Studies on improved technologies and aquacrop model performance for groundnut (Arachis hypogaea L.) production in Yadgir district of Kalyan Karnataka region, India

Introduction

Agriculture is an important source of food security as well as livelihood for rural population in India (Kadiyala et al., 2021) [30] . Agricultural production in India mainly depends on rainfall and other agronomical management practices (Reddy et al., 2020) [41] . Over the past few decades, there have been substantial changes in agricultural productivity due to changes in climate, land utilization and cropping patterns (Umesh et al., 2022) [58] . India is still dedicated to ensuring food, nutritional security and employment through several government initiatives (Panda 2023) [61] . However, climate change, land degradation and high crop inputs cost lead to severe challenges for agricultural production, particularly under rainfed situations (Datta et al., 2022) [17] . These challenges are presumed to decrease food production as well as increase food prices, which leads to malnourishment and hunger-related problems in the country (Reddy et al., 2022) [43] . Groundnut (Arachis hypogaea L.) is an essential oilseed crop in India, which occupies 1 st position in terms of area as well as 2 nd position in terms of production (Rohtas et al., 2016; Srujana and Zacharia, 2020) [45,53] . It is mainly cultivated under rainfed conditions (83%) during June to October, as well as remaining seventeen percent is grown under irrigated conditions in post-monsoon (Singh et al., 2014b) [50] . It is one of the few crops that can be cultivated even on marginal lands under low-input conditions (Chibarabada et al., 2020) [12] . Production and productivity of groundnut crop are the major indicators for enhancing income of groundnut farmers, and both are influenced by various factors (Mwalongo et al., 2020) [36] . The constraints in groundnut production adversely affect the productivity of crop. Understanding of these factors and their linkage with each other is essential. Many researchers across the country studied the constraints and factors influencing groundnut production in the country. The main reasons for low productivity in groundnut crop are nonavailability of quality seeds of new verities (Mwalongo et al., 2020 and Deva et al., 2023) [36,18] , lack of knowledge on soil fertility (Choudhary et al., 2011) [13] , non-adoption of insitu soil moisture conservation practices (Soni et al., 2016 and Mamatha et al., 2022) [51,33] , improper pest and disease management (Murthy et al., 2018) [35] , non-availability of suitable mechanization timely production technologies to combat with adverse climate changes (Srinivasa et al., 2022) [52] and operational problems (Behura et al., 2020) [10] . Study on these factors and their impact on groundnut production in near future through crop modelling is essential. Crop growth models use mathematical and empirical linkages to simulate growth as well as growth of crops under various environments (Rauff and Bello, 2015) [40] . These models act as decision-supporting tools in agricultural production. Modelling of groundnut production was done by (Jones et al., 2003) [29] using Decision Support System for Agrotechnology Transfer (DSSAT) and Agricultural Production Systems Simulator (APSIM) by Robertson et al., (2002) [44] . However, these models require huge amount of input data that is difficult to get under field conditions, which requires more time and resources (Corbeels et al., 2016; Jones et al., 2017) [15,28] . AquaCrop needs comparatively few input parameters (Umesh et al., 2022) [58] . This model is intended for end users, including farmers, policy officials, and students, in addition to scientists (Steduto et al., 2009) [55] . Aquacrop provides opportunity to simulate crop yield under various agroecological practices (Raes et al., 2009; Steduto et al., 2009) [39,55] . This model was utilized for prediction of crop yield for several crops, viz, maize (Heng et al., 2009, Umesh et al., 2022) [25,58] , cotton (Farahani et al., 2009) [22] , wheat (Andarzian et al., 2011) [5] and sorghum (Araya et al., 2016) [6] . Globally, Aquacrop model was used for very few oilseed crops, such as soybean (Adeboye et al., 2017) [4] and bambara groundnut (Karunaratne et al., 2011) [31] . Kadiyala et al., (2021) [30] reported that, there is -2.3 to 43.2 percent alteration in groundnut yields by 2050 throughout different areas in India when climate alone has been factored in. However, no detailed information is available at regional level, particularly in southern Indian part. The calibration as well as validation of AquaCrop helps to comprehend effect of various factors on groundnut production in arid as well as semi-arid areas of Indian sub-continent, particularly major groundnut growing states like Gujarat, Karnataka, Maharashtra and Andhra Pradesh (Chibarabada et al.) This validated model offers the opportunity to predict groundnut yield under different climate change situations at regional level. Yadgir is the new district in North-Eastern dry zone of Karnataka. This agro-ecological zone is a major groundnutproducing belt with an area of about 1.50lakh ha, production of 1.14lakh tonnes, as well as productivity of 742kg ha -1 (Biradar et al., 2024) [11] . Groundnut production in the district covers 40% of (43358 ha) and the productivity of groundnut is 17.5 -20 q ha -1, which is very low as compared to state average 20 -30 q ha - 1 (YDG, 2022-23). The major constraints for low productivity in groundnut yield in the district are non-availability of quality seeds of new verities, lack of knowledge on soil fertility, in-situ soil moisture conservation practices, pest and disease management, non-availability of timely production technologies to combat with adverse climate changes and operational problems (PLCP, NABARD, 2023). Hence, there is an urgent need to educate the groundnut farmers in the Yadgir district through demonstration of improved technologies in groundnut cultivation and which increases the productivity of the groundnut and increases the farm income for sustainable development of the small and marginal groundnut farmers. Understanding this, ICAR-Krishi Vigyan Kendra (KVK) conducted research on effect of climate change as well as different factors on groundnut yield through assessing improved technologies viz, new varieties, integrated nutrient management (INM), integrated pest & disease management (IPDM), sprinkler irrigation (SI), as well as mechanization at 15 locations in Yadgir district. Aquacrop model was validated and used for forecasting groundnut production under various climate change scenarios in the Yadgir district with the following objectives: Conducting field demonstration of improved technologies for their assessment, calibration, along validation of Aquacrop model for groundnut crop production.

