Performance evaluation of AquaCrop model for simulation of onion crop under mulch and non-mulch conditions

Introduction

Water productivity in agriculture, defined as "the ratio of the net benefits from crop, forestry, livestock, and mixed agricultural systems to the amount of water required to produce these benefits" (Molden et al., 2007) [12] , has become increasingly critical in modern agricultural management. The central goal of improving water productivity is to generate food, income, livelihood, and ecological benefits while minimizing environmental and social costs per unit of water utilized. In the present scenario of growing water scarcity, there is an urgent need to manage irrigation water efficiently to achieve optimum agricultural yields with limited water resources. Enhancing current crop water production levels through improved irrigation practices represents one of the most promising strategies for water conservation and optimal production. While irrigation has been practiced since the dawn of civilization, drip irrigation has emerged as one of the most efficient methods, delivering water precisely to the plant root zone. This approach is particularly valuable for horticultural and vegetable crops. Over the past five decades, drip irrigation combined with synthetic plastic mulching techniques has been extensively implemented worldwide to maximize water use efficiency and improve agricultural productivity, especially in water-scarce regions. Singh et al. (2023) [18] demonstrated that employing drip irrigation at 80% of crop evapotranspiration (ETC) with polyethylene mulch led to significant improvements in onion cultivation, including taller plants, increased leaf counts, larger bulb diameters, 47% water savings, and a 48% increase in yield compared to conventional surface irrigation methods. Onions occupy a significant place in human diets due to their nutritional value and medicinal properties. They contain various pharmacologically active compounds, particularly S-alkenyl cysteine sulfoxides (ACSOs), which contribute to their distinctive flavor and pungency. These compounds confer antimicrobial, antibacterial, hypoglycemic, hypocholesterolemic, antiatherosclerotic, and antithrombotic properties (Ortola et al., 2015) [14] . For optimal yield, onion crops typically require between 350 and 550 millimeters of water. Consistent watering is essential to prevent bulb cracking and the formation of 'doubles,' while also ensuring high crop quality. Within India, Maharashtra leads onion production, followed by Madhya Pradesh, with Bihar ranking sixth after Rajasthan. As global population continues to rise, agricultural systems face mounting pressure to increase yields. Given the limited potential for expanding arable land, production increases must come primarily through enhanced productivity per unit of land. This necessitates more efficient water management strategies. Understanding crop water requirements is fundamental to the effective design and management of drip irrigation systems. This knowledge can be obtained either through field experiments or through modeling approaches. Various models are available for simulating agricultural water requirements, irrigation scheduling, yield, biomass, and water productivity. Among these, FAO's AquaCrop model has gained prominence in agricultural research and practice. Developed through collaboration between FAO and renowned experts from major research centers, international governmental organizations, and academic institutions, the model has undergone continuous refinement, with FAO-AquaCrop version 6.1 released in May 2018. As a dynamic crop simulation tool, AquaCrop offers various alternatives for predicting crop responses to different field management practices. Given its focus on yield response to water, the latest AquaCrop version 7.1 presents an excellent opportunity for testing and developing water management strategies specifically for onion cultivation.

Materials and Methods

The experiment took place at the Precision Farming Development Centre (PFDC) in the department of Soil and Water Conservation Engineering, CEAT, Dr. RPCAU, Pusa, Bihar. The field is located at 25.98°N latitude, 85.67°E longitude, and approximately 52.00 meters above the sea level. The region experiences a sub humid climate influenced by the southwest monsoon. This location was chosen due to its relevance for studying the performance of the Aqua Crop model under the local climatic conditions. The soil was formed by sediment transported down by the Burhi Gandak river, and it was relatively fresh. Because there was 26.6% calcium carbonate present, the soil was calcareous. The soil type at the experiment site is sandy loam, comprising 52% sand, 18% silt, and 30% clay. The experiment site's average bulk density was ascertained through applying the core technique. The mean bulk density has been found at 1.56 g/cc. The field technique was used to determine the soil's field capacity and saturation soil moisture content. The onion plants were transplant with a spacing of 15×10 cm on February 3, 2024, on the prepared bed. The experiment was laid out in a randomized block design with six treatment, three replication. The treatment details of experiments are presented in Table 1. The data on the weather condition during the crop growing season of present investigation concerning maximum and minimum temperature, rainfall, wind speed, relative humidity, evaporation and bright sunshine hours were obtained from Agro-meteorology observatory, Dr., RPCAU, Pusa. It has presented in Fig. 1.

