Grain yield variability and stability of corn varieties in rainfed areas in the Philippines

2021 ◽  
Author(s):  
Anecito M. Anuada ◽  
Pompe C. Sta. Cruz ◽  
Lucille Elna P. De Guzman ◽  
Pearl B. Sanchez
Keyword(s):  
Grain Yield ◽  
The Philippines ◽  
Yield Variability

Genetic Dissection of Epistatic Interactions Contributing Grain Yield Variability in Rice under Drought

2021 ◽  
Vol 22 ◽  
Author(s):  
Ratna Rani Majumder ◽  
Nitika Sandhu ◽  
Shailesh Yadav ◽  
Margaret Catolos ◽  
Ma. Teresa Sta. Cruz ◽  
...  
Keyword(s):  
Drought Stress ◽  
Grain Yield ◽  
Genetic Background ◽  
Reproductive Stage ◽  
Yield Variability ◽  
Marker Interval ◽  
Negative Interaction ◽  
Epistatic Interactions ◽  
Lattice Design ◽  
Drought Tolerant

Aims: The aim of the present study was to evaluate the performance of ‘high’-‘low’ yielding pyramided lines (PLs) with the same combinations of qDTYs in Samba Mahsuri, MR219 and IR64-Sub1 genetic backgrounds and understand the genetic interactions of QTL and with genetic background affecting grain yield. Background: Epistasis regulates the expression of traits governed by several major/minor genes/QTL. Multiple pyramided lines (PLs) with the same grain yield QTL (qDTYs) combinations but possessing grain yield variability under different levels of reproductive stage drought stress were identified in different rice genetic backgrounds at International Rice Research Institute (IRRI). Objectives: The objectives of the present study were to evaluate the performance pyramided lines (PLs) with drought QTL in the backgrounds of Samba Mahsuri, MR219 and IR64-Sub1 under reproductive stage drought stress (RS) and NS (non-stress) conditions ii) to understand the effect of epistatic interactions of qDTYs and with genetic background on GY under the differential level of stress iii) to identify the promising drought-tolerant lines with high yield under drought and higher background recovery in different genetic backgrounds. Results: Several digenic interactions were found in different genetic backgrounds, 13 interactions in Samba Mahsuri, 11 in MR219 and 20 in IR64-Sub1 backgrounds. Among all digenic interactions, one QTL × QTL interaction, 17 QTL × background and 26 background × background interactions resulted in GY reduction in low yielding PLs in different genetic backgrounds under LSS or LMS. Negative interaction of qDTY3.1, qDTY4.1 and qDTY9.1 with background markers and background × background interactions caused up to 15% GY reduction compared to the high yielding PLs under LMS in the Samba Mahsuri PLs. In MR219 PLs, the negative interaction of qDTY2.2, qDTY3.2, qDTY4.1 and qDTY12.1 with the background marker interval RM314-RM539, RM273-RM349 and RM445-RM346, RM473D-RM16, respectively resulted in 52% GY reduction compared to the high yielding PLs under LSS. In IR64-Sub1 PLs, qDTY6.1 interacted with background loci at RM16-RM135, RM228-RM333, RM202-RM287 and RM415-RM558A marker interval under LSS; and at RM475-RM525 marker interval under LMS, causing GY reduction to 58% compared to the high yielding PLs. Conclusion: High yielding PLs in Samba Mahsuri (IR 99734:1-33-69-1-22-6), MR219 (IR 99784-156-87-2-4-1) and IR64-Sub1 (IR 102784:2-89-632-2-1-2) backgrounds without any negative interactions were identified. The identified selected promising PLs may be used as potential drought-tolerant donors or may be released as varieties for drought-prone ecosystems in different countries. Methods: The experiments were conducted in 2015DS (dry season), 2015WS (wet season) and 2017 DS at IRRI, Los Baños, Philippines, in a transplanted lowland ecosystem under lowland severe stress (LSS), moderate lowland stress (LMS) and lowland non-stress (LNS). The experiments were laid out in alpha lattice design with two replications.