Materials and Methods

This study assessed the improved technologies at 15 locations on field scale across the Yadgir district along with farmer's practices to study their effect on groundnut yield in near future through aquacrop model.

Description of the Study Area

Research has been carried out during kharif 2021 as well as 2022 and rabi 2021 & 2022 at Yadgir district in North Eastern Dry zone, Karnataka (India) by ICAR-Krishi Vigyan Kendra (KVK) Yadgir at 15 locations consist of ten small farmers (SF) & five marginal farmers (MF) supported by regional office, NABARD (Karnataka) for two years on the basis of different climatic condition and soil characteristics. Map of study area is presented in Fig 1.

Technologies assessed

The details of improved technologies assessed during study period are presented in Table 1 and these technologies are of package of practices for groundnut production for zone-II developed and released by University of Agricultural Sciences (UAS), Raichur (POP, UASR, 2024). The selected technologies were implemented in one acre each at 15 locations and compared with farmer practices for better understanding.

AquaCrop model description and input data

The present version of AquaCrop (7.1), the crop-water productivity model established by FAO, is now available with its standard user interface (GUI), and database has been utilized in current research. The model involves 6 input files for simulation: soil file, initial soil water conditions, crop file, climate file, irrigation file as well as field management file. The model provides the opportunity for user-specific. Crop file includes both non-conservative (user-specific parameters) and conservative parameters (that are location-invariant). Detailed information on AquaCrop model can be seen in (Feng et al., 2022) [23] .

Climate data

Crop parameters

Crop file consists of conservative as well as non-conservative parameters. In current research, values for conservative parameters had been obtained from (Chibarabada et al., 2020) [12] , who calibrated as well as validated AquaCrop for groundnut crop. During calibration of model, these values were adjusted using field scale data collected at 15 locations during kharif 2021. The maximum effective rooting depths at selected locations were estimated at harvesting stage. However, values for water extraction pattern were obtained from AquaCrop defaults files. The observations made during field study on assessment of technologies and farmers' practices during kharif & rabi of 2021 and 2022 were used to create the crop file (.CRO). Data on plant growth metrics, including plant height, root length, aboveground biomass, as well as leaf area index, are collected for the study using random sampling at regular intervals. Plant height, leaf area, as well as root depth observations had been made at intervals of 15 days from the date of seeding to the harvest stage. Days associated with flowering, germination, maximum root depth, maximum canopy, senescence, as well as maturity have also been noted. Parameters for groundnut crop's canopy decline coefficient, crop coefficient for transpiration at complete canopy cover, soil water depletion thresholds for stomatal conductance and leaf growth inhibition, as well as canopy senescence acceleration, had been accepted by (Chibarabada et al., 2020) [12] .

