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Article

Assessing Soybean Yield Potential and Yield Gap in Different Agroecological Regions of India Using the DSSAT Model

by
Raghavendra Nargund
1,
Virender S. Bhatia
1,
Nishant K. Sinha
2,*,
Monoranjan Mohanty
2,
Somasundaram Jayaraman
3,
Yash P. Dang
4,*,
Vennampally Nataraj
1,
Darren Drewry
5 and
Ram C. Dalal
4
1
ICAR-Indian Institute of Soybean Research, Indore 452001, India
2
ICAR-Indian Institute of Soil Science, Bhopal 462038, India
3
ICAR-IISWC, Research Centre, Udhagamandalam 643006, India
4
School of Agriculture and Food Sustainability, The University of Queensland, Brisbane, QLD 4072, Australia
5
Department of Food, Agricultural and Biological Engineering, The Ohio State University, Columbus, OH 43210, USA
*
Authors to whom correspondence should be addressed.
Agronomy 2024, 14(9), 1929; https://doi.org/10.3390/agronomy14091929 (registering DOI)
Submission received: 18 June 2024 / Revised: 23 July 2024 / Accepted: 25 July 2024 / Published: 28 August 2024
(This article belongs to the Section Agroecology Innovation: Achieving System Resilience)
Browse Figures
Versions Notes

Abstract

:
The study used the DSSAT model to assess potential soybean yields in different regions of India and validated it under diverse agroecological conditions. The average simulated yield under irrigated conditions was 3794 kg ha−1 relative to the simulated average rainfed yield of 2446 kg ha−1, showing a 35.52% reduction in grain yield due to adverse moisture conditions under rainfed conditions. Relative to simulated yield, the average observed (actual) rainfed yield across 43 districts of India was 1025 kg ha−1, which was 2769 and 1421 kg ha−1 lower than irrigated and rainfed potential yield, respectively. A significant positive correlation was observed between simulated water non-limited yield and solar radiation (R2 = 0.55, p ≤ 0.05). The simulated rainfed grain yield (R2 = 0.66, p ≤ 0.05) had a significant, positive, and curvilinear relationship with growing season rainfall. On the other hand, the actual yield (R2 = 0.008) showed a non-significant relationship with mean crop seasonal rainfall across locations. The gap between simulated yield under irrigated and rainfed conditions is huge at locations with low seasonal rainfall and narrows with increasing rainfall. In addition, the gap between actual yield and simulated yield under rainfed conditions was larger, even in high seasonal rainfall areas. The yield gap under rainfed conditions is due to the non-adoption of improved crop management practices and could be reduced with proper interventions. This includes adapting drought-resistant varieties, conserving rainwater, changing land configuration, and adopting waterlogging-tolerant varieties using improved technology to reduce the soybean yield gap.

1. Introduction

A rainfed agroecosystem is the mainstay of most developing countries [1]. In India, 56% of the gross cropped area is under rainfed conditions but contributes 40% of food production [2]. However, the significance of rainfed agriculture can be judged by the fact that 85% of food legumes and 72% of oilseeds are produced in rainfed regions of the country [3]. The expansion of cultivable land and water availability for crop production to meet the food and fodder requirements of an ever-increasing population are major concerns. Hence, rainfed agriculture needs to be prioritized, where water is the main constraint on agricultural productivity. Rainfed agriculture relies entirely on seasonal rainfall, which brings many uncertainties. Therefore, there is a need to introduce and expand the cultivation of crops that can adapt well to complex and diverse environments. Soybean (Glycine max L. Mirr.) is a major crop grown during the rainy season and is highly adaptable to various climates [4]. It is cultivated in regions extending from 15° to 25° N, covering 98% of the total cultivated area under rainfed conditions in this zone [5].
Currently, soybean area and production have been increasing by 8.8% and 11.3% annually, respectively. However, crop productivity only increases by 2.8% per year in India [2]. The soybean crop faces various climatic aberrant situations like recurring floods, drought, high soil temperature, cloud cover with reduced light intensity, soil degradation, and poor management practices. These adverse situations have led to an average yield of less than 1.2 t ha−1 in India [6]. Various studies across the globe have shown that the assessment of potential yield and yield gaps could help identify yield-limiting factors and develop suitable strategies to improve crop productivity [4,7,8,9,10]. Determining the yield of different agroecosystems and quantifying the yield gap through field experiments may require many years of experimentation. Besides being time-consuming and expensive, the total elimination of factors other than those governing growth and development and their interaction with a given production level may not be possible under field conditions.
Process-based dynamic crop growth simulation models can predict crop growth, development, and yield by integrating knowledge of underlying processes and the interaction of different components of crop production [11]. Simulation models are increasingly used in yield gap analysis by assessing potential yields (water non-limited, water-limited, or nutrient-limited) for different agroecosystems [4,8,10]. However, before models are used, they need to be tested thoroughly and validated across the region to establish their credibility [11]. The present study utilized the CROPGRO soybean model in the DSSAT (Decision Support System for Agro-technology Transfer) to evaluate potential yield and yield gaps across diverse climate and management conditions in India. The DSSAT models have been widely evaluated across a wide range of soil and climatic conditions, including temperate, tropical, and subtropical regions. The DSSAT performed well across major soybean-growing areas in India by precisely predicting crop phenology, biomass, and seed yield. In India, Bhatia et al. [4] conducted a soybean yield gap study using the CROPGRO model of DSSAT with the available crop, weather, and soil data up to the year 2003. Since then, the soybean crop production area has grown in India, covering around 3.66% of the country’s total geographical area [9]. Identifying the most limiting crop, soil, weather, and management factors for the current crop yield and best practices to close the yield gap is imperative. The yield gap analysis study also effectively prioritizes research, development, and intervention for managing yield gaps. The current study assessed the DSSAT model’s ability to simulate soybean growth, development, and yield in various agroecological regions of India. The study’s main focus was to use the model to predict potential yield and yield gaps in rainfed and irrigated conditions across different soybean-growing areas in India. This will help guide efforts to achieve higher yields in the near term.

2. Materials and Methods

2.1. Crop Model Descriptions

DSSAT is a collection of independent programs that operate together with crop simulation models at its center [11,12,13]. Databases describe the weather, soil, experimental conditions, measurements, and genotype information for applying the models to different situations [13]. The present study utilized DSSAT v4.7.5 CROPGRO and necessitated input data on soil characteristics, crop growth, and development, as well as daily weather (including maximum and minimum temperatures, solar radiation, and rainfall), for calibration and validation under different environmental conditions. The model simulates crop growth and development daily. The detailed description of the model and methodology followed in conducting experimental analysis in the CROPGRO-DSSAT model were elucidated in [4,14].

