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

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.

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

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.

scholarly journals Night Temperature Determines the Interannual Yield Variation in Hybrid and Inbred Rice Widely Used in Central China Through Different Effects on Reproductive Growth

2021 ◽  
Vol 12 ◽  
Author(s):  
Liying Huang ◽  
Fei Wang ◽  
Yi Liu ◽  
Yunbo Zhang
Keyword(s):  
Grain Yield ◽  
Management Practices ◽  
Climate Models ◽  
Grain Filling ◽  
Grain Weight ◽  
Central China ◽  
Daily Minimum Temperature ◽  
Yield Variability ◽  
Night Temperature ◽  
Yield Variation

Interannual variation in grain yield of rice has been observed at both farm and regional scales, which is related to the climate variability. Previous studies focus on predicting the trend of climate change in the future and its potential effects on rice production using climate models; however, field studies are lacking to examine the climatic causes underlying the interannual yield variability for different rice cultivars. Here a 6-year field experiment from 2012 to 2017 was conducted using one hybrid (Yangliangyou6, YLY6) cultivar and one inbred (Huanghuazhan, HHZ) cultivar to determine the climate factors responsible for the interannual yield variation. A significant variation in grain yield was observed for both the inbred and hybrid cultivars across six planting years, and the coefficient of variation for grain yield was 7.3–10.5%. The night temperature (average daily minimum temperature, Tmin) contributed to the yield variability in both cultivars. However, the two cultivars showed different responses to the change in Tmin. The yield variation in HHZ was mainly explained by the effects of Tmin on grain filling percentage and grain weight, while the change in spikelets m−2 in response to Tmin accounted for the yield variability in YLY6. Further analysis found that spikelets m−2 of YLY6 significantly and negatively correlated with Tmin from transplanting to heading. For HHZ, the grain filling percentage and grain weight were significantly affected by Tmin of the week prior to heading and from heading to maturity, respectively. Overall, there were differences in the response mechanism between hybrid and inbred cultivars to high night temperature. These will facilitate the development of climate-resilient cultivars and appropriate management practices to achieve a stable grain yield.

scholarly journals Combining abilities of silage maize grain yield

2005 ◽  
Vol 50 (1) ◽  
pp. 9-18 ◽  
Author(s):  
Tomislav Zivanovic ◽  
M. Secanski ◽  
Gordana Surlan-Momirovic ◽  
S. Prodanovic
Keyword(s):  
Grain Yield ◽  
Inbred Lines ◽  
Yield Variability ◽  
Average Value ◽  
Maize Grain Yield ◽  
Combining Abilities ◽  
Silage Maize ◽  
Additive Type ◽  
Maize Grain ◽  
Additive Genes

The aim of the present study was to evaluate the following parameters of maize grain yield: variability of inbred lines and their diallel hybrids superior-parent heterosis and general and special combining abilities. According to obtained results of the two-year study, it can be concluded that variability of this trait is significantly affected by a genotype, year and their interaction. As expected, hybrids had higher average grain yields than inbreds due to the depression of this trait that occurs in inbreds during inbreeding. The highest average value of heterosis for gain yield was detected in the hybrid ZPLB405 x ZPLB406 (123.0% and 178.1% in 1997 and 1998, respectively). The estimation of combining abilities was done on the basis of diallel hybrids after the method established by Griffing, 1956 (method II, mathematical model I). The analysis of variance of combining ability for grain yield indicated highly significant values of GCA and SCA for the observed trait in both study years. Grain yield inheritance was more affected by non-additive genes (dominance and epistasis) as indicated by the GCA to SCA ratio that was smaller than unity. The inbreds ZPLB401 and ZPLB406 had high GCA effects, while the hybrid combinations ZPLB40? x ZPLB402, ZPLB401 x ZPLB403, ZPLB401 x ZPLB405, ZPLB402 x ZPLB406, ZPLB403 x ZPLB406, ZPLB404 x ZPLB406, ZPLB405 x ZPLB406 had high SCA effects in both study years. These hybrid combinations include one parent with high GCA effects and other with low GCA effects. Furthermore, there are combinations ZPLB402 x ZPLB405, ZPLB403 x ZPLB405 and ZPLB404 x ZPLB405 with significant SCA effects that include parents with low GCA effects. This is probably the result of the additive type (additive x additive) of interaction between parents.

Yield variability in Phalaris canariensis L. due to seeding date, seeding rate and nitrogen fertilizer

2012 ◽  
Vol 92 (4) ◽  
pp. 651-669 ◽  
Author(s):  
W. E. May ◽  
G. P. Lafond ◽  
Y. T. Gan ◽  
P. Hucl ◽  
C. B. Holzapfel ◽  
...  
Keyword(s):  
Grain Yield ◽  
Seed Yield ◽  
Nitrogen Fertilizer ◽  
Response Curve ◽  
Yield Variability ◽  
Seeding Rate ◽  
Nitrogen Rate ◽  
Seeding Date ◽  
The Impact ◽  
Phalaris Canariensis

May, W. E., Lafond, G. P., Gan, Y. T., Hucl, P., Holzapfel, C. B., Johnston, A. M. and Stevenson, C. 2012. Yield variability in Phalaris canariensis L. due to seeding date, seeding rate and nitrogen fertilizer. Can. J. Plant Sci. 92: 651–669. Concern over the year-to-year and field-to-field variability in grain yield has consistently been expressed by annual canarygrass growers in Saskatchewan. The objectives of these studies were to understand the effects of a delayed seeding date (0, 15, 30 and 45 d), seeding rate (15, 25, 35, 45, and 55 kg ha−1 of seed) and applied N fertilizer (20, 40, 60, 80, and 100 kg N ha−1) on the development and yield of annual canarygrass, to improve recommendations of best management practices in annual canarygrass and to determine the impact of these factors on yield variability in annual canarygrass. To address these objectives, three single factor field experiments were conducted, at a number of sites in Saskatchewan from 1998 to 2001. Seeding date had a large effect on grain yield. Grain yield decreased as seeding was delayed by 30 and 45 d from early May. Seeding rate had a small effect on grain yield. The response curve was very shallow peaking at approximately 1310 kg ha−1 at a seeding rate of 45 kg ha−1. Variation in grain yield tended to decrease as the seeding rate increased. There was a small increase in grain yield with the addition of nitrogen fertilizer. The response curve estimated a maximum yield of 1215 kg ha−1, which was obtained with a nitrogen rate of 78 kg ha−1. The majority of the increase was between 20 and 40 kg N ha−1, with a 2.3 kg ha−1 increase in grain yield for each kg of fertilizer N in that range of rates. There was a slight increase in grain yield as the nitrogen rate increased above 40 kg ha−1 but the variation in grain yield also increased reducing the incentive for growers to use N rates above 40 kg ha−1. Seeding date had a large effect on seed yield and could impact yield variability while seeding rate and nitrogen rate did not have a large effect on seed yield or yield variability.

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