Irrigation and field management parameters

Irrigation file in AquaCrop model consists of information on rainfed and depth of irrigation applied. To investigate irrigation effect, two to three critical irrigations with 50 mm through pressurized sprinkler irrigation system were applied during long dryspells and no irrigation was applied under farmer's practices during study period. Field management comprises soil fertility management. In current research, recommended dose of fertilizer RDF) was applied, as well as 50% less RDF was applied under farmers' practices. Water productivity under supplemental irrigation and rainfed is computed utilizing below formula:

Water productivity (kg ha -1 mm -1 ) = --------( 1)

Calibration and validation of AquaCrop model

Model parameters are adjusted during calibration to bring simulated results up to a predefined level, often that of an observation (Abedinpour et al., 2012) [2] . In current research, AquaCrop model has been calibrated using pooled observed data from 15 locations in the study area during kharif season of 2021 and 2022 to simulate groundnut yield as well as water productivity. Objective of the calibration is to decrease prediction error between model-predicted as well as observed values through trial-as well as-error technique by taking one specific input variable as reference, as well as adjusting only those parameters (Diaa et al., 2021) [20] . Days to germination and complete maturity, days to senescence, maximum canopy cover, Canopy growth coefficient (CGC), as well as canopy decline coefficient (CDC), are influencing the groundnut crop canopy.

The trial-and-error method was used to minimize prediction error between field as well as simulated values. Values of crop parameters obtained during calibration of Aquacrop model are observed in table 2. Values obtained for CGC as well as CDC during calibration were 11.82% day -1 as well as 0.674% day -1 , respectively. Harvest index has been computed using economic yield and total biomass obtained under technology fields as 26% at harvest. The water productivity value of 16 g m -2 was determined using equation 1 and used for calibrating the model. Crop water stress category, factors about expansion stress, stomatal closure, as well as early senescence stress had been adopted from (Chibarabada et al., 2020) [12] , which are moderately tolerant, moderately sensitive and moderately tolerant respectively. These calibrated crop water stress function coefficients were used to simulate the groundnut yield under technologies and farmers' practices fields. [60] . The statistical indicators are widely used for assessing the model's performance (Diaa et al., 2021 and Feng et al., 2022) [20,23] . In current study, root mean square error (RMSE), coefficient of determination (R 2 ), prediction error (Pe), mean absolute error (MAE), as well as model efficiency (E) were utilized as per equations mentioned by (Umesh et al., 2022) [58] . The model's predictive capability is indicated by E and R 2 , whereas the degree of error accompanying model prediction is indicated by Pe, MAE, and RMSE (Abedinpour et al., 2012) [2] . [12]

Results and Discussion

The results of the improved technologies assessed at 15 selected farmer's field during kharif and rabi of 2021 and 2022 are presented and discussed in this section.

Supplemental irrigation and soil moisture distribution

The supplemental irrigation with 50 mm depth through pressurized sprinklers during 50 per cent soil moisture depletion at long dry spells of kharif 2021 & 2022 and rabi 2021 & 2022, and the details of soil moisture distribution are presented in Fig. 3 (a-d). The average soil moisture content ranged from 12.92 to 36.10 (%) under demo field and 8.7 to 15.20 (%) under farmer's practices during kharif 2021. During Rabi 2021, the soil moisture ranged from 13.24 to 33.50 (%) and 9.5 to 16.90 (%) under demo and farmers' fields, respectively. The soil moisture content of kharif season 2022 ranged from 10.1 to 25.60 (%) and 12.5 to 18.30 (%) under demo and farmers' fields, respectively. The same trend was observed during Rabi 2022, where the soil moisture ranged from 6.6 to 15.40 (%) and 10.71 to 30.60 (%) under demo and farmers' fields, respectively. The results clearly showed that there was good soil moisture distribution throughout the crop growth period during both the seasons of 2021 and 2022 under demonstrated fields, where fields were given supplemental irrigation through pressurized sprinklers. The high uniformity and application efficiencies enhanced soil moisture availability throughout crop period and saved 25% of irrigation water. The farmer's fields irrigated with surface irrigation were reported to have less moisture distribution due to uneven irrigation, runoff and seepage losses. Traditionally, farmers were irrigating through surface channels, which led to 30 per cent of irrigation loss through seepage and percolation losses (Abou Kheira, 2009) [1] . The irrigation efficiency of surface irrigation is merely 40 to 50 per cent and which can be increased up to 80 per cent by shifting to sprinkler irrigation (Dong et al., 2024) [21] . The water productivity of surface irrigation is very less as compared to sprinkler irrigation (Rojh et al., 2021) [46] . Farmers are educated on use of sprinkler irrigation for irrigation management during crop critical stages. The use of sprinkler irrigation would help to save water and increase the water productivity in groundnut production (Girehsingh and Mahindra, 2016 and Soni et al., 2016) [24,51] .