2.2. Experimental Details and Data Collection

The soybean variety JS-335 is extensively grown in various regions of India and is widely regarded as the most popular cultivar [15]. For model calibration (initial one year) and validation (following two years), field experiments of soybean cultivars JS-335 were carried out in a randomized complete block design with three replications during 2012–2014 at the ICAR-Indian Institute of Soybean Research, Indore (22.7° N, 75.8° E), India. The cultivars were planted on July 6 for three consecutive years (2012–2014). Before sowing, the seeds were treated with the recommended fungicide and inoculated with the Bradyrhizobium strain. At the time of planting, the recommended doses of fertilizer N, P, and K were applied at 20:26:17 kg ha−1. Plant populations of 45 m2 were maintained with 45 cm of row spacing. Further, all improved soybean agronomic management practices were followed as per the soybean extension bulletin [16]. Data was collected for model calibration and validation following the standard procedure outlined by [12]. The data collected from field experiments include crop growth and development, leaf area, above-ground biomass, days to flowering, days to maturity, grain yield and biomass yield, crop management, daily weather conditions, and other soil-related parameters. The model evaluation involved extensive data from numerous experiments conducted between 2003 and 2017 with soybean cultivars JS 335, JS 95–60, and JS 97–52, encompassing a wide range of seasons and management practices at various locations under the All India Coordinated Research Project on Soybean (AICRPS) (Figure S1).

2.3. Model Calibration and Validation

The genetic coefficients for cultivar JS 335 were estimated by repeatedly simulating crop phenology, growth, and yield until a close match was achieved between the observed and simulated results. The genetic coefficients of cultivar JS-335 are presented in Table 1. These coefficients were then used in the soybean yield gap analysis for India. The depth-wise soil information such as texture (sand, silt, and clay, %), bulk density (g cm−3), field capacity or drainage upper limit (cm3 cm−3), permanent wilting point or drainage lower limit (cm3 cm−3), saturated soil water content (cm3 cm−3), saturated hydraulic conductivity (cm h−1), organic carbon (%), pH, etc. were estimated from the field experiment. These estimated soil characteristics were further modified to make them more specific for the experimental site by following the procedure described by [17]. The soil of the experimental site belongs to Vertisols, with an extractable water capacity of 229 mm. The estimated values of U, CN2, and SWCON were 6 mm, 70 mm, and 0.50 mm, respectively. The assumed SLPF value was 1. The soil fertility was not a constraint for crop growth. To assess the performance of the CROPGRO soybean model, validation was carried out with the data generated from field experiments as well as from many diverse experiments under AICRPS [18]. The model’s performance was assessed based on the agreement between simulated and observed data using the standard statistical procedures described below.

2.4. Statistical Evaluation of Models

Model performance and model accuracy were assessed using statistical indicators such as correlation coefficient (r), coefficient of determination (R2), root mean square error (RMSE), and the Willmot index (d-value) [19,20]. The observed and simulated values of leaf area index, above-ground biomass, days to flowering, days to maturity, grain yield, and biological yield were used to calculate the statistics. The RMSE is the square root of the variance of the residuals. It measures how well the model fits the data and how closely the observed data points align with the model’s predicted values. Lower RMSE values indicate a better fit. However, Willmott’s d value is a better indicator of model performance, particularly relative to the 1:1 line. The correlation coefficient (r) or the coefficient of determination (R2) value close to 1 indicates better prediction, while a d value of zero indicates no predictive skill. Correlation analysis was performed using the package “PerformanceAnalytics” in the R programming environment v4.0.1 [21].

2.5. Potential and Actual Yield of Soybean

For potential yield simulation, the study area was selected from a latitudinal belt of 15° to 24° N consisting of major soybean-growing states (Madhya Pradesh, Maharashtra, Rajasthan, Karnataka, and Telangana) of India. It covers approximately 98% of the soybean-growing area in India. Long-term simulations were carried out to estimate the potential yield for 43 locations under rainfed and irrigated conditions (Table 2 and Figure S1). The simulations were conducted for about 22 years based on the availability of weather and soil profile data. All the locations selected for simulation of potential yields have Vertisols and their associated soils, representing major soils on which soybean is mainly cultivated in India. The soil profile data for each location were collected from the ICAR-National Bureau of Soil Survey and Land Use Planning, Nagpur [22]. The experimental analysis component in the CROPGRO model under DSSAT V4.7.5 was used for long-term simulation of potential yield under rainfed and irrigated conditions in all 43 locations by putting a sowing window period of 15 June to 30 July, spanning most of the sowing dates throughout India [23]. The model had the water stress option turned on for rainfed conditions and turned off for irrigated conditions. Other management practices, such as nutrient, pest, and disease management options, were kept turned off, except for nutrients, which were applied as per the recommended dose. The crop was simulated to be sown when the soil moisture content in the top 30 cm of soil reached 40% of its extractable water-holding capacity during the sowing window. A plant population of 33 plants per square meter at 30 cm row spacing was used throughout the simulation study. A soil fertility factor (SLPF) of 1 was applied to simulate crop yield without soil fertility limitations.
The district-level yield was utilized as the actual yield to compare with simulated yields and assess the soybean yield gap in India. The district yield data was sourced from the Directorate of Economics and Statistics, Government of India, covering the period from 1997 to 2018 [24].

3. Results and Discussion

3.1. Model Calibration and Validation

A close agreement between simulated and observed LAI and above-ground biomass was observed in soybean cultivar JS 335 with an R2 value > 0.80, d-stat near 1, and a low RMSE value (Figure 1a–d). Further, the model was validated with the dataset from the Indore location using experimental field data on grain and biomass yield, as shown in Table 3. A reasonably good agreement was found between simulated and observed grain and biomass yield, with RMSE values of 1057 and 965 kg ha−1 for grain yield and 892 and 786 kg ha−1 for biomass yield under rainfed and irrigated conditions, respectively. The well-calibrated and validated CROPGRO soybean model was further evaluated using experimental data collected from 14 locations of the AICRP on Soybean. The results showed that the model reasonably predicted various crop parameters. The root mean square error (RMSE), R2, and d values for predicting days to flowering and days to maturity were 2.13, 0.58, 0.81, and 2.78, 0.71, and 0.91 days, respectively (Figure 2a,b). Similarly, for grain yield, RMSE, R2, and d values were 311 kg ha−1, 0.83, and 0.95 kg ha−1, respectively (Figure 2c). The statistics demonstrated a strong agreement between simulated and observed values of days to flowering, days to maturity, and grain yield across a diverse set of experiments conducted in India under AICRP on soybean.

