scholarly journals Crop yield prediction using CERES-Rice vs 4.5 model for the climate variability of different agroclimatic zone of south and north-west plane zone of Bihar (India)

MAUSAM ◽  
2021 ◽  
Vol 65 (4) ◽  
pp. 539-552
Author(s):  
P.K. SINGH ◽  
K.K. SINGH ◽  
A.K. BAXLA ◽  
B. KUMAR ◽  
S.C. BHAN ◽  
...  
Keyword(s):  
Climate Variability ◽  
Growth Models ◽  
Sowing Date ◽  
Rice Cultivar ◽  
Growth And Yield ◽  
Weather Data ◽  
North West ◽  
Agroclimatic Zone ◽  
Sowing Dates ◽  
Crop Growth Models

CERES-rice models are being validated and tested across the world and vigorously used in agrotechnology transfer. Crop growth models have been considered as potential tools for simulating growth and yield of crops. Hence, DSSAT v 4.5/ CERES-Rice (Decision Support System for Agro-technology Transfer / Crop Estimation through Resource and Environment Synthesis) was applied to validate the Rice productivity from Bihar State in India. Long term historical weather data (1980-2011) and (1985-2011) from South and North West Alluvial plane zones of Bihar was used for yield analysis. Genetic coefficients required for running the CERES-Rice vs 4.5 model were derived and the performance of the model was tested under the climate variability conditions experienced by these two agroclimatic zones. Management combinations simulated were three transplanting dates (1st, 15th & 30th July) for rice cultivar Rmansuri under rainfed conditions.The results indicated that both the early and late sowing dates result in lower yields as compared to optimum sowing date of 15th July. The simulated phenology and yield were found to be in agreement with observed data suggesting that the calibrated model may be operationally used with routinely observed soil, crop management and weather parameters for Rice yield estimation from these two regions of Bihar.

Changing of Vegetative to Reproductive Ratio as a Response to Different Sowing Dates in Sunflower

Helia ◽  
2018 ◽  
Vol 41 (69) ◽  
pp. 253-266
Author(s):  
Ali Asghar Aliloo
Keyword(s):  
Edible Oils ◽  
Sowing Date ◽  
Biodiesel Production ◽  
Growing Degree Days ◽  
Comprehensive Model ◽  
Yield Losses ◽  
Degree Days ◽  
North West ◽  
Long Period ◽  
Sowing Dates

AbstractSunflower is an important source for edible oils and biodiesel production. Its productivity is limited by many agronomical practices one of which is the sowing date. In this study, the effects of different sowing dates from early April to late June on phenology and yield of sunflower cultivars were investigated. The results showed that sunflower has a relatively long period of possible sowing dates, stretching from early April to late June in North West of Iran. However, delayed sowing dates significantly decreased the number of days needed for phenophases. For every day of delay, the model predicted (R2=0.97) a losing rate in achene yield by 22.2 kg h−1 from the first sowing date. For relationships between growing degree days (GDD) and yield, almost the same results were obtained. About 22 kg h−1 reduction (R2=0.79) in yield per day was estimated by GDD index when the average GDDs per day was 14.2. However, helio-thermal units (HTU) did not predict this reduction accurately. A suggested comprehensive model, that used the percent of yield losses and changes in vegetative to reproductive ratio, found a significant and positive relationship between the indices and yield losses. For all indices, an increase in vegetative to reproductive ratio resulted in increased grain yield losses.

