Simulation of alfalfa yield with AquaCrop

Raes, Dirk; Fereres, E.; Vila, Margarita García; Curnel, Yannick; Knoded, David; Çelik, Sema Kale; Uçar, Yusuf; Türk, Mevlüt; Wellens, Joost

doi:10.1016/j.agwat.2023.108341

Cited by 6 publications

(2 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Raes et al's study (2023) [36], the objective was to simulate alfalfa yield by adapting AquaCrop's 7th version to model perennial forage crops. Alfalfa (Medicago sativa L.) is a highly valued forage crop worldwide due to its perennial nature, nutritional richness, and deep root system, which enables it to access water and nutrients from the subsoil.…”

Section: Discussionmentioning

confidence: 99%

Assessment of Grassland Biomass Prediction Using AquaCrop Model: Integrating Sentinel-2 Data and Ground Measurements in Wielkopolska and Podlasie Regions, Poland

Panek-Chwastyk,

Ozbilge,

Dąbrowska-Zielińska

et al. 2024

Agriculture

View full text Add to dashboard Cite

This study aimed to compare remotely sensed data with in situ data using the AquaCrop simulation model for accurately monitoring growth conditions and predict grassland biomass in the north-eastern and central-western regions of Poland from 2020 to 2022. The model was calibrated using input data, including daily climate parameters from the ERA5-Land Daily Aggregated dataset, crop characteristics (initial canopy cover, maximum canopy cover, and harvest index), and soil characteristics. Additionally, parameters such as the leaf area index (LAI), soil texture classes, and plant growth stages were obtained through field campaigns. The grassland’s biomass simulation results indicate that the root mean square error (RMSE) values for the north-eastern region ranged from 0.12 to 0.35 t·ha−1, while for the central-western region, they ranged from 0.07 to 0.12 t·ha−1. Overall, the outcomes obtained from Sentinel-2 data perform comparably to the in situ measurements, and in some instances, even yield superior results. This study contributes valuable insights into grass production management on farms, providing essential information and tools for managers to better understand grass growth and development.

show abstract

Section: Discussionmentioning

confidence: 99%

Assessment of Grassland Biomass Prediction Using AquaCrop Model: Integrating Sentinel-2 Data and Ground Measurements in Wielkopolska and Podlasie Regions, Poland

Panek-Chwastyk,

Ozbilge,

Dąbrowska-Zielińska

et al. 2024

Agriculture

View full text Add to dashboard Cite

show abstract

“…Among all the models, AquaCrop is a water productivity driving model developed by the FAO and has the advantages of high precision and fewer parameters [32]. It has been broadly used in simulating the response of crop growth and water consumption to different water conditions and climate conditions [34][35][36][37] The progress in global climate models (GCMs) at different resolutions and the bias correction methods have opened pathways for numerous studies that aim to simulate the effects of future climate change [38][39][40][41]. Coupled Model Inter-comparison Project Phase 6 (CMIP6) is a global climate model comparison program jointly supported by the World Meteorological Organization (WMO) and the United Nations Environment Program (UNEP) [42].…”

Section: Introductionmentioning

confidence: 99%

Response of Matching Degree between Precipitation and Maize Water Requirement to Climate Change in China

Xiang,

Cheng,

Wang

et al. 2024

Agronomy

View full text Add to dashboard Cite

The synchronicity of rain and heat in the summer of China’s monsoon region provides sufficient water and heat resources for maize growth. However, the intra-annual distribution of precipitation and the probability of extreme precipitation have been inevitably altered by the ongoing climate change, thus affecting the matching degree between precipitation and crop water requirements (MDPCWR). Evaluating the extent to which the MDPCWR will change in the future is of great importance for food security and the sustainable management of water resources. In this study, considering that different growth stages of crops have different sensitivities to water stress, the AquaCrop model was used to calculate the MDPCWR more accurately. In addition, a cumulative distribution function-transform (CDF-t) method was used to remove the bias of 11 global climate models (GCMs) under two typical emission scenarios (SSP2-4.5 and SSP5-8.5) from phase six of the Coupled Model Intercomparison Project (CMIP6). A comprehensive investigation was conducted on how maize growth, water consumption, and the MDPCWR will respond to future climate change with CO2 concentration enrichment in the Huang–Huai–Hai (3H) region in China by driving a well-tested AquaCrop model with the bias-corrected GCMs outputs. The results indicate the following: (1) The CDF-t method can effectively remove seasonal bias, and it also performs well in eliminating the bias of extreme climate events. (2) Under the SSP2-4.5 scenario, the average maximum temperature will increase by 1.31 °C and 2.44 °C in 2021–2050 and 2051–2080, respectively. The average annual precipitation will increase up to 96.8 mm/year, but it will mainly occur in the form of heavy rain. (3) The increased maize evapotranspiration rate does not compensate for the decreased crop water requirement (up to −32 mm/year), due to a shorter growth cycle. (4) The farmland cultivation layer is not able to hold a significant amount of precipitation, due to the increased frequency of heavy rains, resulting in increased irrigation water requirements for maize over the next two periods, with the maximum value of 12 mm/year. (5) Under different scenarios, the projected future MDPCWR will decrease by 9.3–11.6% due to changes in precipitation patterns and crop water requirements, indicating that it will be more difficult for precipitation to meet the water demand of maize growing in the 3H region. The results can provide comprehensive information to understand the impact of climate change on the agricultural water balance and improve the regional strategy for water resource utilization in the 3H region.

show abstract

A holistic simulation model of solid-set sprinkler irrigation systems for precision irrigation

Morcillo,

Ortega,

Ballesteros

et al. 2024

Precision Agric

View full text Add to dashboard Cite

Simulation of alfalfa yield with AquaCrop

Cited by 6 publications

References 33 publications

Assessment of Grassland Biomass Prediction Using AquaCrop Model: Integrating Sentinel-2 Data and Ground Measurements in Wielkopolska and Podlasie Regions, Poland

Assessment of Grassland Biomass Prediction Using AquaCrop Model: Integrating Sentinel-2 Data and Ground Measurements in Wielkopolska and Podlasie Regions, Poland

Response of Matching Degree between Precipitation and Maize Water Requirement to Climate Change in China

A holistic simulation model of solid-set sprinkler irrigation systems for precision irrigation

Contact Info

Product

Resources

About