Table 1: Details of treatments

Sr. No. Treatments

T 1 Application of 100% water requirement without plastic mulch (control) 2.

T 2 Application of 100% water requirement with plastic mulch 3.

T 3 Application of 80% water requirement without plastic mulch 4.

T 4 Application of 80% water requirement with plastic mulch 5.

T 5 Application of 60% water requirement without plastic mulch 6.

T 6 Application of 60% water requirement with plastic mulch.

FAO-AquaCrop model

The FAO-AquaCrop model, developed by Steduto et al.

(2009) [15] and Raes et al. (2009) [15] , is a crop growth simulation tool that integrates four key components. [15] .

Soil water balance

The soil water balance in AquaCrop tracks water storage in the root zone by considering water inflows and outflows. Root zone depletion influences water stress factors (Ks), which affect canopy expansion, transpiration, canopy aging, harvest index, and root growth rate.

Climatic data

The AquaCrop model requires daily weather data, including minimum and maximum air temperatures, reference crop evapotranspiration (ETo), rainfall, and the mean annual carbon dioxide (CO2) concentration. ETo is calculated using the ETo calculator, which considers daily maximum and minimum temperatures, wind speed measured at 2 meters above the ground, solar radiation, and daily relative humidity (RH).

Metrological Condition

Soil parameters

The soil data required for the AquaCrop model at the experimental site includes the number of soil horizons, soil texture, field capacity (FC), permanent wilting point (PWP), saturated hydraulic conductivity (Ksat), and volumetric water content at saturation (sat). The experimental site had no impervious or restrictive soil layers that could hinder root growth. Surface runoff from rainfall during the experiment was estimated using the site's curve number (CN).

Management practices

Field and irrigation management practices were evaluated. Field management included soil fertility levels and methods to conserve soil water, such as plastic mulching. Fertility levels influenced canopy development, senescence, and water productivity.

For irrigation management, we choose irrigated conditions, with drip irrigation methods. Irrigation scheduling varied based on depth, timing, and seasonal needs. Full irrigation (100% via drip) and deficit irrigation (80% and 60%) were applied on the same days as full irrigation plots. Fertility levels were consistent across all treatments.

Parameter Adjustment

The initial onion model parameters were obtained from the AquaCrop database. Key factors such as canopy cover development, rooting depth, and water productivity were adjusted to match observed growth patterns and yield outcomes. These refinements enhanced the model's accuracy, improving its reliability in simulating onion growth and optimizing irrigation and crop management practices.

Model evaluation criteria

The AquaCrop model's onion yield and biomass predictions were compared with observed data from a February 3 to May 22, 2024 experiment. Prediction error (Pe), R², mean absolute error (MAE), root mean square error (RMSE), and model efficiency (E) were used to assess accuracy. R² and E evaluated predictive capability, while Pe, MAE, RMSE, and NRMSE indicated prediction errors. Biomass at harvest and onion bulb yield were key evaluation parameters, with Pe and E calculated using the Nash and Sutcliffe method (1970) [13] .

Where (𝑆 𝑖 ) and (𝑂 𝑖 ) represent the predicted and actual (observed) values, (𝑂 ̅ 𝑖 ) is the mean of the observed values, and N denotes the number of observations.

Where, 𝑦 𝑖 = Actual value 𝑥 𝑖 = Predicted value Model efficiency (E) and R² values close to one, along with prediction error (Pe), mean absolute error (MAE), and root mean square error (RMSE) near zero, indicate better model performance.

Results and Discussion

FAO-AquaCrop Model Evaluation for Onion Crop

The AquaCrop model was analyzed using 2024 experimental data to forecast onion yield under varying irrigation and mulch conditions. The evaluation focused on onion biomass and yield, comparing simulated and observed data. Three irrigation levels and mulch/non-mulch conditions were assessed. Prediction error, R², MAE, RMSE, and model efficiency were used to evaluate performance.

Simulation of bulb yield

The model's performance in predicting onion bulb yield with and without mulch is shown in Table 3 and Fig. 2 Strong correlations were observed between simulated and observed yields, with R² values of 0.7-0.9 for mulch and non-mulch conditions and 0.99 for combined treatments.

The model showed good accuracy with RMSE (0.33), MAE (0.53), and prediction errors ranging from 1.46% to 9.71% (Table 5, & Fig. 4 (a)). It performed reliably across all irrigation levels (100%, 80%, 60%), demonstrating robustness under varying water stress conditions.