Simulating phenology and yield response of canola to sowing date in Western Australia using the APSIM model

2002 ◽  
Vol 53 (10) ◽  
pp. 1155 ◽  
Author(s):  
I. Farré ◽  
M. J. Robertson ◽  
G. H. Walton ◽  
S. Asseng
Keyword(s):  
Western Australia ◽  
Field Experiments ◽  
Rainfall Variability ◽  
Sowing Date ◽  
Weather Data ◽  
Yield Response ◽  
Yield Reduction ◽  
Sowing Dates ◽  
3.0 T ◽  
Using Data

Canola is a relatively new crop in the Mediterranean environment of Western Australia and growers need information on crop management to maximise profitability. However, local information from field experiments is limited to a few seasons and its interpretation is hampered by seasonal rainfall variability. Under these circumstances, a simulation model can be a useful tool. The APSIM-Canola model was tested using data from Western Australian field experiments. These experiments included different locations, cultivars, and sowing dates. Flowering date was predicted by the model with a root mean squared deviation (RMSD) of 4.7 days. The reduction in the period from sowing to flowering with delay in sowing date was accurately reproduced by the model. Observed yields ranged from 0.1 to 3.2 t/ha and simulated yields from 0.4 to 3.0 t/ha. Yields were predicted with a RMSD of 0.3–0.4 t/ha. The yield reduction with delayed sowing date in the high, medium, and low rainfall region (3.2, 6.1, and 8.6% per week, respectively) was accurately simulated by the model (1.1, 6.7, and 10.3% per week, respectively). It is concluded that the APSIM-Canola model, together with long-term weather data, can be reliably used to quantify yield expectation for different cultivars, sowing dates, and locations in the grainbelt of Western Australia.

scholarly journals Influence of Sowing Dates on the Phenological Development, Growth and Yield Of White Maize Genotypes

2021 ◽  
Vol 24 (1) ◽  
pp. 57-70
Author(s):  
S Akhtar ◽  
MJ Ullah ◽  
A Hamid ◽  
MS Islam ◽  
MKU Ahamed ◽  
...  
Keyword(s):  
Dry Matter ◽  
Seedling Emergence ◽  
Winter Season ◽  
Sowing Date ◽  
Growth And Yield ◽  
Growth Stages ◽  
Maturity Stage ◽  
Yield Attributes ◽  
Maize Genotypes ◽  
Sowing Dates

The experiment was conducted at the Sher-e-Bangla Agricultural University (90o22 E, 23o 41 N), Dhaka, Bangladesh in  Rabi (winter) season of 2017-2018 to study the effects of sowing date on  growth and  yield of four white maize genotypes, viz.  PSC-121, Yangnuo-7, Yungnuo-30 and Changnuo-6. Sowing dates were November 26, December 11, and December 26. Data were collected on different phenological growth stages, dry matter, physiological attributes, yield, and yield attributes. A delay in sowing date delayed the time required for seedling emergence, to reach the 6-leaf collar, maturity stage, and also reduced yield. The planting of PSC-121 in November 26 gave the highest dry matter plant-1, the number of grains cob-1, and 100- grain weight that resulted in the highest grain yield (11.65 t/ha) of the genotype. Bangladesh Agron. J. 2021, 24(1): 57-70

Response of Chickpea Cultivars to Water Stress in a Semi-arid Environment

1987 ◽  
Vol 23 (1) ◽  
pp. 53-61 ◽  
Author(s):  
M. V. K. Sivakumar ◽  
Piara Singh
Keyword(s):  
Root Zone ◽  
Dry Weight ◽  
Moisture Stress ◽  
Sowing Date ◽  
Growth And Yield ◽  
Research Centre ◽  
Evaporative Demand ◽  
Irrigation Regimes ◽  
Sowing Dates ◽  
Chickpea Cultivars

SUMMARYThe combined effect of soil moisture stress in the root zone and atmospheric evaporative demand on the growth and yield of two chickpea cultivars was investigated using four sowing dates and three irrigation regimes within each sowing date during the 1980–81 and 1981–82 seasons at the ICRISAT research centre. Total water use was different for different irrigation regimes and sowing dates and there were differences in the total dry weight and seed yield of chickpea between the sowing dates and the irrigation regimes. The cultivars did not differ in dry matter production. However, cv. Annigeri yielded more than cv. L-550 under all irrigation regimes. Differences in seed weight were observed between irrigation regimes and sowing dates and there were interactions between sowing dates and cultivars and between irrigation regimes, sowing dates and cultivars. High air temperatures during the period from flowering to maturity reduced the time to maturity of late-sown chickpea and led to reduced seed size and lower yields. The efficiency of use of applied water was also low for the late-sown crop.

