scholarly journals Evaluating Maize Genotype Performance under Low Nitrogen Conditions Using RGB UAV Phenotyping Techniques

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1815 ◽  
Author(s):  
Ma. Luisa Buchaillot ◽  
Adrian Gracia-Romero ◽  
Omar Vergara-Diaz ◽  
Mainassara A. Zaman-Allah ◽  
Amsal Tarekegne ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Grain Yield ◽  
Vegetation Index ◽  
Yield Potential ◽  
Plant Phenotyping ◽  
Phenotypic Traits ◽  
Optimal Conditions ◽  
Maize Varieties ◽  
Low Nitrogen ◽  
Nitrogen Conditions

Maize is the most cultivated cereal in Africa in terms of land area and production, but low soil nitrogen availability often constrains yields. Developing new maize varieties with high and reliable yields using traditional crop breeding techniques in field conditions can be slow and costly. Remote sensing has become an important tool in the modernization of field-based high-throughput plant phenotyping (HTPP), providing faster gains towards the improvement of yield potential and adaptation to abiotic and biotic limiting conditions. We evaluated the performance of a set of remote sensing indices derived from red–green–blue (RGB) images along with field-based multispectral normalized difference vegetation index (NDVI) and leaf chlorophyll content (SPAD values) as phenotypic traits for assessing maize performance under managed low-nitrogen conditions. HTPP measurements were conducted from the ground and from an unmanned aerial vehicle (UAV). For the ground-level RGB indices, the strongest correlations to yield were observed with hue, greener green area (GGA), and a newly developed RGB HTPP index, NDLab (normalized difference Commission Internationale de I´Edairage (CIE)Lab index), while GGA and crop senescence index (CSI) correlated better with grain yield from the UAV. Regarding ground sensors, SPAD exhibited the closest correlation with grain yield, notably increasing in its correlation when measured in the vegetative stage. Additionally, we evaluated how different HTPP indices contributed to the explanation of yield in combination with agronomic data, such as anthesis silking interval (ASI), anthesis date (AD), and plant height (PH). Multivariate regression models, including RGB indices (R2 > 0.60), outperformed other models using only agronomic parameters or field sensors (R2 > 0.50), reinforcing RGB HTPP’s potential to improve yield assessments. Finally, we compared the low-N results to the same panel of 64 maize genotypes grown under optimal conditions, noting that only 11% of the total genotypes appeared in the highest yield producing quartile for both trials. Furthermore, we calculated the grain yield loss index (GYLI) for each genotype, which showed a large range of variability, suggesting that low-N performance is not necessarily exclusive of high productivity in optimal conditions.

In-Season Prediction of Corn Grain Yield Potential Using Normalized Difference Vegetation Index

2006 ◽  
Vol 98 (6) ◽  
pp. 1488-1494 ◽  
Author(s):  
R. K. Teal ◽  
B. Tubana ◽  
K. Girma ◽  
K. W. Freeman ◽  
D. B. Arnall ◽  
...  
Keyword(s):  
Grain Yield ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Yield Potential ◽  
Corn Grain ◽  
Corn Grain Yield

scholarly journals Genetic Dissection of Grain Yield and Agronomic Traits in Maize under Optimum and Low-Nitrogen Stressed Environments

2020 ◽  
Vol 21 (2) ◽  
pp. 543 ◽  
Author(s):  
Berhanu Tadesse Ertiro ◽  
Michael Olsen ◽  
Biswanath Das ◽  
Manje Gowda ◽  
Maryke Labuschagne
Keyword(s):  
Grain Yield ◽  
Agronomic Traits ◽  
Genotyping By Sequencing ◽  
Genetic Dissection ◽  
Common Qtl ◽  
Chromosome Position ◽  
Maize Varieties ◽  
Dh Lines ◽  
Genomic Regions ◽  
Low Nitrogen

Understanding the genetic basis of maize grain yield and other traits under low-nitrogen (N) stressed environments could improve selection efficiency. In this study, five doubled haploid (DH) populations were evaluated under optimum and N-stressed conditions, during the main rainy season and off-season in Kenya and Rwanda, from 2014 to 2015. Identifying the genomic regions associated with grain yield (GY), anthesis date (AD), anthesis-silking interval (ASI), plant height (PH), ear height (EH), ear position (EPO), and leaf senescence (SEN) under optimum and N-stressed environments could facilitate the use of marker-assisted selection to develop N-use-efficient maize varieties. DH lines were genotyped with genotyping by sequencing. A total of 13, 43, 13, 25, 30, 21, and 10 QTL were identified for GY, AD ASI, PH, EH, EPO, and SEN, respectively. For GY, PH, EH, and SEN, the highest number of QTL was found under low-N environments. No common QTL between optimum and low-N stressed conditions were identified for GY and ASI. For secondary traits, there were some common QTL for optimum and low-N conditions. Most QTL conferring tolerance to N stress was on a different chromosome position under optimum conditions.