3.2. Simulated Yield under Irrigated Conditions

The potential soybean yield under irrigated conditions demonstrates more consistent spatial and temporal patterns than rainfed potential yield (Table 4). The average simulated yield under irrigation conditions over 43 locations across India was reported at 3794 kg ha−1, with a coefficient of variation of 17.7%. Among different locations, the mean simulated irrigated yield ranged from 3104 kg/ha (Junagarh) to 4292 kg/ha (Parbhani). The coefficients of variation for temporal variability ranged from 3.79% to 20.0% across 43 simulated locations (Table 4). The average simulated minimum irrigated yield was 2947 kg/ha, which was 32.04% less than the average maximum simulated yield under irrigated conditions (4337 kg/ha). The yield obtained under irrigated conditions was primarily influenced by climatic factors such as solar radiation and temperature (Table 5). Further [4] found that moderately cooler temperatures under irrigated conditions could result in higher soybean yields.

3.3. Simulated Yield under Rainfed Condition

The 15 June to 30 July sowing window was selected for simulating soybean yield in water-limited conditions across India. The average simulated rainfed potential yield of soybean was 2446 kg/ha with a coefficient of variation of 33.4% (Table 4). Across 43 locations in India, the rainfed potential yield, limited by water availability, ranged from 419 kg/ha in Bellary to 3663 kg/ha in Pantnagar. The temporal coefficient of variation varied from 6.73% to 107.9% across these 43 locations. The average minimum yield of the 43 locations was 1010 kg/ha, which was 72.01% lower than the maximum simulated rainfed yield of 3609 kg/ha. At this level, soybean productivity is mainly governed by rainwater availability and distribution. Spatial and temporal variation in simulated rainfed yield was greater than in simulated irrigated (water non-limited) yield. This significant variability in simulated rainfed yield highlights the crucial impact of rainfall amount and distribution on soybean productivity in rainfed agricultural systems in India. In specific locations such as Amravati, Rajgarh, Hoshangabad, Dharwad, Gulbarga, Jhabua, Bellary, Bijapur, Coimbatore, Junagadh, Ludhiana, and New Delhi, crop failure was observed during the simulation period (Table 4). Simulated crop failure in the particular years mentioned in Table 4 was mainly attributed to the low total rainfall received during cropping seasons (Table 6 and Table 7). In addition, the failure of crops under AICRP’s experimental stations, FLDs under farmer’s fields, and actual yield in respective districts during respective simulation years were also presented in Table 6. However, soybean yield was maintained in Amravati, Rajgarh, Dharwad, and Gulbarga districts, even though a meager amount of total seasonal rainfall was received, attributed to additional irrigation provided by the farmers during critical periods. Soil type, in addition to rainfall, played a critical role in soybean production. The simulation results clearly showed that different soil series at the same location led to significantly different minimum, maximum, and mean yields.

3.4. Actual Yield of Soybean

Actual (observed) yields are much lower than simulated yields. The actual yields ranged from 756 kg ha−1 (Dharwad) to 2018 kg ha−1 (Pune), with an average value of 1025 kg ha−1 as compared to simulated irrigated (3794 kg ha−1) and rainfed (2446 kg ha−1) yields (Table 4). Actual yield showed a poor relationship with mean crop seasonal rainfall (R2 = 0.008) at 37 selected locations across India (Figure 3). The yield data variations around the fitted regression line demonstrate the impact of rainfall distribution and soil properties on soybean yield. This explains the differing yields in different locations. When compared to simulated yield under rainfed conditions, actual yield only slightly increases with more rainfall. This could be attributed to variations in farmers’ crop management practices and complex environmental and soil factors. In addition, actual yield only marginally increases with up to approximately ~742 mm of rainfall. There was no substantial change in yield in the rainfall range between approximately ~742 and ~964 mm. Any increase in rainfall beyond ~964 mm negatively impacts actual yield due to poor drainage and waterlogging. Hence, it is crucial to implement proper soil and water conservation management strategies to improve yield under such conditions [6,25,26]. The yield response between 310 mm and 950 mm brings out the importance of factors other than water availability in farmers’ fields, limiting the realization of the rainfed yield potential of the soybean crop. In addition to suboptimal availability of water, improved crop management practices such as imbalanced nutrient use, inappropriate sowing time, poor crop stand, heavy weed infestation, and pest and disease load further limit the productivity of soybean under rainfed conditions in Indi [4,27,28].

3.5. Climate Variability Effects on Soybean Yield

The effect of seasonal climatic variability on soybean grain yield in India is presented in Figure 4, Figure 5 and Figure 6. The long-term rainfall, solar radiation, and minimum and maximum temperatures during the cropping season at different locations ranged from 383 to 1309 mm, 16.4 to 21.2 MJ m−2 day−1, 22.1 to 25 °C, and 26.5 to 36.1 °C, respectively (Table 5 and Table 7). A positive and significant correlation was observed between simulated rainfed grain yield and seasonal rainfall (r = 0.76***). Similarly, solar radiation (r = 0.73***) had a significant, positive correlation with simulated grain yield under irrigated conditions. Soybean is grown mainly as a rainfed crop in India, and rainfall and solar radiation fluctuations can be observed to have a strong influence on monsoon activity. The current study showed that there was greater variability in seasonal rainfall and solar radiation during the cropping period compared to the variability in minimum and maximum temperatures. It also suggested that achieving a high soybean yield is possible with moderate to cooler temperatures, higher solar radiation, and rainfall ranging from 700 to 900 mm, with uniform distribution throughout the cropping season, as also reported by [4]. However, [29] also reported that even an increase in temperature up to (18–30 °C) during the grain filling stage has a positive effect on soybean grain yield in cooler locations. Our study also reported that the mean simulated yield under irrigated conditions was significantly associated with the maximum temperature (r = 0.35*). Similar results were also reported by [30]. The maximum temperature above 35 °C causes heat stress, which harms soybean flowering, pod set, and yield [31]. Most locations in the present investigation had an average maximum temperature of less than 35 °C. The seasonal minimum temperature did not have a significant association with simulated yield (r = −0.18) under irrigated conditions.
It is suggested that the variation in simulated yield across different locations in India is mainly due to differences in rainfall, solar radiation, and maximum temperature. Additionally, there was no significant relationship between the actual observed yield and solar radiation or the minimum and maximum temperature across 43 locations. The seasonal mean rainfall also did not show a significant correlation with the actual yield. This indicates that factors such as improper management practices, biotic stresses, and socioeconomic issues are the primary reasons for low soybean yield under farmers’ condition rather than climatic parameters, as reported by [9].

