Bhupinder Singh

Interests

Research

My Extension and Research program aimed at studying the three primary factors of Cropping Systems (genotype, management, and environment) to enhance crop yield and quality. My program goal is to develop a program that offers practical solutions to the challenges farmers and stakeholders face. My program utilize outreach programs such as Cooperative Extension, Twitter, Webex, Podcasts, YouTube, and newspapers to understand the needs of farmers and promptly share my research and extension findings. My program focus areas include crop physiology, remote sensing, and crop modeling to understand the mechanisms that enable crop adaptation and acclimation to various stresses in the desert environment of Arizona. Additionally, I am interested in studying the long-term environmental impacts of conservation and integrated farming practices under projected future climatic conditions. Visit the SINGH LAB for more information: https://sites.arizona.edu/singh-lab/

Courses

No activities entered.

Scholarly Contributions

Chapters

• Singh, B. (2020). Cotton responses to temperature and moisture stress during seed germination 2020. In Cotton seed and seedlings.
• Singh, B. (2020). Cotton seedling growth and development responses to temperature and drought stress. In Cotton seed and seedlings.

Journals/Publications

• Al-mafraji, I. K., Mohammed, A. H., Alsajri, F. A., Sah, S. K., Kakar, N., Wijewardana, C., Singh, B., & Jumaa, S. H. (2024). Studying the cumulative vigor response index of morpho-physiological, quality, and yield-related traits of wheat cultivars using planting dates. Tikrit Journal for Agricultural Sciences, 24(1), 223-237. doi:https://doi.org/10.25130/tjas.24.1.18
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  Wheat (Triticum aestivum L) is one of the major staple food crops consumed globally. Nonetheless, the cultivation of wheat is influenced by various environmental factors, with the planting date being significantly impacted by the effects of climate change. Addressing these changes could involve evaluating wheat genotypes to identify appropriate planting dates. A phenotypic screening experiment was conducted in the field crop station of Agriculture College of Tikrit University to determine the suitable planting time for wheat cultivars under local environmental conditions during 2022-23. Several morpho-physiological, quality, and yield traits were measured. Factorial experiment using spilt plot through randomized completely block design (RCBD). was used with three replications. The five planting dates (5-10, 25-10, 15-11, 5-12, and 25-12) were considered as the main plot, and the eight wheat cultivars (Ipaa99, Al-Rasheed, Al-Baraka, Sham6, Tammuz2, Al-Hashimiya, Al-Noor, and Al-Adnanieh) as sub-main plot. Data were used to calculate the Individual, Cumulative, and Total Vigor Response Indices (IRI, CRI & TRI). Cultivars were classified into different categories using total cumulative early or late planting date vigor response index values (TRI-e) or (TRI-l) and standard deviation (SD). The (TRI-e) values ranged from 36.07 (sensitive) for the cultivar Al-Baraka to 39.13 (tolerant) for the cultivar Al-Hashimiya. However, the (TRI-l) values ranged from 36.59 (sensitive) for the cultivar Al-Noor to 39.52 (tolerant) for the cultivar Al-Hashimiya. The correlation coefficient (r2) between the (TRI-e) and cumulative very early/early planting date vigor response index was positively correlated (r2 = 0.70 for very early planting date (5-Oct) and r2 = 0.60 for early planting date (25-Oct). Furthermore, 76% of the total variation in the (TRI-l) was explained by the cumulative very late planting date vigor response index (CRI-vl) while just 49% of the total variation was explained by the cumulative late planting date vigor response index (CRI-l). Based on those results, wheat producers could select either tolerant cultivars for early planting or tolerant cultivars for late planting to maximize wheat production in their specific growing environments including planting dates.
• Samanta, S., Ale, S., Himanshu, S. K., Singh, B., & Kothari, K. (2024). Identification of Priority-based Variable Deficit Irrigation Strategies for Grain Sorghum Production in the Texas High Plains under Increasing Climate Variability. Journal of Agricultural Engineering (India), 61(3). doi:https://doi.org/10.52151/jae2024613.1850
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  Agriculture accounts for 70% of global freshwater usage, majority of which is used for irrigation. Irrigated agriculture in the Texas High Plains (THP) region of the United States highly depends on groundwater availability in the Ogallala Aquifer. Rapidly declining groundwater levels, recurring droughts and increasing climate variability are primary concerns for crop production in the THP, and they necessitate the adoption of efficient irrigation strategies. The objective of this study is to identify efficient growth-stage-based variable deficit irrigation (GS-VDI) strategies for grain sorghum (Sorghum bicolor L.) production using a prioritization scheme that assigns different priority weights for yield, crop water productivity (CWP), and irrigation under different climate variability classes. The Decision Support System for Agrotechnology Transfer Cropping System Model (DSSAT CSM) CERES-Sorghum model was used to simulate a total of 257 GS-VDI strategies, including a control scenario with 100% evapotranspiration (ET)-replacement and 256 combinations of scenarios