scholarly journals Assessment of Genetic Diversity for Drought, Heat and Combined Drought and Heat Stress Tolerance in Early Maturing Maize Landraces

Plants ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 518 ◽  
Author(s):  
Nelimor ◽  
Badu-Apraku ◽  
Tetteh ◽  
N’guetta
Keyword(s):  
Climate Change ◽  
Heat Stress ◽  
Drought Stress ◽  
Grain Yield ◽  
Yield Potential ◽  
Yield Reduction ◽  
International Institute ◽  
Improvement Program ◽  
Maize Varieties ◽  
Drought And Heat Stress

Climate change is expected to aggravate the effects of drought, heat and combined drought and heat stresses. An important step in developing ‘climate smart’ maize varieties is to identify germplasm with good levels of tolerance to the abiotic stresses. The primary objective of this study was to identify landraces with combined high yield potential and desirable secondary traits under drought, heat and combined drought and heat stresses. Thirty-three landraces from Burkina Faso (6), Ghana (6) and Togo (21), and three drought-tolerant populations/varieties from the Maize Improvement Program at the International Institute of Tropical Agriculture were evaluated under three conditions, namely managed drought stress, heat stress and combined drought and heat stress, with optimal growing conditions as control, for two years. The phenotypic and genetic correlations between grain yield of the different treatments were very weak, suggesting the presence of independent genetic control of yield to these stresses. However, grain yield under heat and combined drought and heat stresses were highly and positively correlated, indicating that heat-tolerant genotypes would most likely tolerate combined drought and stress. Yield reduction averaged 46% under managed drought stress, 55% under heat stress, and 66% under combined drought and heat stress, which reflected hypo-additive effect of drought and heat stress on grain yield of the maize accessions. Accession GH-3505 was highly tolerant to drought, while GH-4859 and TZm-1353 were tolerant to the three stresses. These landrace accessions can be invaluable sources of genes/alleles for breeding for adaptation of maize to climate change.

MAIZE YIELD DETERMINANTS IN FARMER-MANAGED TRIALS IN THE NIGERIAN NORTHERN GUINEA SAVANNA

1998 ◽  
Vol 34 (4) ◽  
pp. 407-422 ◽  
Author(s):  
R. J. CARSKY ◽  
S. NOKOE ◽  
S. T. O. LAGOKE ◽  
S. K. KIM
Keyword(s):  
Grain Yield ◽  
Stepwise Regression ◽  
Maize Yield ◽  
Yield Potential ◽  
Local Varieties ◽  
Current Season ◽  
Damage Score ◽  
Northern Guinea Savanna ◽  
Guinea Savanna ◽  
Maize Varieties

Farmer-managed tests of Striga hermonthica-resistant maize varieties were conducted in 1994 in a moderately intensified zone in the northern Guinea savanna of Nigeria. Field history, soil properties, current season fertility management, and crop management observations were recorded for 37 farmer-managed trials. Site averages for maize grain yield varied from 300 to 4000 kg grain ha−1. In spite of the tremendous variability observed, the grain yield was significantly higher for the striga-resistant hybrid 8321-18 compared with an improved open-pollinated variety, STR Syn-W, and the farmers' current variety. Correlation analysis and stepwise regression analysis of grain yield on measured variables suggested that maize yield was a function of plant density for all three varieties. The rate of nitrogen fertilizer application was an important variable only for the hybrid, while the day of first weeding was most important for the improved varieties. The yield of the local varieties and STR Syn-W was related to the number of emerged striga at harvest in the stepwise regression, and the yield of the local varieties was highly correlated with the striga-damage score on maize. The striga-damage score was significantly lower on 8321-18 than on the other varieties, suggesting some degree of resistance in the hybrid. The number of emerged striga was lower for the hybrid but not significantly different. Farmers were almost unanimous in ranking the hybrid as least damaged by striga and highest yielding. Besides being related to maize variety, striga-damage score was lower if crop residue was observed on the field at the time of site confirmation. Highest yields (approximately 4 t ha−1) were recorded on fields near the homestead (compound fields) where soil organic carbon values were 2.0–2.5%. Realization of maize yield potential in the absence of manure or fertilizer will only be possible on long-term compound fields. Striga-resistant maize can maintain high yields under S. hermonthica infestation.

scholarly journals Determining nitrogen deficiencies for maize using various remote sensing indices