Groundnut Yield

Groundnut yield from technology demonstrated & farmers' practices were collected in one acre each and are compared to study the impact of improved technologies on groundnut production. The results of demonstrated and farmers' practices are shown in Table 3. Results clearly show that, adaptation of improved technologies in groundnut production significantly increased the groundnut production. The groundnut yield during kharif season was in the range of 22.53 to 31.55q ha -1 as well as 22.95 to 28.73q ha -1 under demonstration field during 2021 and 2022, respectively, as compared to 19.18 to 26.45q ha -1 and 19.35 to 23.63q ha -1 under farmers' practices. The groundnut yield during rabi season was in the range of 27.93 to 34.95q ha -1 as well as 28.40 to 37.45q ha -1 under demonstration field during 2021 and 2022, respectively, as compared to 24.25 to 29.73q ha - 1 as well as 24.65 to 32.40q ha -1 under farmers' practices. The lower groundnut yield in farmers' practices was due to use of low-yielding old varieties, poor soil nutrients, occurrence of dryspell and high pest and disease incidence during crop growth period. The introduction of high-yielding groundnut variety K-1812 (Deva et al., 2023) [18] with soil health card-based nutrient management (Jat et al., 2023) [27] . Precise irrigation water management has significantly increased groundnut yield under demonstration field (Waseem et al., 2017) [59] . The use of biocontrolling agents such as metarighium and trichoderma (Sharma et al., 2017) [47] under integrated pest and disease management has reduced pest and disease incidence, thereby increasing groundnut yield under demonstration field (Sharma et al., 2012; Srujana and Zacharia, 2020 and Arunasri et al., 2021) [48, 53, 8] . The increase in soil fertility through application of micro nutrients such as boron, zinc and magnesium sulphate has helped to increase the groundnut yield, and alike outcomes have also been noticed by (Nagaraj et al., 2018 and Subbiah, G. (2023) [37,56] . The two seasons' average groundnut yield under technologies demonstrated field of kharif 2021 and 2022 ranged from 22.95 to 28.84 q ha -1 as compared to 19.79 to 24.11 q ha -1 under farmers' practices. The two-year average groundnut yield during rabi season was 28.76 to 35.24q ha -1 under technology demonstration field as compared to 24.28 to 29.93q ha -1 under farmers' practices. Average groundnut yield of 15 selected farmers under technologies demonstrated field was 25.91q ha -1, as compared to 21.83 q ha -1 under farmers' practices during kharif season. The average groundnut yield of 15 selected farmers under technologies demonstrated field was 31.34q ha -1 as compared to 26.53q ha -1 under farmers' practices during rabi season. The improved technologies positively increased groundnut yield by 18.68 (%) and 18.13 (%) during kharif and rabi seasons, respectively, as compared to farmers' practices. Statistical analysis indicates that there has been significant yield difference between demonstration as well as farmers' plots during both seasons (Fig 4). The increase of groundnut production under technologies demonstrated was because of employing high yielding types, soil health based nutrient management, use of bio controlling agents for integrated pest and disease management, soil moisture-based irrigation application and use of groundnut digger cum elevator during groundnut harvest. Improved technologies not only increased groundnut production but also saved the crop inputs such as seeds, fertilizers, chemicals, irrigation water and labour, along with time, and similar outcomes have also been stated by Sunandini, G., & Devi, I. S. (2020) [57] and Srinivasa et al., (2022) [52] . Similar findings have also been obtained by ( [16,47,32,49] Using micronutrients for groundnut production. New groundnut variety K-1812 cultivated in Andhra Pradesh yielded 15 to 25% higher yield than traditional varieties (Deva et al., 2023) [18] . The adoption of sprinkler irrigation in groundnut production has increased the groundnut yield, water utilize efficacy (8 kgha -1 mm -1 ) and saved 25% irrigation water in the present study, and same results were also recorded by (Gireshsingh and [34,59,21,46] . During calibration, the values of statistical measures such as RMSE, MAE and E were 0.08, 0.87 and 0.96, respectively, for groundnut yield. The difference between simulated and observed groundnut yield was 9.54%, which is in acceptable range. The RMSE, MAE and E for groundnut biomass were 0.63, 0.70 and 0.88, respectively. The difference between simulated as well as observed groundnut biomass was 18.70%. The model prediction error between simulated as well as observed data was minimized by model fine-tuning. The values of plant growth parameters, temperature range, as well as harvesting index are shown in Table 2. Days to canopy decline (23), canopy growth coefficient (12.2) and canopy growth decline (0.683) were adjusted for during calibration to reduce model prediction error and biomass prediction. As per Chibarabada et al., (2020) [12] , biomass is utilized to simulate yield employing HI. The harvesting index (HI) and water productivity (WP) adjusted for groundnut yield prediction and values for the same were 24 and 15, respectively. The prediction error for biomass was higher than the yield, this is due to model underestimating the biomass. Data on groundnut yield and biomass of 2022 is utilized for validation of aquacrop model, and model showed that there was good agreement between simulated as well as observed values.

