Browsing by Author "Kumar, Vipin"
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- Cover crop termination options and application of remote sensing for evaluating termination efficiencyKumar, Vipin; Singh, Vijay; Flessner, Michael L.; Haymaker, Joseph; Reiter, Mark S.; Mirsky, Steven B. (Public Library of Science, 2023-04-20)Efficient termination of cover crops is an important component of cover crop management. Information on termination efficiency can help in devising management plans but estimating herbicide efficacy is a tedious task and potential remote sensing technologies and vegetative indices (VIs) have not been explored for this purpose. This study was designed to evaluate potential herbicide options for the termination of wheat (Triticum aestivum L.), cereal rye (Secale cereale L.), hairy vetch (Vicia villosa Roth.), and rapeseed (Brassica napus L.), and to correlate different VIs with visible termination efficiency. Nine herbicides and one roller-crimping treatment were applied to each cover crop. Among different herbicides used, glyphosate, glyphosate + glufosinate, paraquat, and paraquat + metribuzin provided more than 95% termination for both wheat and cereal rye 28 days after treatment (DAT). For hairy vetch, 2,4-D + glufosinate and glyphosate + glufosinate, resulted in 99 and 98% termination efficiency, respectively, followed by 2,4-D + glyphosate and paraquat with 92% termination efficiency 28 DAT. No herbicide provided more than 90% termination of rapeseed and highest control was provided by paraquat (86%), 2,4-D + glufosinate (85%), and 2,4-D + glyphosate (85%). Roller-crimping (without herbicide application) did not provide effective termination of any cover crop with 41, 61, 49, and 43% termination for wheat, cereal rye, hairy vetch, and rapeseed, respectively. Among the VIs, Green Leaf Index had the highest Pearson correlation coefficient for wheat (r = -0.786, p = <0.0001) and cereal rye (r = -0.804, p = <0.0001) with visible termination efficiency rating. Whereas for rapeseed, the Normalized Difference Vegetation Index (NDVI) had the highest correlation coefficient (r = -0.655, p = <0.0001). The study highlighted the need for tankmixing 2,4-D or glufosinate with glyphosate for termination instead of blanket application of glyphosate alone for all crops including rapeseed and other broadleaf cover crops.
- Impact of Futuristic Climate Variables on Weed Biology and Herbicidal Efficacy: A ReviewKumar, Vipin; Kumari, Annu; Price, Andrew J.; Bana, Ram Swaroop; Singh, Vijay; Bamboriya, Shanti Devi (MDPI, 2023-02-15)Our changing climate will likely have serious implications on agriculture production through its effects on food and feed crop yield and quality, forage and livestock production, and pest dynamics, including troublesome weed control. With regards to weeds, climatic variables control many plant physiology functions that impact flowering, fruiting, and seed dormancy; therefore, an altered climate can result in a weed species composition shift within agro-ecosystems. Weed species will likely adapt to a changing climate due to their high phenotypic plasticity and vast genetic diversity. Higher temperatures and CO2 concentrations, and altered moisture conditions, not only affect the growth of weeds, but also impact the effectiveness of herbicides in controlling weeds. Therefore, weed biology, growth characteristics, and their management are predicted to be affected greatly by changing climatic conditions. This manuscript attempted to compile the available information on general principles of weed response to changing climatic conditions, including elevated CO2 and temperature under diverse rainfall patterns and drought. Likewise, we have also attempted to highlight the effect of soil moisture dynamics on the efficacy of various herbicides under diverse agro-ecosystems.
- Influence of Cover Crop Termination Timing on its Volunteers and Weed SuppressionKumar, Vipin (Virginia Tech, 2023-01-19)Cover crops are widely planted in the mid-Atlantic region for their environmental and agronomic benefits, but incomplete or delayed termination can lead to cover crops becoming weeds in the subsequent cash crop, known as volunteers. Studies were conducted from 2020-2022 to evaluate the effect of four commonly grown cover crop species, winter wheat (Triticum aestivum L.), cereal rye (Secale cereal L.), hairy vetch (Vicia villosa Roth), and rapeseed (Brassica napus L.), and four termination timings; 28, 14, 5, and 1 days before corn planting (DBP). Results indicated volunteerism was only an issue with rapeseed. Delaying rapeseed termination resulted in 0, 5, 12, and 22 volunteer plants m-2 at 28, 14, 5, and 1 DBP in corn. In order to manage these rapeseed volunteers, herbicide evaluations were conducted and indicated that atrazine, isoxaflutole, metribuzin, and pyroxasulfone resulted in 92-94% control when applied preemergence. Similarly, atrazine and glyphosate provided 99% rapeseed control and glufosinate resulted in 89% control when applied postemergence. Therefore, volunteers can easily be controlled with commonly used herbicides in corn. Studies were also conducted to evaluate the benefits of these cover crops, which have the potential to overcome the aforementioned risks. Results indicate that hairy vetch produced the most biomass and provided greater control of summer annual grasses, small-seeded broadleaf and large-seeded broadleaf weeds than other cover crops. Biomass accumulation and extent of weed control increased with delaying cover crop termination. Corn yield was greatest following hairy vetch and was least in rapeseed plots. Termination of cover crops 14 DBP planting increased corn yield by 12%; whereas termination at 1 DBP decreased corn yield by 15% as compared to no cover crop-no till plots. Effective termination of cover crops is an important management consideration and information on termination efficiency can help in devising management plans. In order to assist managers by evaluating cover crop termination efficiency, studies were conducted to evaluate selective and non-selective herbicides and a roller crimper for correlating vegetative indices with visible termination efficiency. Among vegetative indices, the Green Leaf Index had the highest Pearson correlation coefficient for wheat (r = -0.79, p = <0.0001) and cereal rye (r = -0.80, p = <0.0001) with visible termination efficiency. Whereas, for rapeseed, Normalized Difference Vegetation Index (NDVI) had the highest correlation coefficient (r = -0.66, p = <0.0001). However, for hairy vetch none of the vegetative indices correlated significantly with visible termination efficiency. While further research is necessary, remote sensing technologies may help in devising management plans by increasing crop scouting efficiency.