2022 ◽  
Author(s):  
Brayden W. Burns ◽  
V. Steven Green ◽  
Ahmed A. Hashem ◽  
Joseph H. Massey ◽  
Aaron M. Shew ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Grain Yield ◽  
Nitrogen Fertilizer ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Nitrogen Deficiency ◽  
Maize Grain Yield ◽  
Nitrogen Inputs ◽  
Chlorophyll Index ◽  
Maize Grain

AbstractDetermining a precise nitrogen fertilizer requirement for maize in a particular field and year has proven to be a challenge due to the complexity of the nitrogen inputs, transformations and outputs in the nitrogen cycle. Remote sensing of maize nitrogen deficiency may be one way to move nitrogen fertilizer applications closer to the specific nitrogen requirement. Six vegetation indices [normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), red-edge normalized difference vegetation index (RENDVI), triangle greenness index (TGI), normalized area vegetation index (NAVI) and chlorophyll index-green (CIgreen)] were evaluated for their ability to detect nitrogen deficiency and predict grain maize grain yield. Strip trials were established at two locations in Arkansas, USA, with nitrogen rate as the primary treatment. Remote sensing data was collected weekly with an unmanned aerial system (UAS) equipped with a multispectral and thermal sensor. Relationships among index value, nitrogen fertilizer rate and maize growth stage were evaluated. Green NDVI, RENDVI and CIgreen had the strongest relationship with nitrogen fertilizer treatment. Chlorophyll Index-green and GNDVI were the best predictors of maize grain yield early in the growing season when the application of additional nitrogen was still agronomically feasible. However, the logistics of late season nitrogen application must be considered.

scholarly journals Trait-QTL-heritability of grain yield and other agronomic traits under low nitrogen conditions in bi-parental maize populations

2021 ◽  
Vol 2 (1) ◽  
pp. 096-116
Author(s):  
Collins Kimutai ◽  
Manje Gowda ◽  
Oliver Kiplagat
Keyword(s):  
Grain Yield ◽  
Complex Traits ◽  
Agronomic Traits ◽  
Complex Nature ◽  
Maize Crop ◽  
Maize Populations ◽  
Solution Mapping ◽  
Genomic Regions ◽  
Low Nitrogen ◽  
Nitrogen Conditions

Limited or low Nitrogen is a wanting abiotic stress in maize mainly in Sub-Sahara Africa, affecting yields and quality development of maize crop. As an approach to getting a breeding solution; mapping of QTLs and understanding the heritability factor can provide useful information and guide for breeders in developing low nitrogen resilient maize. QTL mapping which is a molecular breeding component forms an actual basis in estimation of genomic regions associated to the expression of quantitative traits, and how heritable are such traits. Conducting a selection for Low N-tolerance is challenging due to its complex nature with strong interaction between genotypes and environments; therefore, marker assisted breeding is key to improving such complex traits, but at the same time requires markers associated with the trait of interest. In this study, three bi-parental populations were subjected to either or both low and optimum N conditions to detect and determine the QTLs heritability for grain yield and other agronomic traits. Essential to the study; genotype by environmental interaction, significance and heritability was examined for each population with most traits expressing low (<0.2) and moderate to high heritabilities (0.3>). These QTLs with high heritabilities across environments will be of great value for rapid introgression into maize populations using marker assisted selection approach. The study was a preliminary and therefore require further validation on heritability and fine mapping for them to be useful in MAS.

Prediction of crop productivity and evapotranspiration with two photosynthetic parameter regionalization methods

2012 ◽  
Vol 152 (1) ◽  
pp. 119-133 ◽  
Author(s):  
S. HU ◽  
X. MO
Keyword(s):  
Remote Sensing ◽  
Grain Yield ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Census Data ◽  
Wheat Yield ◽  
Ecosystem Model ◽  
Crop Productivity ◽  
Photosynthetic Parameter ◽  
Area Index

SUMMARYParameter regionalization is the foundation for the spatial application of an ecosystem model at the canopy level and has been improved greatly by remote sensing (RS). Photosynthetic rate is restricted by the carboxylation rate, which is limited by the activity of the enzyme Rubisco. By including RS normalized difference vegetation index (NDVI) and census data of grain yield at the county level in an ecosystem model (vegetation interface processes (VIP) model), the pattern of photosynthetic parameter Vcmax (maximum catalytic activity of Rubisco) of winter wheat was obtained and then used to simulate the wheat yield and evapotranspiration (ET) in the North China Plain (referred to as the Vcmax method). To evaluate its performance, the simulated yield and ET were compared with those derived by the leaf area index (LAI) method using the retrieved LAI from NDVI to drive the VIP model. The results showed that the Vcmax method performed better than the LAI method in highly productive fields, while the LAI method described the inter-annual variations of yield more favourably in fields with low productivity. Over the study area, average yield (4520 kg/ha) and seasonal ET (360 mm) simulated by the LAI method was slightly lower than those simulated using the Vcmax method (4730 kg/ha for yield and 372 mm for ET). Compared with the census data of yield, the relative root mean square error (RMSE) of grain yield with Vcmax method (0·17) was lower than that of the LAI method (0·20). In conclusion, the physical model with spatial Vcmax pattern from remote sensing is reliable for regional crop productivity prediction.