3.6. Yield Gaps of Soybean

Long-term simulations of the CROPGRO-Soybean model across 43 locations under irrigated rainfed conditions revealed a high yield potential for the soybean crop. The average actual yield observed across 43 locations was 1025 kg ha−1, which was 2769 and 1421 kg ha−1 less than the average simulated potential yield under irrigated and rainfed conditions, respectively. Thus, a 72.98% and 58.09% reduction in actual yield was observed compared to irrigated and rainfed conditions, respectively. The yield gap (irrigated potential yield minus the actual observed yield) ranged from 1716 kg ha−1 to 3237 kg ha−1, with an average yield of 2775 kg ha−1 (Table 4), and was not affected by the quantity of rainfall received across the 43 locations (Figure 3). The yield loss due to suboptimal availability of water was 1348 kg ha−1 (Table 4) and varied from location to location (122 to 3395 kg ha−1) depending on the magnitude of the rainfall received. The yield gap was higher at locations where low rainfall was received and narrowed with increased rainfall (Figure 3). At 900 mm of rainfall, the yield gap caused by water deficiency was almost the same as other factors limiting crop yield. Hence, water stress appears to be the main reason for yield reductions of up to 900 mm of rainfall during the growing season. Adaptation of better soil and water conservation measures (broad bed furrow) and drought-tolerant varieties like JS 97–52, NRC 7, and NRC 136 [6] may improve the soybean yield in these areas. The gaps between actual and rainfed simulated yield ranged from 583 to 2546 kg ha−1, with an average of 1433 kg ha−1, and were narrow at locations with low rainfall and increased as the quantity of rainfall increased in different locations. The yield gap (rainfed simulated yield minus actual yield) reflects the actual yield gap under rainfed conditions. Further, this actual yield gap is mainly due to the non-adoption of improved management practices such as better varieties, balanced nutrient application, timely plant protection measures, timely planting, crop geometry, etc. [4]. Large surface water runoff (Table 7), which on average accounted for about 25% of the seasonal average total rainfall received across 43 locations, indicated that efficient use of water through the adoption of improved water management practices such as watershed, farm pond, conservation tillage practices, residue recycling, and mulching could help to reduce the soybean yield gap across low and high rainfall areas [6,32,33,34]. All three observations (i.e., district yield, experimental yield under AICRPS, and frontline demonstration (FLDs) yield) were available for only seven of the 43 districts; hence, were taken for estimation of soybean yield gap I (difference between potential and achievable yield) and yield gap II (difference between achievable and average farmer yield). Further, these seven districts represent India’s five major soybean-growing states: Madhya Pradesh, Maharashtra, Rajasthan, Karnataka, and Chhattisgarh (Table 8). Across the locations, the average yield gap I (YG I) was 247 kg ha−1 and ranged from −215 (Indore, Madhya Pradesh) to 1324 kg ha−1 (Pune, Maharashtra). The average yield gap II (YG II) was 564 kg ha−1 and ranged from −369 (Pune, Maharashtra) to 1156 kg ha−1 (Dharwad, Karnataka). The high values of the coefficient of variation were recorded for YG I (219%) and YG II (86.0%), indicating a different level of yield gaps across locations in India. High variations in YG II across locations indicated the varying levels of adoption of improved technologies and management practices, including supplementary irrigation at pod filling. The earlier studies showed that a 50% reduction in rainfall during August or early September resulted in a reduction in seed yield [35]. Further, supplemental irrigation at the pod-filling stage improved LAI, biomass, and seed yield by 12–14%. More than 500 kg ha−1 YG II was observed in all locations except Pune, Kota, and Amaravati. It indicates considerable scope to improve the productivity level of soybean in India with proper management practices. Further, YG II formed a significant part of the total yield gap, and there is a need to scale up the improved crop production technologies from on-farm demonstrations at farmer’s fields across India’s different locations. A similar result was also reported by [36]. A total of 13,318 frontline demonstrations (FLDs) were conducted across India from 1997 to 2018. The average FLD yield (rainfed achievable yield) was about 1740 kg ha−1 [18], which is against the DSSAT model simulated yield of 2446 kg ha−1 under rainfed conditions. The reported average yield gap between potential rainfed yield and national average yield is about 1025 kg ha−1 compared to 1433 kg ha−1 obtained between simulated rainfed yield and actual yield/district yield in the current study. The close values of rainfed potential and yield gap of soybean obtained in on-farm trials and through simulation studies indicated that the CROPGRO soybean simulation model could also be useful in quantifying potential yields and yield gaps. These results also align with the earlier results of [4].
In addition, the model can forecast crop growth, development, and yield by utilizing a system approach that spans various climate conditions and management techniques. This makes it well-suited for identifying the obstacles that impede soybean productivity in different regions across India and at the national level.

4. Conclusions

The CROPGRO soybean model successfully predicts soybean crop growth, development, and grain yield under different conditions. The average simulated yield was 3794 kg ha−1 under irrigated conditions and 2446 kg ha−1 under rainfed conditions, indicating a 35.52% reduction in yield. Actual yield was around 1025 kg ha−1, which was 2769 kg ha−1 and 1421 kg ha−1 less than simulated soybean yield under irrigated and rainfed conditions, respectively. Across 43 locations in India, rainfall (r = 0.76***) was significantly and positively correlated with simulated yield under rainfed conditions. Solar radiation (0.73***) and maximum temperature (0.35*) under irrigated conditions had the strongest positive and significant correlations with soybean simulated yield. The gap between simulated irrigated yield and rainfed yield was very large at locations with low crop seasonal rainfall. It narrowed at locations with an increased quantity of seasonal rainfall. On the other hand, the gap between actual and rainfed simulated yield was narrow at locations with low rainfall and increased as the quantity of rainfall increased among different locations. The yield gap (rainfed simulated yield minus actual yield) reflects the actual yield gap in the rainfed ecosystem. Across different locations in India, the average YG I (difference between potential and achievable yield) was 247 kg ha−1, and the average YG II (difference between achievable and average farmer yield) was 564 kg ha−1. Further, high variations in YG II across locations were observed and indicated varying levels of adoption of improved technologies and management practices. More than 500 kg ha−1 YG II was observed in all locations except Pune, Kota, and Amaravati. The results suggest that there is a significant opportunity to enhance soybean productivity in India through targeted interventions. Furthermore, YG II accounted for a significant portion of the overall yield gap, underscoring the importance of implementing advanced crop production technologies demonstrated on farmers’ fields in various locations across India.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy14091929/s1, Figure S1: Locations selected for simulation study (AMR: Amravati, Betul: BET, Dhar: DHA, Indore: IND, Nagpur: NAG, Rajgarh: RAJ, Ratlam: RAT, Sagar: SAG, Sehore: SHE, Shajapur: SHA, Ujjain: UJJ, Vidisha: VID, Akola: AKO, Bhopal: BHO, Guna: GUN, Hosangabad: HOS, Kota: KOT, Nanded: NAN, Neemuch: NEE, Parbhani: PAR, Wardha: WAR, Belagavi: BEL, Dharwad: DHA, Gulbarga: GUL, Jabalpur: JAB, Jhabua: JHA, Anantpur: ANN, Bangalore: BAN, Bellary: BEL, Vijayapura: VIJ, Coimbatore: COI, Faizabad: FAI, Hissar: HIS, Hyderabad: HYD, Junagarh: JUN, Kanpur: KANP, Karnool: KAR, Ludhiana: LUD, New Delhi: NDL, Pantnagar: PAN, Pune: PUN, Raichur: RAIC, Raipur: RAIP).

