and economic water productivity ratio (EWPR) relative to the traditional practice and five improved irrigation conditions using precise land levelling: LB-100 and LB-200, level basin method with lengths of 100 m and 200 m, respectively; GF-1-100, GF-1-200, and GF-1-265, graded furrow method with slope of 1.0‰ and fields of 100 m, 200 m, and 265 m, respectively.
Figure 2. Indicators of Lower Mondego irrigation district case study for maize crop: (a) beneficial water use fraction (BWUF) and distribution uniformity (DU); (b) irrigation water productivity (IWP) and economic water productivity ratio (EWPR) relative to the traditional practice and five improved irrigation conditions using precise land levelling: LB-100 and LB-200, level basin method with lengths of 100 m and 200 m, respectively; GF-1-100, GF-1-200, and GF-1-265, graded furrow method with slope of 1.0‰ and fields of 100 m, 200 m, and 265 m, respectively.
Blsf 03 00046 g002
Table 1. Main soil properties on case studies of Hetao and Lower Mondego.
Table 1. Main soil properties on case studies of Hetao and Lower Mondego.
Particle Size Distribution (%) Soil Water Content (cm3·cm−3)
Study SiteDepth (m)ClaySiltSandBulk Density
(g cm−3)
At SaturationField CapacityWilting Point
0.00–0.2023.076.70.31.520.470.360.16
0.20–0.4012.181.66.31.490.480.370.16
Hetao0.40–0.6014.684.21.21.490.490.370.16
0.60–0.8035.164.90.01.450.500.390.17
0.80–1.0042.557.50.01.440.520.410.18
Lower Mondego0.00–0.301534511.310.520.310.13
0.30–0.601534511.330.510.300.12
0.60–0.901825571.400.500.290.11
Data obtained in the soil laboratory of the Inner Mongolia Agricultural University, Hohhot, and in the soil laboratory of the College of Agriculture of Polytechnic Institute of Coimbra.
Table 2. Average data of maize full irrigation scheduling and crop cycle in the experimental areas (source [26]).
Table 2. Average data of maize full irrigation scheduling and crop cycle in the experimental areas (source [26]).
Study SiteNIENTI
(mm)
SNI
(mm)
SNIS 1
(mm)
Yield
(Mg ha−1)
AI
(mm)
ER
(mm)
ETc Act
(mm)
CC
(Days)
Hetao59045030312.00230103753154
Lower-Mondego75639214012.000130535140
1 NIE—number of irrigation events; NTI—net target irrigation (mm); SNI—season net irrigation (mm); SNIS—season non-irrigation supply (mm); AI—autumn irrigation (mm); ER—effective rainfall (mm); ETcact—actual crop evapotranspiration (mm); CC—crop cycle (days). Hetao data refers to silty loam on Dengkou; Mondego data refers to loamy soil.
Table 3. Infiltration and hydraulic roughness parameters used for irrigation system design (source [26]).
Table 3. Infiltration and hydraulic roughness parameters used for irrigation system design (source [26]).
Irrigation EventK
(m3 m−1 min−a)
a
(-)
f0
(m3 m−1 min−1)
n
(m−1/3 s)
HetaoFirst 0.00490.52600.04
Later0.00450.51000.04
Lower--MondegoFirst 0.00420.6250.000200.04
Later0.00320.5630.000170.04
K—coefficient of infiltration function (m3 m−1 min−a); a—exponent of infiltration function (dimensionless); f0—basic infiltration rate (m3 m−1 min−1); n—Manning’s hydraulic roughness (m−1/3 s); Hetao data refers to silty loam on Dengkou; Lower Mondego data refers to loamy soil.
Table 4. Technical and economic parameters used for irrigation system design (2020 prices) (source [26]).
Table 4. Technical and economic parameters used for irrigation system design (2020 prices) (source [26]).
ParameterHetaoL. Mondego
Water distribution equipment type of equipmentNon-lined canalLayflat tubing
aquisition cost0.125 € m−11.0 € m−1
effective life time1 year1 year
Water cost price per volume0.010 € m−30.025 € m−3
fixed per area100 € ha−1100 € ha−1
Crop priceyield price0.30 € kg−10.30 € kg−1
Labour cost unit cost4.0 € h−15.0 € h−1
Currency exchange: 1 Euro = 8.0 Yuan.
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Gonçalves, J.M.; Miao, Q.; Duarte, I.M.; Shi, H. Water-Saving Techniques and Practices for On-Farm Surface Irrigation Systems. Biol. Life Sci. Forum 2021, 3, 46. https://doi.org/10.3390/IECAG2021-09675

AMA Style

Gonçalves JM, Miao Q, Duarte IM, Shi H. Water-Saving Techniques and Practices for On-Farm Surface Irrigation Systems. Biology and Life Sciences Forum. 2021; 3(1):46. https://doi.org/10.3390/IECAG2021-09675

Chicago/Turabian Style

Gonçalves, José Manuel, Qingfeng Miao, Isabel Maria Duarte, and Haibin Shi. 2021. "Water-Saving Techniques and Practices for On-Farm Surface Irrigation Systems" Biology and Life Sciences Forum 3, no. 1: 46. https://doi.org/10.3390/IECAG2021-09675

APA Style

Gonçalves, J. M., Miao, Q., Duarte, I. M., & Shi, H. (2021). Water-Saving Techniques and Practices for On-Farm Surface Irrigation Systems. Biology and Life Sciences Forum, 3(1), 46. https://doi.org/10.3390/IECAG2021-09675

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