Effect of Sowing Dates and Maize Cultivars in Growth and Yield of Maize along with their Agro-Climatic Indices in Nawalparasi, Nepal

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
UMESH SHRESTHA ◽  
LAL PRASAD AMGAIN ◽  
TIKA BAHADUR KARKI ◽  
KHEM RAJ DAHAL ◽  
JIBAN SHRESTHA
Keyword(s):  
Grain Yield ◽  
Sowing Date ◽  
Growth And Yield ◽  
Phenological Stages ◽  
Climatic Indices ◽  
Maize Cultivars ◽  
Sowing Dates ◽  
Use Efficiency ◽  
Research Period ◽  
Late Sowing

A field experiment on different maize cultivars planted at different sowing dates were accomplished at Kawasoti-5, Nawalparasi during spring season of 2013 to find suitable sowing date and maize cultivar for the location. Along with this, effect of sowing dates and maize cultivars on different agro-climatic indices were also calculated using formulas. Result showed that RML- 4/RML-17 produced higher kernel rows ear-1 (13.77), kernel per row (30.42) and test weight (244.9 g). Significantly higher grain yield was also found for RML-4/RML-17 (6.03 tha-1) compared to Poshilo makai-1 (4.73 t ha-1), Arun-2 (3.55 t ha-1) and Local (2.92 t ha-1). Earlier sowing date (7th April) produced higher kernel row-1 (27.97), kernel rows ear-1 (12.89) and 1000 grain weight (230 g). Significantly higher grain yield (5.13t ha-1) was obtained in earlier sowing date (7th April). Although the mean ambient temperature during research period was increasing with delayed sowing, days to attain different phenological stages decreased with late sowing. The statistically similar GDD was recorded for different sowing dates and higher PTI values were noticed with delay in planting. Similarly, heat use efficiency (HUE) was found higher in early sowing date. Arun-2 had small reduction in HUE so, it can be considered stable and best cultivar among the tested cultivars.

A Critical Analysis of Effect of Projected Temperature and Rainfall for Differential Sowing of Maize Cultivars Under RCP4.5 and RCP6.0 Scenarios for Punjab

2021 ◽  
Author(s):  
Shivani Kothiyal ◽  
Prabhjyot Kaur ◽  
Jatinder Kaur
Keyword(s):  
Minimum Temperature ◽  
Rainfall Variability ◽  
Weather Data ◽  
High Yield ◽  
Strong Negative Correlation ◽  
Rainfall Variation ◽  
Agroclimatic Zone ◽  
Maize Cultivars ◽  
Sowing Dates ◽  
Agroclimatic Zones

Abstract A simulation study was conducted for two cultivars of maize (PMH1 and PMH2) in four agroclimatic zones of Punjab state of India where climate change depicts a consistent rise in temperature and increased variability in amount and distribution of rainfall. The yield assessment was performed for four agroclimatic zones of Punjab comprising of seven locations because variability in temperature rise and rainfall existed from location to location. Corrected ensemble model weather data (temperature and rainfall) for RCP4.5 and RCP6.0 was used as an input in the calibrated and validated CERES-Maize model and yield was simulated for a period of 70 years. The simulated yield for near as well as far-future was statistically assessed to understand the yield trend in Punjab under current dates of sowing and the results indicated a strong negative correlation between the yield and the weather parameters under the two scenarios at the considered four agroclimatic zones of Punjab. An increase in maximum and minimum temperature was observed ranging 0-4°C and 3-8°C, respectively at all the agroclimatic zones except Faridkot (zone V) where the increase in minimum temperature was observed by 0-3°C during the crop growth season while the rainfall variability ranged from 200-800mm under both the scenarios. At agroclimatic zone II and zone III similar results were obtained with higher yields at later dates of sowing and the rainfall at agroclimatic zone III was higher under RCP6.0 (300-600mm) while the yields for agroclimatic zone IV and V (Abohar) with rainfall variation of 270-450mm and 200-400mm, respectively showed no yield increment. Maize at Faridkot performed well with higher yields at early sowing dates. Among the two cultivars PMH1 showed more high yield years than PMH2 for most of the years. The yield under differential sowing dates showed the first fortnight of June and end June to be the best sowing dates for most of the locations as the yield for these dates were higher for most of the years. Thus, the study can be further applied to decide the future sowing window of maize for the agricultural state like Punjab.

