Irrigation scheduling of paddy rice using short-term weather forecast data

Cao, Jiyue; Tan, Junwei; Cui, Yuanlai; Luo, Yufeng

doi:10.1016/j.agwat.2018.10.046

Cited by 51 publications

(20 citation statements)

References 35 publications

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“…In addition, a main N and P loss pathway is the direct loss of manure, fertilizer and/or soil to surface water by runoff [7]. Moreover, surface runoff is determined primarily by high irrigation and precipitation events [8]. Minimizing N and P concentrations in runoff is therefore important to protect receiving waters from eutrophication.…”

Section: Introductionmentioning

confidence: 99%

Nitrogen and phosphorus losses by surface runoff and soil microbial communities in a paddy field with different irrigation and fertilization managements

Wang¹,

Huang²

2021

PLoS ONE

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Rice cultivation usually involves high water and fertilizer application rates leading to the nonpoint pollution of surface waters with phosphorus (P) and nitrogen (N). Here, a 10-year field experiment was conducted to investigate N and P losses and their impact factors under different irrigation and fertilization regimes. Results indicated that T2 (Chemical fertilizer of 240 kg N ha−1, 52 kg P ha−1, and 198 kg K ha−1 combined with shallow intermittent irrigation) decreased N loss by 48.9% compared with T1 (Chemical fertilizer of 273 kg N ha−1, 59 kg P ha−1, and 112 kg K ha−1 combined with traditional flooding irrigation). The loss ratio (total N loss loading/amount of applied N) of N was 9.24–15.90%, whereas that of P was 1.13–1.31% in all treatments. Nitrate N (NO3-−N) loss was the major proportion accounting for 88.30–90.65% of dissolved inorganic N loss through surface runoff. Moreover, the N runoff loss was mainly due to high fertilizer input, soil NO3-−N, and ammonium N (NH4+−N) contents. In addition, the N loss was accelerated by Bacteroidetes, Proteobacteria, Planotomycetes, Nitrospirae, Firmicutes bacteria and Ascomycota fungi, but decreased by Chytridiomycota fungi whose contribution to the N transformation process. Furthermore, T2 increased agronomic N use efficiency (AEN) and rice yield by 32.81% and 7.36%, respectively, in comparison with T1. These findings demonstrated that T2 might be an effective approach to ameliorate soil chemical properties, regulate microbial community structure, increase AEN and consequently reduce N losses as well as maintaining rice yields in the present study.

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Section: Introductionmentioning

confidence: 99%

Nitrogen and phosphorus losses by surface runoff and soil microbial communities in a paddy field with different irrigation and fertilization managements

Wang¹,

Huang²

2021

PLoS ONE

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“…In irrigated crops, most of the times farmers apply irrigation without assessing its actual requirement. A new method for irrigation scheduling in paddy rice ( Oryza sativa ) incorporating short term weather forecasts in China proved to be a promising method for saving irrigation water without significant yield loss (Cao, Tan, Cui, & Luo, 2019). In a case study on corn production in the Havana Lowlands region, Illinois, incorporation of different types of weather forecasts into irrigation scheduling increased profitability over farmers’ practices (Wang & Cai, 2009).…”

Section: Resultsmentioning

confidence: 99%

Algorithms for weather‐based management decisions in major rainfed crops of India: Validation using data from multi‐location field experiments

et al. 2021

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Crop weather calendars (CWC) serve as tools for taking crop management decisions. However, CWCs are not dynamic, as they were prepared by assuming normal sowing dates and fixed occurrence as well as duration of phenological stages of rainfed crops. Sowing dates fluctuate due to variability in monsoon onset and phenology varies according to crop duration and stresses encountered. Realizing the disadvantages of CWC for issuing accurate agromet advisories, a protocol of dynamic crop weather calendar (DCWC) was developed by All India Coordinated Research Project on Agrometeorology (AICRPAM). The DCWC intends to automatize agromet advisories using prevailing and forecasted weather. Different modules of DCWC, namely, Sowing & irrigation schedules, crop contingency plans, phenophase-wise crop advisory, and advisory for harvest were prepared using long-term data of ten crops at nine centers of AICRPAM in eight states in India. Modules for predicting sowing dates and phenology were validated for principal crops and varieties at selected locations. The predicted sowing dates of 10 crops pooled over nine centers showed close relationships with observed values (r 2 of .93). Predicted phenology showed better agreement with observed in all crops except cotton (Gossypium L.; at Parbhani) and pigeon pea [Cajanus cajan (L.) Millsp.] (at Bangalore). Predicted crop phenology using forecasted and realized weather by DCWC are close to each other, but number of irrigations differed, and it failed for accurate prediction in groundnut at Anantapur in drought year (2014). The DCWCs require further validation for making it operational to issue agromet advisories in all 732 districts of India.