Author Contributions

Conceptualization, R.N. and V.S.B.; methodology, R.N.; software, R.N. and N.K.S.; validation, R.N., N.K.S. and M.M.; formal analysis, R.N. and V.N.; investigation, R.N. and V.N.; resources, R.N., N.K.S. and S.J.; data curation, R.N. and S.J.; writing—R.N. and N.K.S., writing—review and editing, D.D., Y.P.D. and R.C.D.; visualization, R.N. and V.N.; supervision, V.S.B. and M.M.; project administration, V.S.B. and M.M.; funding acquisition, Y.P.D. and R.C.D. All authors have read and agreed to the published version of the manuscript.

Funding

IITM Pune: Ministry of Earth Science, Govt. of India funded the project.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Materials, further inquiries can be directed to the corresponding authors.

Acknowledgments

The authors sincerely thank IITM Pune, Ministry of Earth Science, Govt. of India, for their advice and support towards funding and conduct of this research.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Comparison of simulated (blue lines) and observed values (red lines) of leaf area index and above-ground biomass of soybean cultivar JS 335 under irrigated (a,b) and rainfed (c,d) conditions during the calibration process from a field experiment conducted at Indore. R2, coefficient of determination; d, Willmott index of agreement [20], ranging from 0 to 1, 1 being perfect agreement; RMSE, root mean error square.
Figure 1. Comparison of simulated (blue lines) and observed values (red lines) of leaf area index and above-ground biomass of soybean cultivar JS 335 under irrigated (a,b) and rainfed (c,d) conditions during the calibration process from a field experiment conducted at Indore. R2, coefficient of determination; d, Willmott index of agreement [20], ranging from 0 to 1, 1 being perfect agreement; RMSE, root mean error square.
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Figure 2. Comparison of simulated and observed (a) days to flowering, (b) days to maturity, and (c) grain yield at harvest of soybean cultivar JS 335 using AICRP experimental data sets. d, Willmott index of agreement [20], ranging from 0 to 1, 1 being perfect agreement; RMSE, root mean error square.
Figure 2. Comparison of simulated and observed (a) days to flowering, (b) days to maturity, and (c) grain yield at harvest of soybean cultivar JS 335 using AICRP experimental data sets. d, Willmott index of agreement [20], ranging from 0 to 1, 1 being perfect agreement; RMSE, root mean error square.
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Figure 3. Association of long-term mean simulated water non-limiting potential yield (●), mean simulated water-limited potential yield (◊), and actual yield (∆) with mean crop seasonal rainfall of selected locations across India. (a) yield gap between simulated water non-limiting and water-limiting yield, (b) yield gap between simulated water-limiting and actual yield, and (c) yield gap between simulated water non-limiting and actual yield or total yield gap.
Figure 3. Association of long-term mean simulated water non-limiting potential yield (●), mean simulated water-limited potential yield (◊), and actual yield (∆) with mean crop seasonal rainfall of selected locations across India. (a) yield gap between simulated water non-limiting and water-limiting yield, (b) yield gap between simulated water-limiting and actual yield, and (c) yield gap between simulated water non-limiting and actual yield or total yield gap.
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Figure 4. Association of long-term mean simulated water non-limiting potential yield with mean crop seasonal solar radiation among selected locations across India.
Figure 4. Association of long-term mean simulated water non-limiting potential yield with mean crop seasonal solar radiation among selected locations across India.
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Figure 5. Association of long-term mean simulated water non-limiting potential yield with crop seasonal (a) maximum and (b) minimum temperature among selected locations across India.
Figure 5. Association of long-term mean simulated water non-limiting potential yield with crop seasonal (a) maximum and (b) minimum temperature among selected locations across India.
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Figure 6. Correlation analysis to understand the effect of long-term climatic variability in rainfall, minimum (Tmin) and maximum temperature (Tmax), and solar radiation (SRD) on simulated grain yield of soybean under irrigated (SY_IRR) and rainfed (SY_RF) conditions along with actual (ACY) district-level yield for different locations across India. (* indicates p ≤ 0.05 and *** p ≤ 0.001).
Figure 6. Correlation analysis to understand the effect of long-term climatic variability in rainfall, minimum (Tmin) and maximum temperature (Tmax), and solar radiation (SRD) on simulated grain yield of soybean under irrigated (SY_IRR) and rainfed (SY_RF) conditions along with actual (ACY) district-level yield for different locations across India. (* indicates p ≤ 0.05 and *** p ≤ 0.001).
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Table 1. Estimated genetic coefficients for JS 335 soybean cultivars by using the CROPGRO model under DSSAT.
Table 1. Estimated genetic coefficients for JS 335 soybean cultivars by using the CROPGRO model under DSSAT.
S. No.Cultivar TraitsAcronymUnit* Genetic
Coefficients
1Critical Short-Day Length below which reproductive
development progresses with no day length effect		CSDLh12.35
2Slope of the relative response of development to photoperiod with timePPSENh−10.315