developed by implementing four levels of ET-replacement (30%, 50%, 70%, and 90%) in four growth stages of sorghum: (i) emergence to panicle initiation, (ii) panicle initiation to boot, (iii) boot to early grain filling, and (iv) early to late grain filling. A prioritization index was developed to analyse the outcomes of these strategies under three prioritization scenarios: (i) equal priority on yield, CWP, and irrigation (PS-1), (ii) higher priority on yield and CWP (PS-2), and (iii) higher priority on CWP and irrigation (PS-3). All three prioritization schemes identified S-61 (30-90-90-30% ET replacement levels during growth stages one through four) and S-29 (30-50-90-30) strategies as the ideal GS-VDI strategies during dry and wet years, respectively. During the normal years, S-62 (30-90-90-50) was identified as the ideal strategy under PS-1 and PS-2 schemes, whereas S-61 strategy was identified as ideal in the PS-3 scheme. These ideal strategies could save 26% to 42% of irrigation water at the expense of 10% to 20% loss in yield in different years. Results from this study would be useful for producers, farm managers, and natural resource conservationists in their efforts to adopt or promote GS-VDI to maximize cropland productivity and reduce irrigation water usage from the Ogallala Aquifer.
• Singh, B. (2023). Reniform nematode impact on cotton growth and management strategies: A review. Agronomy Journal.
• Singh, B. (2023). Single and Multispecies Cover Crop Effects on Corn Production and Economic Returns. Journal of Contemporary Water Research & Education.
• Singh, B. (2022). Early Season Growth Responses of Resistant and Susceptible Cotton Genotypes to Reniform Nematode and Soil Potassium Application.
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  A greenhouse study was conducted to investigate the roles that host plant resistance and soil potassium (K) levels play in affecting Rotylenchulus reniformis Linford and Oliveira (Tylenchida: Hoplolaimidae) (RN) populations and early season cotton (Gossypium hirsutum L.) growth. Two upland, RN-resistant cotton lines (G. barbadense introgressions: 08SS110-NE06.OP and 08SS100), a genetic standard (Deltapine 16) and a commercially available susceptible cultivar (PHY 490 W3FE) were evaluated at four different levels of K [100% of recommended rate, 150% of recommended, 50% of recommended, and a base level] from seeding until harvesting, 60 days after sowing (DAS). Quadratic functions (r2 = 0.82 to 0.95) best described the early season growth response of cotton genotypes to soil K. The base K level was associated with the lowest values for most morphological variables, including plant height (PH), mainstem nodes (MSN), leaf area, and dry weight at 30 DAS and 60 DAS. However, soil K did not affect RN population counts (RC). Additionally, soil K did not influence the rate of change in growth variables among genotypes. The resistant genotype 08SS110-NE06.OP showed greater growth in terms of time to first true leaf, PH, MSN, and above-ground dry weights compared to the commercially available susceptible genotype. No interaction between K and RN or genotype and RN was found in early season cotton growth. However, RC in pots of resistant genotypes was less than in pots of susceptible genotypes. Our research on the early season growth response to soil K by novel, RN-resistant genotypes and susceptible genotypes contributes to the development of improved RN resistance and fertilization management in cotton.
• Singh, B. (2022). Early Season Growth Responses of Resistant and Susceptible Cotton Genotypes to Reniform Nematode and Soil Potassium Application. Agronomy.
• Singh, B. (2021). Agronomic characterization of cotton genotypes susceptible and resistant to reniform nematode in the United States Midsouth. Agronomy Journal.
• Singh, B. (2020). EarlySeason Morphological and Physiological Responses of Resistant and Susceptible Cotton Genotypes to Reniform Nematode and Soil Nitrogen. Agronomy.
• Singh, B. (2020). Temperature Effects on Soybean Seedling Shoot and Root Growth and Developmental Dynamics.. Journal of the Mississippi Academy of Sciences.
• Singh, B. (2019). Evaluating soybean cultivars for low- And high-temperature tolerance during the seedling growth stage. Agronomy.
• Singh, B. (2019). Parental environmental effects on seed quality and germination response to temperature of andropogon gerardii. Agronomy.
• Singh, B. (2019). Projected day/night temperatures specifically limits rubisco activity and electron transport in diverse rice cultivars. Environmental and Experimental Botany.
• Singh, B. (2019). Sensitivity and recovery of grain sorghum to simulated drift rates of glyphosate, glufosinate, and paraquat. Agriculture (Switzerland).
• Singh, B. (2019). Weed management programs in grain Sorghum (Sorghum bicolor). Agriculture (Switzerland).
• Singh, B. (2018). Assessing morphological characteristics of elite cotton lines from different breeding programmes for low temperature and drought tolerance. Journal of Agronomy and Crop Science.
• Singh, B. (2018). Assessing stomatal and non-stomatal limitations to carbon assimilation under progressive drought in peanut (Arachis hypogaea L.). Journal of Plant Physiology.
• Singh, B. (2017). Developing a screening tool for osmotic stress tolerance classification of rice cultivars based on in vitro seed germination. Crop Science.
• Singh, B. (2017). Screening of Rice Cultivars for Morpho-Physiological Responses to Early-Season Soil Moisture Stress. Rice Science.