- Predicting lake surface water phosphorus dynamics using process-guided machine learningHanson, Paul C.; Stillman, Aviah B.; Jia, Xiaowei; Karpatne, Anuj; Dugan, Hilary A.; Carey, Cayelan C.; Stachelek, Joseph; Ward, Nicole K.; Zhang, Yu; Read, Jordan S.; Kumar, Vipin (2020-08-15)Phosphorus (P) loading to lakes is degrading the quality and usability of water globally. Accurate predictions of lake P dynamics are needed to understand whole-ecosystem P budgets, as well as the consequences of changing lake P concentrations for water quality. However, complex biophysical processes within lakes, along with limited observational data, challenge our capacity to reproduce short-term lake dynamics needed for water quality predictions, as well as long-term dynamics needed to understand broad scale controls over lake P. Here we use an emerging paradigm in modeling, process-guided machine learning (PGML), to produce a phosphorus budget for Lake Mendota (Wisconsin, USA) and to accurately predict epilimnetic phosphorus over a time range of days to decades. In our implementation of PGML, which we term a Process-Guided Recurrent Neural Network (PGRNN), we combine a process-based model for lake P with a recurrent neural network, and then constrain the predictions with ecological principles. We test independently the process-based model, the recurrent neural network, and the PGRNN to evaluate the overall approach. The process-based model accounted for most of the observed pattern in lake P; however it missed the long-term trend in lake P and had the worst performance in predicting winter and summer P in surface waters. The root mean square error (RMSE) for the process-based model, the recurrent neural network, and the PGRNN was 33.0 mu g P L-1, 22.7 mu g P L-1, and 20.7 mu g P L-1, respectively. All models performed better during summer, with RMSE values for the three models (same order) equal to 14.3 mu g P L-1, 10.9 mu g P L-1, and 10.7 mu g P L-1. Although the PGRNN had only marginally better RMSE during summer, it had lower bias and reproduced long-term decreases in lake P missed by the other two models. For all seasons and all years, the recurrent neural network had better predictions than process alone, with root mean square error (RMSE) of 23.8 mu g P L-1 and 28.0 mu g P L-1, respectively. The output of PGRNN indicated that new processes related to water temperature, thermal stratification, and long term changes in external loads are needed to improve the process model. By using ecological knowledge, as well as the information content of complex data, PGML shows promise as a technique for accurate prediction in messy, real-world ecological dynamics, while providing valuable information that can improve our understanding of process.
- Process-Guided Deep Learning Predictions of Lake Water TemperatureRead, Jordan S.; Jia, Xiaowei; Willard, Jared; Appling, Alison P.; Zwart, Jacob A.; Oliver, Samantha K.; Karpatne, Anuj; Hansen, Gretchen J. A.; Hanson, Paul C.; Watkins, William; Steinbach, Michael; Kumar, Vipin (2019-11-08)The rapid growth of data in water resources has created new opportunities to accelerate knowledge discovery with the use of advanced deep learning tools. Hybrid models that integrate theory with state-of-the art empirical techniques have the potential to improve predictions while remaining true to physical laws. This paper evaluates the Process-Guided Deep Learning (PGDL) hybrid modeling framework with a use-case of predicting depth-specific lake water temperatures. The PGDL model has three primary components: a deep learning model with temporal awareness (long short-term memory recurrence), theory-based feedback (model penalties for violating conversation of energy), and model pretraining to initialize the network with synthetic data (water temperature predictions from a process-based model). In situ water temperatures were used to train the PGDL model, a deep learning (DL) model, and a process-based (PB) model. Model performance was evaluated in various conditions, including when training data were sparse and when predictions were made outside of the range in the training data set. The PGDL model performance (as measured by root-mean-square error (RMSE)) was superior to DL and PB for two detailed study lakes, but only when pretraining data included greater variability than the training period. The PGDL model also performed well when extended to 68 lakes, with a median RMSE of 1.65 degrees C during the test period (DL: 1.78 degrees C, PB: 2.03 degrees C; in a small number of lakes PB or DL models were more accurate). This case-study demonstrates that integrating scientific knowledge into deep learning tools shows promise for improving predictions of many important environmental variables.