3Time between plant emergence and flower appearance (R1)EM-FLPhotothermal days22
4Time between first flower and first pod (R3)FL-SHPhotothermal days6.5
5Time between first flower and first seed (R5)FL-SDPhotothermal days13
6Time between first seed (R5) and physiological maturity (R7)SD-PMPhotothermal days32
7Time between first flower (R1) and end of leaf expansionFL-LFPhotothermal days18
8Maximum leaf photosynthesis rate at 30 °C, 350 vpm CO2, and high lightLFMAXmg Co2 m−2 s−11.03
9Specific leaf area of cultivar under standard growth conditionsSLAVRcm2 g−1400
10Maximum size of full leaf (three leaflets)SIZLFcm2180
11Maximum fraction of daily growth that is partitioned to seed + shellXFRT 1.00
12Maximum weight per seedWTPSDg0.15
13Seed filling duration for pod cohort at standard growth conditionsSFDURPhotothermal days22
14Average seed per pod under standard growing conditionsSDPDVNumbers per pod2.20
15Time required for cultivar to reach final pod load under optimal conditionsPODURPhotothermal days7.5
16Threshing percentage. The maximum ratio of
(seed/(seed + shell)) at maturity. Causes seeds to stop growing as their dry weight increases until shells are filled in a cohort.		THRSHPercentage78
17Fraction protein in seedsSDPROg protein g−1 seed0.400
18Fraction oil in seedsSDLIPg oil g−1 seed0.200
* Genetic coefficients are modified from [4].
Table 2. Geographical details, period of weather data used, and soil characteristics of the locations selected for simulation of potential yields of soybean in India.
Table 2. Geographical details, period of weather data used, and soil characteristics of the locations selected for simulation of potential yields of soybean in India.
S. No. Location StatePeriod No. of Simulated Years Latitude (°N)Longitude (°E)Soil Depth (CM)Soil Water Extractable at Maturity (SWXM)
1AmravatiMaharashtra1997–20172120.937477.7796240187
2BetulMadhya Pradesh1997–20182221.967277.7452240253
3DharMadhya Pradesh1997–20182222.495975.154545.029.9
4IndoreMadhya Pradesh1997–20172122.719675.8577160146
5NagpurMaharashtra1997–20182221.145879.0882140134
6RajgarhMadhya Pradesh1997–20182223.850976.733714069.9
7RatlamMadhya Pradesh1997–20182223.334275.0376160142
8SagarMadhya Pradesh1997–20182225.838878.7378140126
9SehoreMadhya Pradesh1997–20182223.205077.0851160124
10ShajapurMadhya Pradesh1997–20182223.418676.5951140132
11UjjainMadhya Pradesh1997–20172123.179375.784945.0120
12VidishaMadhya Pradesh1997–20182223.525177.8081140124
13Akola Maharashtra1997–20182220.700277.0082240181
14BhopalMadhya Pradesh1997–20172123.259977.4126140122
15GunaMadhya Pradesh1997–20182224.645577.286577.052.6
16HoshangabadMadhya Pradesh1997–20182222.744177.7370140137
17KotaRajasthan1997–20182225.213875.864818898
18NandedMaharashtra1997–20182219.138377.3210240216
19NeemuchMadhya Pradesh1997–20182224.476474.8624140102
20ParbhaniMaharashtra1997–20182219.264476.6413140125
21WardhaMaharashtra1997–20182220.745378.6022150127
22BelagaviKarnataka1997–20182215.849774.4977170171
23DharwadKarnataka1997–20182215.458975.007817086.6
24GulbargaKarnataka1997–20182217.329776.8343200127
25JabalpurMadhya Pradesh1997–20182223.181579.9864180189
26JhabuaMadhya Pradesh1997–20182222.915974.6869160154
27AnantapurAndhra Pradesh1997–20182214.681977.600615674.7
28BangaloreKarnataka1997–20182212.971677.594614693.3
29BellaryKarnataka1997–20182215.139476.921417047.7
30BijapurKarnataka1997–20182218.860880.721417079.3
31CoimbatoreTamil Nadu1997–20182211.016876.955812427.2
32FaizabadUttar Pradesh1997–20182226.773082.1458128105
33HissarHaryana1997–20182229.149275.721716857.6
34HyderabadTelangana state1997–20182217.385078.486720283.5
35JunagarhGujrat1997–20182221.522270.457912068.0
36KanpurUttar Pradesh1997–20182226.449980.331915663.6
37KarnoolAndhra Pradesh1997–20182215.828178.037315089.1
38LudhianaPunjab1997–20182230.901075.857316573.0
39New DelhiUttarakhand1997–20182228.613977.209016569.1
40PantnagarUttarakhand1997–20182228.961079.5154128109
41PuneMaharashtra1997–20182218.520073.8500150104
42RaichurKarnataka1997–20182216.200877.3622150125
43RaipurChhattisgarh1997–20182221.251481.6296160166
Table 3. Simulated and observed grain yield, and biomass yield of soybean cultivar JS 335 obtained from validation experiments conducted at Indore.
Table 3. Simulated and observed grain yield, and biomass yield of soybean cultivar JS 335 obtained from validation experiments conducted at Indore.
YearsRainfed JS 335Irrigated JS 335
GY_ObsGY_SimBY_ObsBY_SimGY_ObsGY_SimBY_ObsBY_Sim
(kg ha−1)(kg ha−1)(kg ha−1)(kg ha−1)(kg ha−1)(kg ha−1)(kg ha−1)(kg ha−1)
2013661168932484339883180137714560
2014628171440084829950181643215103
RMSE1057965892786
Table 4. Simulated yield (irrigated and rainfed), actual yield, and yield gaps of soybean at selected locations across India.
Table 4. Simulated yield (irrigated and rainfed), actual yield, and yield gaps of soybean at selected locations across India.
S. No. Locations Simulated Potential Yield (kg ha−1)Actual Yield
(kg ha−1)
(C)		Yield Gap (kg ha−1)
Irrigated (Water Non-Limiting)Rainfed (Water Limiting) Water Limitation
(A − B)		Factor Other than Water Availability (B − C)Total
(A − C)
Min Max Mean (A)CVMin Max Mean (B)CV
1Amravati33354979426210.204563272235.11025154016973237
2Betul25254051361510.517533485280518.697481018312640
3Dhar28535293403117.65092284147236.2118125592922850
4Indore21624214339820.014313634257629.0117082214062228
5Nagpur3222412738186.108823529240129.1916141714842902
6Rajgarh24854829408912.5041271417107.993126724873159
7Ratlam27054497386211.815843971279825.31085106417132777