Difference between simulated as well as observed groundnut yield as well as biomass was 14.13% and 15.80%, respectively.

The RMSE, MAE and E for groundnut yield were 0.07, 0.78, as well as 0.93, respectively. RMSE, MAE and E for groundnut biomass were 0.68, 0.68 and 0.83, respectively during validation (Table 04). Performance of aquacrop model during calibration as well as validation is presented in Figures 5 and6, respectively. Correlation (R 2 ) between simulated as well as observed values for groundnut yield and biomass was 0.92 and 0.97 under calibration. Similarly, under validation, correlation (R 2 ) between simulated as well as observed values for groundnut yield as well as biomass was 0.93 and 0.97. Many researchers used aquacrop model for its performance on different major crops viz, maize (Heng et al., 2009, Umesh et al., 2022) [25,58] , cotton (Farahani et al., 2009) [22] , wheat (Andarzian et al., 2011) [5] and sorghum (Araya et al., 2016) and reported that, aquacrop model performed well for all the crops. Chibarabada et al., (2020) [12] utilized aquacrop model simulating groundnut yield under irrigation, as well as rainfed conditions, along with reported model performed well, with model efficiency more than 95%, and all the statistical parameters were within the acceptable range. In the present study, model output and statistical indicators showed good fit, as well as can be inferred that, aquacrop model is suitable for simulating the groundnut yield as well as biomass of groundnut crop under different agronomic practices and irrigation levels.

Conclusion

The improved technologies, such high high-yielding variety K-1812, soil health-based nutrient management, IPDM and supplemental irrigation through sprinkler irrigation, positively increased the groundnut yield in the study area. The average groundnut yield under technologies demonstrated field was 25.91 q ha -1 as compared to 21.83q ha -1 under farmers' practices during kharif season. The average groundnut yield under technologies demonstrated field was 31.34q ha -1 as compared to 26.53q ha -1 under farmers' practices during rabi season. The improved technologies positively increased groundnut yield by 18.68 (%) and 18.13 (%) during kharif and rabi seasons, respectively, as compared to farmers' practices and helped to minimize the cost of groundnut production. The BC ratio increased to 3.77 compared to 1.58 in farmer's field. AquaCrop model has been calibrated for simulation of groundnut yield as well as biomass for demonstration plot, as well as prediction error statistics were 0.96 <E< 0.88%, 0.08 <RMSE< 0.63q ha -1, as well as 0.87 <MAE< 0.70q ha -1 . During validation, model performed well with model efficacy of 93% as well as 83% for groundnut pod yield as well as biomass, respectively and are within acceptable limits. Aquacrop model performed well, and it can be used for study on effect of climate change as well as agronomical practices on groundnut yield as well as biomass in study area.

Acknowledgement

Acknowledgement: University of Agricultural Sciences, Raichur (Karnataka) is expressing heartfelt gratitude to National Bank for Agriculture and Rural Development (NABARD), Regional Office, Bengaluru, for financial assistance provided for successful completion of this study.

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