- ReaLSAT, a global dataset of reservoir and lake surface area variationsKhandelwal, Ankush; Karpatne, Anuj; Ravirathinam, Praveen; Ghosh, Rahul; Wei, Zhihao; Dugan, Hilary A.; Hanson, Paul C.; Kumar, Vipin (Nature Portfolio, 2022-06-21)Lakes and reservoirs, as most humans experience and use them, are dynamic bodies of water, with surface extents that increase and decrease with seasonal precipitation patterns, long-term changes in climate, and human management decisions. This paper presents a new global dataset that contains the location and surface area variations of 681,137 lakes and reservoirs larger than 0.1 square kilometers (and south of 50 degree N) from 1984 to 2015, to enable the study of the impact of human actions and climate change on freshwater availability. Within its scope for size and region covered, this dataset is far more comprehensive than existing datasets such as HydroLakes. While HydroLAKES only provides a static shape, the proposed dataset also has a timeseries of surface area and a shapefile containing monthly shapes for each lake. The paper presents the development and evaluation of this dataset and highlights the utility of novel machine learning techniques in addressing the inherent challenges in transforming satellite imagery to dynamic global surface water maps.
- Seed Germination Ecology of Chenopodium album and Chenopodium muraleBana, Ram Swaroop; Kumar, Vipin; Sangwan, Seema; Singh, Teekam; Kumari, Annu; Dhanda, Sachin; Dawar, Rakesh; Godara, Samarth; Singh, Vijay (MDPI, 2022-11-01)Chenopodium album L. and Chenopodium murale L. are two principal weed species, causing substantial damage to numerous winter crops across the globe. For sustainable and resource-efficient management strategies, it is important to understand weeds’ germination behaviour under diverse conditions. For the germination investigations, seeds of both species were incubated for 15 days under different temperatures (10–30 °C), salinity (0–260 mM NaCl), osmotic stress (0–1 MPa), pH (4–10), and heating magnitudes (50–200 °C). The results indicate that the germination rates of C. album and C. murale were 54–95% and 63–97%, respectively, under a temperature range of 10 to 30 °C. The salinity levels for a 50% reduction in the maximum germination (GR50) for C. album and C. murale were 139.9 and 146.3 mM NaCl, respectively. Regarding osmotic stress levels, the GR50 values for C. album and C. murale were 0.44 and 0.43 MPa, respectively. The two species showed >95% germination with exposure to an initial temperature of 75 °C for 5 min; however, seeds exposed to 100 °C and higher temperatures did not show any germination. Furthermore, a drastic reduction in germination was observed when the pH was less than 6.0 and greater than 8.0. The study generated information on the germination biology of two major weed species under diverse ecological scenarios, which may be useful in developing efficient weed management tactics for similar species in future agri-food systems.
- Zinc-Coated Urea for Enhanced Zinc Biofortification, Nitrogen Use Efficiency and Yield of Basmati Rice under Typic FluventsBana, Ramesh Chand; Gupta, Ashok K.; Bana, Ram Swaroop; Shivay, Yashbir Singh; Bamboriya, Shanti D.; Thakur, Narendra P.; Puniya, Ramphool; Gupta, Meenakshi; Jakhar, Shish Ram; Choudhary, Raj Singh; Bochalya, Ranjeet Singh; Bajaya, Tejpal; Kumar, Vipin; Kumar, Parshotam; Choudhary, Anil K. (MDPI, 2022-01)Deficiency of Zn in human diet is an emerging health issue in many developing countries across the globe. Agronomic Zn biofortification using diverse Zn fertilization options is being advised for enhancing Zn concentration in the edible portion of rice.A field study was carried out to find out the Zn fertilization effects on biofortification of basmati rice and nutrient use efficiencies in the Himalayan foothills region. Amongst the Zn nutrition treatments, 4.0% Zn-coated urea (ZnCU) + 0.2% Zn foliar spray (FS) using ZnSO4 center dot 7H(2)O recorded the highest grain (3.46 t/ha) and straw (7.93 t/ha) yield of basmati rice. On average, the rice productivity increase due to ZnCU application was ~25.4% over Commercial Urea. Likewise, the same Zn fertilization treatment also resulted in the maximum Zn (35.93 and 81.64 mg/kg) and N (1.19 and 0.45%) concentration in grain and straw of rice, respectively. Moreover, N use efficiency (NUE) was also highest when ZnCU was applied at 4.0% (ZnSO4 center dot 7H(2)O) in comparison to soil application. From the grain quality viewpoint, Zn ferti-fortification had significant effect on elongation ratio and protein concentration of grain only and respective Zn fertilization treatment recorded highest quality parameters 1.90 and 7.44%, respectively. Therefore, ZnCU would be an important low-cost and useful strategy for enhancing yield, NUE and biofortification, and also in minimizing the Zn malnutrition related challenges in human diet in many developing economies.