Improvements in biomass production: Learning lessons from the bioenergy plants maize and sorghum

Ogbaga, Chukwuma C.; Bajhaiya, Amit K.; Gupta, Sanjay

doi:10.22438/jeb/40/3/mrn-891

Cited by 7 publications

(4 citation statements)

References 42 publications

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“…Sorghum has an annual growth cycle, high calorific value, and low management cost [25], making it an efficient biofuel crop. Many studies focus on developing enhanced genotypes that can produce more energy-rich plant material (biomass) [26]. It is important for these breeding studies to predict the end-of-season yield biomass of the planted varieties as soon as possible in the growing season to screen varieties, and thus reduce investment of expensive resources in monitoring for the whole season.…”

Section: Introductionmentioning

confidence: 99%

Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

et al. 2020

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High-throughput phenotyping using high spatial, spectral, and temporal resolution remote sensing (RS) data has become a critical part of the plant breeding chain focused on reducing the time and cost of the selection process for the “best” genotypes with respect to the trait(s) of interest. In this paper, the potential of accurate and reliable sorghum biomass prediction using visible and near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral data as well as light detection and ranging (LiDAR) data acquired by sensors mounted on UAV platforms is investigated. Predictive models are developed using classical regression-based machine learning methods for nine experiments conducted during the 2017 and 2018 growing seasons at the Agronomy Center for Research and Education (ACRE) at Purdue University, Indiana, USA. The impact of the regression method, data source, timing of RS and field-based biomass reference data acquisition, and the number of samples on the prediction results are investigated. R2 values for end-of-season biomass ranged from 0.64 to 0.89 for different experiments when features from all the data sources were included. Geometry-based features derived from the LiDAR point cloud to characterize plant structure and chemistry-based features extracted from hyperspectral data provided the most accurate predictions. Evaluation of the impact of the time of data acquisition during the growing season on the prediction results indicated that although the most accurate and reliable predictions of final biomass were achieved using remotely sensed data from mid-season to end-of-season, predictions in mid-season provided adequate results to differentiate between promising varieties for selection. The analysis of variance (ANOVA) of the accuracies of the predictive models showed that both the data source and regression method are important factors for a reliable prediction; however, the data source was more important with 69% significance, versus 28% significance for the regression method.

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Section: Introductionmentioning

confidence: 99%

Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

et al. 2020

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“…This explains the low values of growth parameters observed under the fertilizer rate of 0 kg N ha −1 . An increase in the number of leaves is associated with an increase in leaf surface area with increasing population density is associated with effective light interception [17], greater photosynthetic output per unit area and the observed greater dry biomass production [18]. The high number of sorghum seeds observed under the 90 kg N ha −1 fertilized field (Figure 11) is due to the high number of florets per panicle observed under the 90 kg ha −1 field (Figure 10).…”

Section: Discussionmentioning

confidence: 92%

Growth, Yield and Yield Components Response of Local Sorghum Varieties to Nitrogenous Fertilizer Rates in Northern Ghana

Kugbe

2019

WJASS

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The economic importance of Sorghum (sorghum bicolor L. Moench) to rural households and household food security in northern Ghana, remains a key reason for the continues cultivation of the crop, though average yield (700 kg/ha) is below that of maize and rice. The low yield, however, implies that sorghum could contribute more to food supplies than at present, especially in resource-constrained regions and people in greatest need. The crop has been referred to as one with vast untapped potential [1,2]. Across northern Ghana the crop is preferred by farmers because of the relatively dry agricultural environment. Sorghum is an extremely drought tolerant crop and has an ability to survive and yield grain during continuous or intermittent drought stress [3]. As a result, the crop has been and is still an important staple in most Semi-Arid Tropics (SAT) of Asia and Africa [4]. In Africa, sorghum has wider geographical adaptation than any other cereal growing from lowlands to highlands [5].Though adapted to the arid climates of northern Ghana, productivity is by far below its potential. In areas where sorghum is commonly grown, yields of more than 3000 to 4000 kg ha -1 are obtained under better conditions, dropping down to 300 to 1000 kg

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“…In a study on soil porosity and water infiltration as influenced by tillage methods, Lipic et al (2005) reported improved infiltration of rainwater into the soil in artificial rain water harvesting soil surface structures that increase water availability to sorghum plants. In a study on improvement of biomass production Ogbaga et al (2019) reported increased biomass accumulations with increasing soil water availability.…”

Section: Effect Of In-field Water Harvesting Tillage Methods On Soil Water Dynamicsmentioning

confidence: 99%

Soil moisture, dryland sorghum (Sorghum bicolor L.) growth and grain yield responses to in-field rain water harvesting tillage methods

Masaka

Chohunoita²,

Mupfiga

2021

Cogent Food & Agriculture

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Dryland Sorghum, which is the fifth most important cereal crop in the world, is grown in Zimbabwe typically as a rain-fed crop in areas receiving low, erratic and highly variable rainfall. A two-season field trial was carried out at Save Experiment Station (20°24′S; 28°29′E) in Zimbabwe to establish soil water storage, selected biometric parameters of dryland grain sorghum responses to in-field rainwater harvesting tillage methods. Deep basin tillage method significantly increased the soil moisture content by 14.6% (62.5 mm); 6.2 % (26.5 mm) above that on the ridges and in furrows of open and closed end tied ridges, respectively. Soil moisture storage in deep basins, on ridges and in furrows of open and closed end tied ridges exceeded that in conventional tillage plots by 98.0 mm (29.8%); 35.5 mm (10.8%) and 69.5 mm (21.1%), respectively. Consequently, sorghum plant biometric growth parameters in deep basin tillage treatments were significantly higher than those in conventional, open-and close-ended tied ridges tillage plots. However, the use of conservation tillage methods where tied end ridges are opened introduces ABOUT THE AUTHORS Professor Johnson Masaka is a PhD holder who specialized in Plant Mineral Nutrition. He is currently employed as the Executive Dean of the Faculty of Agriculture, Environment and Natural Resources Management of the Midlands State University. His research focuses on soil and water conservation; greenhouse gas emissions from cultivated wetlands and land cover changes. Mr. Collen Chohunoita is an agronomist/plant scientist who holds a BSc Honors degree in Agronomy. He is currently employed as a senior research officer in the Government Department of Research and Specialist Services. He has researched extensively on tillage systems for rain water harvesting in dry areas of Zimbabwe. Mr. Elvis Mupfiga is an irrigation engineer/soil and water conservation specialist with a BSc Honors degree in Irrigation Engineering; MSc in Integrated Water Resources Management and MSc in GIS and RS. His research focuses mostly on soil and water conservation in semi arid regions of Zimbabwe.

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Improvements in biomass production: Learning lessons from the bioenergy plants maize and sorghum

Cited by 7 publications

References 42 publications

Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

Growth, Yield and Yield Components Response of Local Sorghum Varieties to Nitrogenous Fertilizer Rates in Northern Ghana

Soil moisture, dryland sorghum (Sorghum bicolor L.) growth and grain yield responses to in-field rain water harvesting tillage methods

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