8Sagar22544418393110.819103921325317.880367824503128
9Shajapur2575440239228.98933691267727.2965124517122957
10Sehore25364223362111.911143763289425.3111872717762503
11Ujjain30524790408610.222714292357612.5105051025263036
12Vidisha23714256373911.615424075303919.997570020642764
13Akola 2809456941729.2712253532215731.11142201510153030
14Bhopal2856424438489.1012533860288928.2103695918532812
15Guna3764456241564.446483548213135.9963202511683193
16Hoshangabad24443836321712.416473490273218.888148518512336
17Kota3099433038537.745393833202950.6119318248352659
18Nanded2954385934537.0415583621257523.9101187815632442
19Neemuch33275069422811.03914044253038.3920169816103308
20Parbhani3880483842925.9315663947277725.91131151516473161
21Wardha27004890384314.314524688335918.9103948523192804
22Belagavi2462352931149.1610873134255318.682356117302290
23Dharwad3217399036025.1003320147072.375621317142846
24Gulbarga3283403736786.6803652233940.2805133915342873
25Jabalpur24464467381111.923654220348212.893632925462875
26Jhabua2538387834529.5014733446254922.278590317642667
27Anantpur3478415938315.222473059116464.011362667292695
28Bangalore3731455941404.589393702249827.61012164214863128
29Bellary3321487438349.630122943988.410223395−5832812
30Bijapur26114561380212.50304191578.778228871333020
31Coimbatore2618366233107.430173643881.6*2872--
32Faizabad3543429240105.927484166344224.5*568--
33Hissar3481431740854.7343696128376.7*2802--
34Hyderabad2995395334726.282143616270230.1*770--
35Junagarh2687354931046.3643029216640.081193913542293
36Kanpur3022473439028.459843705270029.7819120218813083
37Kurnool3259388135283.794313483232749.2142112019062107
38Ludhiana3632439939564.4119013923321321.3*742--
39New Delhi21874082374010.916733978317822.7*562--
40Pantnagar3409414238525.982901402236637.97115118925122702
41Pune2613426537348.7014363910292024.520188159021716
42Raichur3537502341757.541673482147354.7100027014733175
43Raipur2748385335826.332667373034606.73113512223252447
Average2947433737948.9410103609244636.01025134814332775
CV a15.89.798.0239.280.017.733.464.321.764.152.712.9
a CV, coefficient of variation (%). * District/state average yields are not available due to negligible area under soybean.
Table 5. Solar radiation and minimum and maximum temperatures during the crop growth period of simulated soybean at selected locations across India.
Table 5. Solar radiation and minimum and maximum temperatures during the crop growth period of simulated soybean at selected locations across India.
S. No Locations Solar Radiation (MJm−2 day−1)Maximum Temperature (°C)Minimum Temperature (°C)
MinMaxMeanCVMinMaxMeanCVMinMaxMeanCV
1Amravati17.024.420.29.6031.536.934.33.3118.925.323.27.43
2Betul15.719.017.65.5630.933.732.42.2322.524.223.32.01
3Dhar16.122.019.27.8229.035.934.33.9319.424.723.25.71
4Indore10.820.916.618.230.435.033.33.4323.024.724.01.87
5Nagpur18.222.620.78.5133.136.334.92.0224.325.825.11.61
6Rajgarh16.923.619.69.7931.339.635.34.6021.026.724.36.08
7Ratlam15.520.918.57.0631.135.333.22.5222.624.623.71.92
8Sagar16.320.818.75.0731.635.133.92.3323.324.924.21.83
9Shajapur15.620.918.26.6532.735.734.12.2123.324.924.21.54
10Sehore16.820.418.95.230.038.133.617.923.725.024.41.34
11Ujjain17.121.519.36.4532.235.833.72.3221.724.523.43.12
12Vidisha15.820.518.57.2232.837.034.73.0722.525.324.52.21
13Akola 16.220.819.76.0433.136.034.72.3523.825.424.71.98
14Bhopal16.919.418.34.5931.635.633.82.3323.024.924.32.00
15Guna18.521.319.93.4232.937.035.02.2923.225.224.42.19
16Hoshangabad15.118.517.25.940.035.532.225.523.825.624.61.78
17Kota18.222.719.96.1235.039.736.63.0524.826.425.61.68
18Nanded15.519.917.95.5030.737.334.23.3122.124.823.92.74
19Neemuch17.423.420.58.5833.237.134.62.3719.825.123.37.55
20Parbhani18.622.020.24.8132.735.434.32.1021.323.923.02.90
21Wardha15.123.319.010.732.736.534.72.5420.026.924.66.00
22Belagavi14.618.216.35.3027.736.630.18.2220.423.121.94.42
23Dharwad16.518.917.63.0428.331.529.52.0720.321.520.91.61
24Gulbarga16.118.817.44.3731.836.333.62.6222.724.623.51.88
25Jabalpur16.720.418.65.7631.935.634.02.2423.225.424.52.34
26Jhabua15.519.217.96.350.036.232.425.523.925.724.62.02
27Anantpur18.523.120.96.3533.336.334.91.7422.725.424.22.48
28Bangalore17.319.418.52.7628.130.229.21.8019.120.219.71.65
29Bellary16.725.919.49.7132.137.334.12.7417.226.423.47.79
30Bijapur15.921.418.97.6831.836.033.22.9820.823.322.02.53
31Coimbatore16.020.918.35.4028.033.632.32.7320.823.722.92.54
32Faizabad17.423.319.16.1333.036.534.62.2423.626.825.13.09
33Hissar18.822.320.74.2435.639.137.12.2822.926.624.63.59
34Hyderabad15.619.216.94.9930.633.932.72.0519.923.722.63.99
35Junagarh16.320.718.45.210.036.433.217.925.126.025.50.87
36Kanpur18.022.519.36.0034.037.435.42.5121.427.424.96.64
37Kurnool18.520.219.32.4434.136.535.32.1424.026.025.22.12
38Ludhiana18.323.419.97.110.038.134.417.923.925.925.01.82
39New Delhi9.720.618.511.70.039.335.217.924.326.825.92.20
40Pantnagar17.021.118.75.3031.735.033.72.6020.224.823.84.01
41Pune14.818.917.65.0729.532.531.02.2521.522.922.31.49
42Raichur18.123.420.56.370.037.133.817.819.324.623.24.91
43Raipur17.018.917.82.7229.436.834.13.5424.526.325.41.85
Average16.421.218.86.4426.536.133.85.5722.125.023.93.05
CV a10.98.526.0743.345.15.314.791208.565.665.2261.1
a CV, coefficient of variation (%).
Table 6. Crop failure observed during simulation periods, reasons for crop failure obtained from the crop simulation model, and supporting data for crop failure from AICRP, FLDs, and actual/district yield.
Table 6. Crop failure observed during simulation periods, reasons for crop failure obtained from the crop simulation model, and supporting data for crop failure from AICRP, FLDs, and actual/district yield.