Grain Yield of Rice Cultivars and Lines Developed in the Philippines since 1966

Crop Science ◽  
2000 ◽  
Vol 40 (2) ◽  
pp. 307-314 ◽  
Author(s):  
S. Peng ◽  
R.C. Laza ◽  
R.M. Visperas ◽  
A.L. Sanico ◽  
K.G. Cassman ◽  
...  
Keyword(s):  
Grain Yield ◽  
The Philippines ◽  
Rice Cultivars

Simulation of Maize Grain Yield Variability within a Surface-Irrigated Field

2001 ◽  
Vol 93 (4) ◽  
pp. 773-782 ◽  
Author(s):  
Jose Cavero ◽  
Enrique Playán ◽  
Nery Zapata ◽  
Jose M. Faci
Keyword(s):  
Grain Yield ◽  
Yield Variability ◽  
Maize Grain Yield ◽  
Maize Grain

Climate and global grain yield variability

1980 ◽  
Vol 2 (4) ◽  
pp. 349-361 ◽  
Author(s):  
C. Sakamoto ◽  
S. Leduc ◽  
N. Strommen ◽  
L. Steyaert
Keyword(s):  
Grain Yield ◽  
Yield Variability

scholarly journals Identification of an Elite Core Panel as a Key Breeding Resource to Accelerate the Rate of Genetic Improvement for Irrigated Rice

2021 ◽  
Author(s):  
Roselyne U. Juma ◽  
Jérôme Bartholomé ◽  
Parthiban Thathapalli Prakash ◽  
Waseem Hussain ◽  
John Damien Platten ◽  
...  
Keyword(s):  
Grain Yield ◽  
Genetic Gain ◽  
Genetic Improvement ◽  
Critical Role ◽  
The Philippines ◽  
Breeding Program ◽  
Breeding Cycle ◽  
Relationship Matrix ◽  
Irrigated Rice ◽  
Breeding Values

Abstract Rice genetic improvement is a key component of achieving and maintaining food security in Asia and Africa in the face of growing populations and climate change. In this effort, the International Rice Research Institute (IRRI) continues to play a critical role in creating and disseminating rice varieties with higher productivity. Due to increasing demand for rice, especially in Africa, there is a strong need to accelerate the rate of genetic improvement for grain yield.In an effort to identify and characterize the elite breeding pool of IRRI’s irrigated rice breeding program, we analyzed 102 historical yield trials conducted in the Philippines during the period 2012-2016 and representing 15,286 breeding lines (including released varieties). A mixed model approach based on the pedigree relationship matrix was used to estimate breeding values for grain yield, which ranged from 2.12 to 6.27 t·ha-1. The rate of genetic gain for grain yield was estimated at 8.75 kg·ha-1·year-1 (0.23%) for crosses made in the period from 1964 to 2014. Reducing the data to only IRRI released varieties, the rate doubled to 17.36 kg·ha-1·year-1 (0.46%). Regressed against breeding cycle the rate of gain for grain yield was 185 kg·ha-1·cycle-1 (4.95%). We selected 72 top performing lines based on breeding values for grain yield to create an elite core panel (ECP) representing the genetic diversity in the breeding program with the highest heritable yield values from which new products can be derived. The ECP closely aligns with the indica 1B sub-group of Oryza sativa that includes most modern varieties for irrigated systems. Agronomic performance of the ECP under multiple environments in Asia and Africa confirmed its high yield potential.We found that the rate of genetic gain for grain yield found in this study was limited primarily by long cycle times and the direct introduction of non-improved material into the elite pool. Consequently, the current breeding scheme for irrigated rice at IRRI is based on rapid recurrent selection among highly elite lines. In this context, the ECP constitutes an important resource for IRRI and NAREs breeders to carefully characterize and manage that elite diversity.

scholarly journals Season Affects Yield and Metabolic Profiles of Rice (Oryza sativa) under High Night Temperature Stress in the Field

2020 ◽  
Vol 21 (9) ◽  
pp. 3187 ◽  
Author(s):  
Stephanie Schaarschmidt ◽  
Lovely Mae F. Lawas ◽  
Ulrike Glaubitz ◽  
Xia Li ◽  
Alexander Erban ◽  
...  
Keyword(s):  
Oryza Sativa ◽  
Grain Yield ◽  
Climatic Factors ◽  
Global Climate ◽  
The Philippines ◽  
Gas Chromatography Mass Spectrometry ◽  
Night Temperature ◽  
N Assimilation ◽  
Negative Effect ◽  
Main Food Source

Rice (Oryza sativa) is the main food source for more than 3.5 billion people in the world. Global climate change is having a strong negative effect on rice production. One of the climatic factors impacting rice yield is asymmetric warming, i.e., the stronger increase in nighttime as compared to daytime temperatures. Little is known of the metabolic responses of rice to high night temperature (HNT) in the field. Eight rice cultivars with contrasting HNT sensitivity were grown in the field during the wet (WS) and dry season (DS) in the Philippines. Plant height, 1000-grain weight and harvest index were influenced by HNT in both seasons, while total grain yield was only consistently reduced in the WS. Metabolite composition was analysed by gas chromatography-mass spectrometry (GC-MS). HNT effects were more pronounced in panicles than in flag leaves. A decreased abundance of sugar phosphates and sucrose, and a higher abundance of monosaccharides in panicles indicated impaired glycolysis and higher respiration-driven carbon losses in response to HNT in the WS. Higher amounts of alanine and cyano-alanine in panicles grown in the DS compared to in those grown in the WS point to an improved N-assimilation and more effective detoxification of cyanide, contributing to the smaller impact of HNT on grain yield in the DS.

scholarly journals ­­­Can Combination of UAV-derived Vegetation Indices with Biophysical Variable(s) Improve Yield Variability Assessment in Smallholder Farms?