Presentations

• Singh, B. (2025, January). Agronomy Extension and Research Program for Arizona. Organic and Agronomy Cropping Systems Meet and Greet. Yuma Agricultural Center: UA Cooperative Extension.
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  I presented my agronomy program to Yuma farmers and agricultural stakeholders and peer scientists. 
• Singh, B. (2024, August). Introduction to UA Agronomy Program. Annual Cotton Tent Talks. Goodyear, AZ.
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  UA Agronomy Program focuses on exploring three key factors in Agronomy: genotype, management, and environment, with the aim of boosting both crop yield and quality. The primary goal is to provide practical solutions to the challenges faced by farmers and stakeholders in Arizona. To effectively engage with the agricultural community, the program employs various outreach methods such as Cooperative Extension, Twitter, Webex, podcasts, YouTube, and newspapers, ensuring that the needs of farmers are understood and that agronomic research and extension findings are communicated in a timely manner. Key focus areas of the program include crop physiology, remote sensing, and crop modeling, all aimed at uncovering the mechanisms that allow crops to adapt and acclimate to the various stresses present in Arizona’s desert environment.
• Singh, B. (2024, August). Introduction to UA Agronomy Research and Extension Program . Alfalfa and Forage Tent Talks. Buckeye, AZ: UA Cooperative Extension.
  More info