S. No.LocationsYears Reasons for Crop Failure* AICRP
Experiment Yield
(kg ha−1)		* FLDs Yield (kg ha−1)** Actual/
District Yield
(kg ha−1)
1Amravati2009During 2011, rainfall data was not available. Whereas, in 2009, only 30 mm seasonal total rainfall was recorded.1611800809
201184814521258
2Rajgarh2002During 2008 and 2011 rainfall data was not available. However, in 2002, 2003, and 2004 meager amount of total seasonal rainfall was received (11–21 mm)--341
2003--1135
2004--885
2008--1087
2011--856
3Hoshangabad2017Weather data not available---
2018---
4Dharwad2003Meager amount of total seasonal rainfall received (46 mm)--638
5Gulbarga2008Meager amount of total seasonal rainfall received (181 mm)--690
6Jhabua2017Weather data not available---
2018---
7Bellary2001Meager amount of total seasonal rainfall was received (103.5 mm in 2001, 8.5mm in 2003 and 19.5 mm in 2006)---
2003---
2006---
8Bijapur1997During 1997 rainfall data was not available. In the remaining years 2001 and 2003 meager amount of seasonal rainfall was received
(28–37 mm)		---
2001---
2003---
9Coimbatore2012Meager amount of total seasonal rainfall was received (20 mm)---
10Junagarh2018Weather data not available---
11Ludhiana2016Weather data not available---
12New Delhi2005Weather data not available---
* Data taken from AICRP on soybean reports of respective years ** DE&S, Govt. of India.
Table 7. Water balance components of simulated soybean at selected locations across India.
Table 7. Water balance components of simulated soybean at selected locations across India.
S. No.Location Seasonal RainfallSeason Surface RunoffSeason Water Drainage
MinMaxMeanCVMinMaxMeanCVMinMaxMeanCV
1Amravati098066141.1030616650.0017119235
2Betul6241604103523.012667231347.4043217669.8
3Dhar442118178723.74742121650.16049925040.2
4Indore483112482922.99249528534.8025278103
5Nagpur652127894319.013753131235.6024710162.7
6Rajgarh01380487101.205161391160488127127
7Ratlam480117583823.36540222243.5032011687.7
8Sagar6281925108630.811292737053.1066822371.2
9Shajapur405194390736.898101329968.50444122110
10Sehore709149296424.78951623350.26750924853.9
11Ujjain483115083826.4927213060.29459631249.6
12Vidisha6161938110228.412767931146.45970530457.6
13Akola 352102164227.14234316746.4012312254
14Bhopal598160395927.414274033743.2449715679.1
15Guna410149190129.77264832249.33446520753.9
16Hoshangabad7821644112724.315056832038.817462634344.4
17Kota310105362528.54224712449.0026944156
18Nanded517104376220.06445016371.1018937172
19Neemuch288135373630.04863418765.7034410889.2
20Parbhani430119576427.56050122052.1022865114
21Wardha370130887423.28256520749.91143224244.5
22Belagavi5172180133533.69464833344.9092144765.1
23Dharwad4685749843.701898463.00869248
24Gulbarga18178255528.810844058.109111224
25Jabalpur7992099130128.013789346246.23462429856.2
26Jhabua639120989921.113735123730.2033015766.8
27Anantpur11272740339.40972012410634320236.6
28Bangalore28189960326.151435666.618150335824.0
29Bellary957431348.9016531121000-
30Bijapur0104746851.0034510078.001487469
31Coimbatore2092021187.5014925139024613409
32Faizabad337118573637.6831911474.84855827956.7
33Hissar12471043235.61027410663.90446194
34Hyderabad293141273537.13282423870.7000-
35Junagarh155135866944.32155919866.9039398110
36Kanpur Dehat379136875731.772516790.411881841838.3
37Kurnool25083253826.7112289855.6000-
38Ludhiana31097463332.31428510873.016848631132.1
39New Delhi384103465726.51829716846.44743417151.0
40Pantnagar6942999153537.9144111443356.5266138169146.1
41Pune362190474247.41738612767.4174133141560.8
42Raichur30581354925.5152438464.509617181
43Raipur7101522109721.29746123340.814166138441.9
Average383130978033.755.445919561.941.5419176112
CV a59.636.235.246.99354.557.539.116273.689.789
a CV, coefficient of variation (%).
Table 8. Soybean yield gap I and yield gap II of major soybean-growing states in India.
Table 8. Soybean yield gap I and yield gap II of major soybean-growing states in India.
LocationsDistrict/Actual/Farmer Yield
(A)		Experimental/Potential Yield (B)FLDs/Achievable
Yield
(C)		YG_1
(B-C)		YG_2
(C-A)
(kg ha−1) (kg ha−1)(kg ha−1) (kg ha−1)(kg ha−1)
Indore (MP)158821482363−215775
Amravati (MH)95314031431−28478
Parbhani (MH)95520641863201908
Pune (MH)2220317618521324−369
Dharwad (KT)765246619215441156
Kota (RAJ)11891657160156412
Raipur (CH)126916981858−160589
Average127720881841247564
* CV38.728.615.8219.086.0
* CV: Coefficient of variation (%); MP, Madhya Pradesh; MH, Maharashtra; RAJ, Rajasthan; KT, Karnataka and CH, Chhattisgarh.
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Nargund, R.; Bhatia, V.S.; Sinha, N.K.; Mohanty, M.; Jayaraman, S.; Dang, Y.P.; Nataraj, V.; Drewry, D.; Dalal, R.C. Assessing Soybean Yield Potential and Yield Gap in Different Agroecological Regions of India Using the DSSAT Model. Agronomy 2024, 14, 1929. https://doi.org/10.3390/agronomy14091929

AMA Style

Nargund R, Bhatia VS, Sinha NK, Mohanty M, Jayaraman S, Dang YP, Nataraj V, Drewry D, Dalal RC. Assessing Soybean Yield Potential and Yield Gap in Different Agroecological Regions of India Using the DSSAT Model. Agronomy. 2024; 14(9):1929. https://doi.org/10.3390/agronomy14091929

Chicago/Turabian Style

Nargund, Raghavendra, Virender S. Bhatia, Nishant K. Sinha, Monoranjan Mohanty, Somasundaram Jayaraman, Yash P. Dang, Vennampally Nataraj, Darren Drewry, and Ram C. Dalal. 2024. "Assessing Soybean Yield Potential and Yield Gap in Different Agroecological Regions of India Using the DSSAT Model" Agronomy 14, no. 9: 1929. https://doi.org/10.3390/agronomy14091929

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