2020 ◽  
Author(s):  
Julius Adewopo ◽  
Helen Peter ◽  
Alpha Kamara ◽  
Ibrahim Mohammed ◽  
Bernard Vanlauwe ◽  
...  
Keyword(s):  
Grain Yield ◽  
Vegetation Index ◽  
Management Practices ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Yield Variability ◽  
Spatial And Temporal Variability ◽  
Experimental Conditions ◽  
Smallholder Farms ◽  
Maize Genotypes

Rapid assessment of maize yields in smallholder farming system is important to understand its spatial and temporal variability and for timely agronomic decision-support. Imageries acquired with unmanned air vehicles (UAV) offer opportunity to assess agronomic variables at field scale, however, it is not clear if this can be translated into reliable yield assessment on smallholder farms where field conditions, maize genotypes, and management practices vary within short distances. This study was conducted to assess the predictability of maize grain yield using UAV-derived vegetation indices (VI), with(out) biophysical variables, in smallholder farms. High-resolution images were acquired with UAV-borne multispectral sensor at 4 and 8 weeks after sowing (WAS) on 31 farmers’ managed fields (FMFs) and 12 nearby Nutrient Omission Trials (NOT), all distributed across 5 locations within the core maize region of Nigeria. The NOTs included non-fertilized and fertilized plots (with and without micronutrients), sown with open pollinated or hybrid maize genotypes. Acquired multispectral images were post-processed into several three (s) vegetation indices (VIs), normalized difference vegetation index (NDVI), normalized difference red-edge (NDRE), green-normalized difference vegetation index (GNDVI). Biophysical variables, plant height (Ht) and percent canopy cover (CC), were measured with the georeferenced plot locations recorded. In the NOTs, the nutrient status, not genotype, influenced the grain yield variability and outcome. The maximum grain yield observed in NOTs was 9.3 tha-1, compared to 5.4 tha-1 in FMF. Without accounting for between- and within-field variations, there was no relationship between UAV-derived VIs and grain yield at 4WAS (r<0.02, P>0.1), but significant correlations were observed at 8WAS (r≤0.3; p<0.001). Ht was positively correlated with grain yield at 4WAS (r=0.5, R2=0.25, p<0.001), and more strongly at 8WAS (r=0.7, R2=0.55, p<0.001), while relationship between CC and yield was only significant at 8WAS. By accounting for within- and between-field variations in NOTs and FMF (separately) through linear mixed-effects modeling, predictability of grain yield from UAV-derived VIs was generally (R2≤0.24), however, the inclusion of ground-measured biophysical variable (mainly Ht) improved the explained yield variability (R2 ≥0.62, RMSEP≤0.35) in NOTs but not in FMF. We conclude that yield prediction with UAV-acquired imageries (before harvest) is more reliable under controlled experimental conditions (NOTs), compared to actual farmer-managed fields where various confounding agronomic factors can amplify noise-signal ratio.

Forecasting Model of Wheat Yield in Relation to Rainfall Variability in North Africa Countries

2019 ◽  
Vol 4 (2) ◽  
pp. 1-17
Author(s):  
Ibrahim M. A. Soliman
Keyword(s):  
Grain Yield ◽  
North Africa ◽  
Rainfall Variability ◽  
Wheat Yield ◽  
Yield Variability ◽  
Forecasting Model ◽  
Representative Case ◽  
Farm Price ◽  
Rainfall Variations

The study investigated the effect of rainfall variations on wheat yield in Morocco as a representative case study of North Africa region. The data were collected for the period 2004– 2015 from 12 meteorological stations. The wheat yield variability range was 79.5%-38.0%. It increased in poor-rain years and the regions of precipitation ≤ 350 mm. The wheat yield showed more significant response to monthly perception changes than the annual. The estimated forecasting model showed that March's rain was the critical month for wheat yield as the elasticity of production was 0.587. April and May showed an elasticity of 0.011 and 0.023, respectively. The estimated response of wheat farm price to grain yield showed that 10% increase in wheat yield would decrease the farm gate price by 4.1%, i.e. poor rainy seasons mean income foregone with the loss of inputs expenses and expansion in imported wheat. A country buffer stock, a regional strategic stock of wheat and supplementary water for irrigation in poor precipitation years are required.

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