  UA Agronomy Program focuses on exploring three key factors in Agronomy: genotype, management, and environment, with the aim of boosting both crop yield and quality. The primary goal is to provide practical solutions to the challenges faced by farmers and stakeholders in Arizona. To effectively engage with the agricultural community, the program employs various outreach methods such as Cooperative Extension, Twitter, Webex, podcasts, YouTube, and newspapers, ensuring that the needs of farmers are understood and that agronomic research and extension findings are communicated in a timely manner. Key focus areas of the program include crop physiology, remote sensing, and crop modeling, all aimed at uncovering the mechanisms that allow crops to adapt and acclimate to the various stresses present in Arizona’s desert environment.
• Singh, B. (2024, December 3). Introduction to UA Agronomy Research and Extension Program. The Arizona Pest Management Center IPM Coordinating Committee Meeting. Maricopa Agricultural Center: UA Coperative Extension.
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  The UA Agronomy research and extension programs centered on “Cropping Systems and Physiology.” This initiative focuses on exploring three key factors in Agronomy: genotype, management, and environment, with the aim of boosting both crop yield and quality. The primary goal is to provide practical solutions to the challenges faced by farmers and stakeholders in Arizona. To effectively engage with the agricultural community, the program employs various outreach methods such as Cooperative Extension, Twitter, Webex, podcasts, YouTube, and newspapers, ensuring that the needs of farmers are understood and that agronomic research and extension findings are communicated in a timely manner. Key focus areas of the Lab include crop physiology, remote sensing, and crop modeling, all aimed at uncovering the mechanisms that allow crops to adapt and acclimate to the various stresses present in Arizona’s desert environment.
• Singh, B. (2024, December 3, 2024). Assessing Morpho-Physiological Mechanisms Driving Lettuce Nutrient Use Efficiency in Response to Biostimulants. The 14th Annual Central AZ Farmer Field Day. Maricopa Agricultural Center, Maricopa, AZ: UA Cooperative Extension.
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  Familiarize Arizona farmers on the use of various agronomic and plant physiological tools to assess morpho-physiological mechanisms driving lettuce nutrient use efficiency in response to biostimulants
• Singh, B. (2024, December). U of A Cooperative Extension Agronomy Program Intro. Maricopa Farm Bureau Board Meeting. Tempe, AZ: Arizona Farm Bureau.
• Singh, B. (2024, October 7). Strategic Framework for Agronomy Extension and Research Program. County Supervisor's Tour Planning Meeting. Maricopa Agricultural Center.
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  The UA Agronomy research and extension programs centered on “Cropping Systems and Physiology.” This initiative focuses on exploring three key factors in Agronomy: genotype, management, and environment, with the aim of boosting both crop yield and quality. The primary goal is to provide practical solutions to the challenges faced by farmers and stakeholders in Arizona. To effectively engage with the agricultural community, the program employs various outreach methods such as Cooperative Extension, Twitter, Webex, podcasts, YouTube, and newspapers, ensuring that the needs of farmers are understood and that agronomic research and extension findings are communicated in a timely manner. Key focus areas of the program include crop physiology, remote sensing, and crop modeling, all aimed at uncovering the mechanisms that allow crops to adapt and acclimate to the various stresses present in Arizona’s desert environment.
• Singh, B., Ale, S., Samanta, S., Barnes, E., & Thorp, K. (2024, November 10-13). Potential Effects of Climate Change on Cotton Phenology and Production at Maricopa in Arid Central Arizona. ASA, CSSA, SSSA International Annual Meeting. San Antonio, TX, USA: Agronomic Science Foundation.
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  Changing climate is a rising concern for cotton production in arid central Arizona. Understanding the impacts of climate change on cotton production is essential for developing ideal cotton production strategies. The overall objective of this study was to simulate the effects of projected changes in future climate on irrigated cotton production at Maricopa in arid central Arizona using the Decision Support System for Agrotechnology Transfer (DSSAT) Cropping System Model (CSM) CROPGRO-Cotton model. This study used a previously evaluated DSSAT CROPGRO-Cotton model based on seed cotton yield and in-season growth data obtained from field experiments relating to the Free-Air CO2 Enrichment (FACE) conducted at the Maricopa Agricultural Center in Maricopa, AZ. The latest CMIP6 climate change projections from 1950 to 2100 were obtained for the study site for four Shared Socioeconomic Pathway (SSP) scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. and categorized into four time periods: Historic/baseline (1991-2020), Near-future (2023-2040), Mid-century (2041-2070), and Late-century (2071-2100). The model outputs for the selected four time intervals were analyzed to assess the projected climate change impact on cotton phenology, seed cotton yield, irrigation water requirement, and growing season length (i.e., time to maturity). Substantial differences in cotton growing season length, yield, and irrigation requirement among future periods and SSPs were simulated. Compared to the historical period, the growing season length was projected to slightly reduce by a maximum of one week in the future. Simulated irrigated seed cotton yield decreased stepwise from 24 to 60% over future periods, despite a step-wise increase in irrigation amount from 10 to 26%. As expected, the lowest decline in irrigated seed cotton yield was predicted under SSP1-2.6 (32%) and the highest under SSP5-8.5 (52%) in the future. The expected increase in irrigation water requirement was mainly attributed to an increase in total seasonal transpiration. Findings from this study will be useful to evaluate and develop short and long-term production strategies that could be sustainable and profitable for cotton production in arid central Arizona in the future.
• Singh, B., Samanta, S., Ale, S., Barnes, E., & Thorp, K. (2024, November). Projected Climate Change Impacts on Irrigated Cotton Production in Two Distinct Regions of the United States Cotton Belt. ASA, CSSA, SSSA International Annual Meeting. San Antonio, TX, USA: Agronomic Science Foundation.
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  Changes to the projected climate are apparent between distinct geographic locations across the Cotton Belt, which may, therefore, impact cotton phenology and production to a variable degree. The overall objective of this study was to evaluate the effects of projected changes in future climate on irrigated cotton production in the semi-arid high plains region of Texas and the central desert region of Arizona. The latest CMIP6 climate change projections from 1950 to 2100 were obtained for Halfway, TX, and Maricopa, AZ, representing the above two distinct geographical locations four Shared Socioeconomic Pathway (SSP) scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The DSSAT CROPGRO-Cotton model was previously evaluated for the study regions based on measured yield and in-season physiological data at Maricopa and Halfway. The evaluated models were used in this study for climate change impact assessment. The projected climate data were categorized into four time periods: Historic/baseline (1991-2020), Near-future (2023-2040), Mid-century (2041-2070), and Late-century (2071-2100). The results from simulations were analyzed to assess and compare projected climate change impact on cotton phenology and yield, irrigation water requirement, and growing season length (time to maturity) between the two locations. The results indicated substantial differences among future time periods and SSPs at both locations. Compared to the historical period, season length could be reduced by 17-23 days at Halfway in the future while no change is expected at Maricopa. The irrigated seed cotton yield was projected to increase by a maximum of 652 kg ha-1at Halfway and 1450 kg ha-1at Maricopa. Simulated seed cotton yield was higher in the future periods at Halfway than Maricopa. The seed cotton yield could increase with an increase in irrigation water requirement by 7.5% at Halfway and only 1.2% at Maricopa in the future, attributing to higher crop water productivity at Halfway than at Maricopa.