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“…Additionally, at the field scale, between 60 and 120 mm of water can be saved for autumn wheat, whose roots can be supplied with water from the shallow aquifer during the spring season. According to previous studies [14][15][16], the use of weather forecast should be considered for improving irrigation scheduling and for saving water in the irrigated agriculture. Our proposed approach is based on both weather and water table forecasts for providing irrigation forecast adapted to the alluvial soils specific to the Embanked Great Island of Danube River, Brăila, Romania.…”

Section: Comparison Between Short-term Irrigation Forecasts Applied mentioning

confidence: 99%

“…In the last two decades, several Decision Support Systems for irrigation scheduling (IS-DSS) have been designed for supporting irrigation decision makers [7][8][9]: PlanteInfo (Denmark), WISE (Washington Irrigation Scheduling Expert), IRRINET (Italy), IrriSAT (Australia), ISS-ITAP (Spain), BEWARE (Greece), Anglian river Basin (UK), and IRRISA (France). Modern IS DSS can be designed using different approaches such as Earth Observation (EO) techniques for crop monitoring [10][11][12][13], short and long-term weather forecasts [14][15][16], soil water balance numerical simulations [17,18], machine learning techniques [6], etc. Each of these approaches presents advantages and disadvantages, as follows: EO techniques are suitable for crop monitoring over large areas but in the case of optical sensors the presence of clouds results in gaps in observations; short and long-term weather forecasts provide anticipated meteorological conditions over a specific area and period of time but their performance decreases as the forecast period increases; soil water balance models are able to simulate soil water movement in the root zone but require high spatial resolution data.…”

Section: Introductionmentioning

confidence: 99%

Improving Irrigation Scheduling Using MOSES Short-Term Irrigation Forecasts and In Situ Water Resources Measurements on Alluvial Soils of Lower Danube Floodplain, Romania

Chiţu

Tomei

Villani

et al. 2020

Water

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In recent decades, water scarcity has become a frequent and widespread phenomenon. Intensification of water scarcity will have economic impact on the main water-using sectors. The highest pressure on the water resources is exerted by agriculture. Irrigation is the largest consumer of the agriculture sector and the efficient use of water is of utmost importance. The aim of this study is to explore the capability of an innovative platform that combines Earth Observation data, weather forecasts and numerical simulations to plan more precisely water allocation in space and time in the irrigated agriculture. This platform, created in the framework of MOSES, provides in Romania short-term irrigation forecasts adapted to the hydrological behavior of alluvial soils specific to the Lower Danube Floodplain. The short-term irrigation forecasts have been tested with applied water volumes and in situ water resources measurements in order to assess the water allocation in the irrigated agriculture. Although irrigation forecast was run operationally only one crop year (2017–2018), in the framework of MOSES Project, the comparison revealed that the irrigation scheduling in this area is more influenced by the infrastructure characteristics and less by the spatial distribution of crop water needs and availability of water resources. Our results show that short-term irrigation forecasts accompanied by real time monitoring of water resources could be successfully used in the irrigation scheduling activity for improving water allocation in space and time in the irrigated agriculture.

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Irrigation scheduling of paddy rice using short-term weather forecast data

Cited by 51 publications

References 35 publications

Nitrogen and phosphorus losses by surface runoff and soil microbial communities in a paddy field with different irrigation and fertilization managements

Nitrogen and phosphorus losses by surface runoff and soil microbial communities in a paddy field with different irrigation and fertilization managements

Algorithms for weather‐based management decisions in major rainfed crops of India: Validation using data from multi‐location field experiments

Improving Irrigation Scheduling Using MOSES Short-Term Irrigation Forecasts and In Situ Water Resources Measurements on Alluvial Soils of Lower Danube Floodplain, Romania

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