Current Research

Ph.D. Research Projects

Project 1: Impact of recent climate change on Mississippi corn: ARDL modeling

Our work focuses on analyzing trends in climatic variables (carbon emissions, maximum temperature, lowest temperature, precipitation, diurnal temperature range, and relative humidity) during the Mississippi corn growing season using data inputs from the previous 50 years using Mann-Kendall and Sen slope method. Second, the study uses the auto-regressive distributive lag model to calculate the impact of each meteorological variable on corn yield. The model is able to account for both direct and indirect effects of climate change by incorporating residual effects from past agricultural input years. The study also focuses on identifying the critical crop-climate months to assist farmers in preparing for those critical months by cautiously altering their management strategy for planting dates.

Project 2: Suitability of optical sensors in predicting sulfur deficiency in Mississippi corn.

From past two decades, due to drastic decline in atmospheric industrial emissions caused by improved SO2 pollution control strategies, sulfur (S) deposition to soils has reduced, and hence, S is increasingly becoming deficient. As a S-diagnostic tool, the recent studies showed that the opportunities exist in testing optical sensors. Therefore, the study is aimed to ascertain the applicability of optical sensors in assessing the S requirement in Mississippi corn. More specifically, the crop circle, SPAD, and MicaSense RedEdge sensors are tested at different growth stages to identify the best sensor, vegetation index, and growth stage for predicting S deficiency. Study outcomes will guide farmers well in time so that the additional fertilization can be done before plant loses its potential to regain the yield.

Project 3: Effect of nitrogen and sulfur interaction on yield and quality of Mississippi corn

Over the last two decades, the research has revealed the unbalanced crop supplemental nitrogen (N) and S fertilization rates in major corn producing countries including the United States, China, Brazil, and Argentina. Hence, this study is focusing on testing the effect of N and S interaction on yield and quality of MS corn, to guide the best combined rates for N and S. The four different rates of S (0, 20, 40, and 60 lb/acre) are tested with four different rates of N (0, 100, 200, and 300 lb/acre) using complete factorial randomized complete block design, to see which combination yields maximum yield and quality. Also, the study is calculating the agronomic optimum rates of N and S by fitting the linear-plateau or quadratic plateau models using R® statistical software.

Other Research Projects

Project 4: Greenhouse study on nitrogen and sulfur interaction in corn

Examining the suitability of hyperspectral sensing in predicting sulfur deficiency in corn under controlled conditions. The best vegetation indices and corn growth stage that can efficiently predict sulfur deficiency will be identified. The agronomic optimum rates of nitrogen and sulfur for corn will be determined.   

Project 5: Mississippi's major crops and climate change

Investigating the short-term and long-term sensitivities of soybeans, cotton, rice, wheat, and sorghum to Mississippi climate change using advanced ARDL econometric model.

Project 6: Reviewing the role of sulfur in crop production.

In the early 1950s, S was only deficient in specific soils, but now it is universally deficient. The amount of plant-available S in the soil has decreased by 34 to 86% between 2000 and 2020, leaving crops at risk of deficiency. Recognizing the aforesaid, we have designed a project focused on highlighting the future perspectives of S research on areas covering the S demand and availability, nutritive effects in improving yield, S use efficiency, its effect on quality in major cereals, precisely and timely diagnostic methods, efficient S fertilizing strategies, and how it alleviates plant stresses.

Projects in Collaboration (Other Universities)

Project 7: Energy Use Efficiency and Greenhouse Gas Emissions in Punjab (India) Agriculture (collaboration with Punjab Agricultural University, India)

The study is focused on quantifying the GHGE and energy use efficiency in major crops (cotton, maize, paddy, wheat, and sugarcane) of Punjab (India). The study's findings can help the climate mitigation strategists in disintegrating the understanding on climate-energy nexus at regional level.

Project 8: Adoption of Direct Seeded Rice Technology to tackle climate challenges in Indian Punjab: Lessons and Challenges (collaboration with McMaster University, Canada. University of Toronto, Canada. Central University of Punjab, India)

The Punjab (India) government's current agricultural policies aim to transform rice farming systems from transplanting to direct seeding systems in order to conserve underground water. Recently, the Punjab government launched a shifting program for farmers, therefore, this study is focused on identifying the challenges faced by Punjab (India) farmers in the shifting from transplanting to the directed seeded rice (DSR) technology The study outcomes can help the government to improve the DSR adoption rate, by framing better farm-size specific strategies considering various categories, including small, marginal, and large farmers.

Project 9: Performance of machine learning models for crop yield prediction (collaboration with Chitkara University, India)

This study is focused on conducting a systematic literature review on suitability and effectiveness of machine learning models specifically for corn and soybean yield prediction.

Project 10: Examining the effect of CO2 emissions on agricultural growth in India: An ARDL approach.

Understanding the short term and long-term nexus between energy use, economic expansion, greenhouse gas emissions, and agricultural (cereals and course cereals) production using the ARDL econometric model based on 1975-2019 time series dataset.