Response of early maturing maize landraces and improved varieties to moisture deficit and sufficient water supply

2009 ◽  
Vol 7 (03) ◽  
pp. 205-215 ◽  
Author(s):  
Abebe Menkir ◽  
Baffour Badu-Apraku ◽  
Sam Ajala ◽  
Alpha Kamara ◽  
Abdou Ndiaye
Keyword(s):  
Water Supply ◽  
Grain Yield ◽  
Rank Order ◽  
Yield Potential ◽  
Maize Production ◽  
Improved Varieties ◽  
Drought Tolerant ◽  
Moisture Deficit ◽  
Maize Varieties ◽  
Early Maturing

In drought-affected maize production zones with short growing periods, the development and use of early maturing drought-tolerant cultivars can stabilize maize production. We evaluated 10 improved and 25 farmers' early maturing maize varieties under moisture deficit and well-watered conditions for 2 years to identify suitable genetic materials for breeding drought-tolerant cultivars. The varieties exhibited significant differences in grain yield and other traits under both moisture deficit and well-watered conditions. Changes in the rank order of the varieties for grain yield was not significant across the different levels of moisture supply in this study. Grain yield was significantly correlated with days to anthesis, days to silking, plant height, ear height, ear number and anthesis–silking interval (ASI) under the two irrigation treatments and with leaf death scores under moisture deficit, suggesting that the common traits were beneficial in maximizing grain yield under both sufficient water supply and moisture deficit. Grain yield and the traits significantly correlated with it differentiated the early maturing maize varieties into two distinct groups under well-watered condition and moisture deficit. The improved varieties were superior to the farmers' varieties in grain yield and other traits under moisture deficit, possibly due to selection of their progenitors for improved performance in multiple locations. We found some farmers' and improved varieties with similar yield potential and flowering time under well-watered conditions but with marked differences in grain yield and other traits under moisture deficit. Use of such promising landraces that would also be invaluable sources of desirable farmers-preferred end-use quality traits in combination with promising improved varieties as breeding materials could enhance the genetic grain from selection for drought tolerance in early maize.

scholarly journals Productivity of maize (Zea mays L.) as affected by varieties and sowing dates

2018 ◽  
Vol 2 (2) ◽  
Author(s):  
Dil Bahadur Gurung ◽  
Balram Bhandari ◽  
Jiban Shrestha ◽  
Mahendra Prasad Tripathi
Keyword(s):  
Zea Mays ◽  
Grain Yield ◽  
Research Program ◽  
Zea Mays L ◽  
Sowing Date ◽  
Research Field ◽  
Yield Potential ◽  
Grain Production ◽  
Sowing Dates ◽  
Maize Varieties

Genotypic yield potential of maize varieties is greatly affected by sowing dates. In order to investigate the effects of sowing dates and varieties on the grain yield of maize, the field experiment was carried out at research field of National Maize Research Program (NMRP), Rampur, Chitwan, Nepal from April 2009 to March 2010.  Three varieties namely Rampur Composite, Arun-2 and Gaurav were sown at every week. The results of experiment showed that interaction effect of variety and sowing date on grain yield of maize was significant. Rampur Composite produced highest grain yield (6.1 t/ha) in August and lowest yield (2.6 t/ha) in May. Similarly Arun-2 produced highest yield (4.6 t/ha) in August and lowest yield (2.1 t/ha) in May. Gaurav produced highest grain yield (5.1 t/ha) in September followed by 4.9, 4.8 and 4.6 t/ha in February, July and August respectively and lowest yield (1.5 t/ha) in November. The sowing date was highly significant on grain production. The highest grain production was 5.1 t/ha in August followed by in February (4.9 t/ha), September (4.6 t/ha) and March (4.4 t/ha) respectively. The lowest grain yield was produced in May (2.4 t/ha). Therefore it was concluded that August planting was best for higher grain production of maize varieties (Rampur Composite, Arun-2 and Gaurav) in terai region of Nepal.

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