Simulation of Biomass

The AquaCrop model accurately simulated onion biomass with R² values of 0.6-0.9 for individual treatments and 0.99 for combined data. Prediction errors were low (RMSE: 0.28, MAE: 0.44) with errors ranging from 0.71% to 5.26% (Table 4, Fig. 3 [11] , Bello and Walker (2017) [7] , reinforcing AquaCrop's reliability in simulating onion growth under varying conditions.

AquaCrop model evaluation results

References

1. Abdelkhalik A, Pascual B, Nájera I, Baixauli C, Pascual-Seva N. Regulated deficit irrigation as a watersaving strategy for onion cultivation in Mediterranean conditions. Agronomy. 2019;9(9):521. https://doi.org/10.3390/agronomy9090521
2. Agbemabiese YK. Modelling biomass and bulb yield of onion (Allium cepa) under different irrigation regimes using the AquaCrop model [Doctoral dissertation]. 2015.
3. Agbemabiese YK, Shaibu AG, Gbedzi VD. Validation of AquaCrop for different irrigation regimes of onion (Allium cepa) in Bontanga Irrigation Scheme. Irrigation Science, Engineering and Development. 2017;1(1):112.
4. Allen RG, Pereira LS, Raes D, Smith M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations. 1998;56(97):e156.
5. FAO. Discussions on the risk assessment of Campylobacter. Risk Assessment. 2001;11:53.
6. Bekele S, Tilahun K. Regulated deficit irrigation scheduling of onion in a semiarid region of Ethiopia. Agricultural Water Management. 2007;89(1-2):148152. https://doi.org/10.1016/j.agwat.2007.01.002
7. Bello ZA, Walker S. Evaluating AquaCrop model for simulating production of Amaranthus cruentus, a leafy vegetable, under irrigation and rainfed conditions. Agricultural and Forest Meteorology. 2017;247:300310.
8. Carr MKV. Crop yield response to water. FAO Irrigation and Drainage Paper 66. By Steduto P, Hsiao TC, Fereres E, Raes D. Rome, Italy: Food and Agriculture Organization of the United Nations. 2013;49(2):311-1. https://www.fao.org/4/i2800e/i2800e00.htm
9. Kumar V. Performance evaluation of FAO-AquaCrop model for maize crop in eastern part of Indo-Gangetic plain [M.Tech Thesis]. Dr. RPCAU; 2018.
10. Kumari P, Bara AD, Kumar M, Job M, Rai P. Crop water requirement and water use efficiency of cauliflower under mulching and drip irrigation in eastern plateau hills region of Jharkhand. Progressive Horticulture. 2020;52(1):81-87. http://dx.doi.org/10.5958/2249-5258.2020.00012.3
11. Mohammadi M, Ghahraman B, Davary K, Ansari H, Shahidi A. Validation of AquaCrop model for simulation of winter wheat yield and water use efficiency under simultaneous salinity and water stress. Agricultural Water Management. 2019;217:444-456.
12. Molden D, Oweis TY, Pasquale S, Kijne JW, Hanjra MA, Bindraban PS, et al. Pathways for increasing agricultural water productivity; 2007.
13. Nash JE, Sutcliffe JV. River flow forecasting through conceptual models part I-A discussion of principles. Journal of Hydrology. 1970;10(3):282-290.
14. Ortolá MP, Knox JW. Water relations and irrigation requirements of onion (Allium cepa L.): A review of yield and quality impacts. Experimental Agriculture. 2015;51(2):210-231.
15. Raes D, Steduto P, Hsiao TC, Fereres E. AquaCropthe FAO crop model to simulate yield response to water: II. Main algorithms and software description. Agronomy Journal. 2009;101(3):438-447. https://doi.org/10.2134/agronj2008.0140s
16. Rao KVR, Gangwar S, Aherwar P, Yadav D. Growth, yield, economics and water use efficiency of onion (Allium cepa L.) under different micro irrigation systems; 2019.
17. Shanono NJ, Abba BS, Nasidi NM. Evaluation of AquaCrop Model using Onion Crop under Deficit Irrigation and Mulch in Semi-arid Nigeria. Turkish Journal of Agricultural Engineering Research. 2022;3(1):131-145. https://doi.org/10.46592/turkager.1078082
18. Singh MC, Rajak D, Dahiphale P. Impact of differential irrigation on performance of onion cultivated with and without plastic mulching: Performance of onion under differential irrigation. Journal of Soil Salinity and Water Quality. 2023;15(1):84-93.