scholarly journals Integrating Crop Growth Models with Remote Sensing for Predicting Biomass Yield of Sorghum

2021 ◽  
Author(s):  
Kai-Wei Yang ◽  
Scott Chapman ◽  
Neal Carpenter ◽  
Graeme Hammer ◽  
Greg McLean ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Plant Breeding ◽  
Growth Models ◽  
Remote Sensing Data ◽  
Crop Growth ◽  
Weather Data ◽  
Crop Models ◽  
Geographical Regions ◽  
Crop Growth Models ◽  
Photoperiod Sensitive

Abstract Plant phenotypes are often descriptive, rather than predictive of crop performance. As a result, extensive testing is required in plant breeding programs to develop varieties aimed at performance in the target environments. Crop models can improve this testing regime by providing a predictive framework to (1) augment field phenotyping data and derive hard-to-measure phenotypes and (2) estimate performance across geographical regions using historical weather data. The goal of this study was to parameterize the Agricultural Production Systems sIMulator (APSIM) crop growth models with remote sensing and ground reference data to predict variation in phenology and yield-related traits in 18 commercial grain and biomass sorghum hybrids. Genotype parameters for each hybrid were estimated using remote sensing measurements combined with manual phenotyping in West Lafayette, Indiana in 2018. The models were validated in hybrid performance trials in two additional seasons at that site and against yield trials conducted in Bushland, Texas between 2001 and 2018. These trials demonstrated that (1) maximum plant height, final dry biomass, and radiation use efficiency (RUE) of photoperiod sensitive and insensitive forage sorghum hybrids tended to be higher than observed in grain sorghum, (2) photoperiod sensitive sorghum hybrids exhibited greater biomass production in longer growing environments, and (3) the parameterized and validated models perform well in above ground biomass simulations across years and locations. Crop growth models that integrate remote sensing data offer an efficient approach to parameterise larger plant breeding populations.

scholarly journals Effect of Sowing Dates and Varities on Yield and Yield Attributes of Direct Seeded Rice in Chitwan Condition

2013 ◽  
Vol 14 ◽  
pp. 121-130
Author(s):  
KP Dawadi ◽  
NK Chaudhary
Keyword(s):  
Grain Yield ◽  
Sowing Date ◽  
Growth And Yield ◽  
Sowing Time ◽  
Early Maturity ◽  
Yield Attributes ◽  
Net Return ◽  
Sowing Dates ◽  
Direct Seeded ◽  
Use Efficiency

Rice transplanting and sowing time sometimes get delayed due to lack of assured irrigation or surplus of rainfall. Moreover, no specific varieties have been specifically developed for this purpose. An experiment was conducted to study the effect of sowing dates and varieties on growth and yield of direct seeded rice during rainy season in 2010. The experiment was laid out in split plot design with four sowing dates and three varieties in sub plot. Sowing date on June 13th contributed to higher grain yield; higher gross return; net return and higher B:C ratio per hectare. Similarly, the variety Hardinath-1 excelled better in all these parameters with early maturity. The interaction effect of Hardinath-1 with June 13 sowing took lower days for maturity; produced higher number of effective tillers (386.3); heat use efficiency (2.14); straw yield (7.43 t ha-1); and relatively higher grain yield (4.22 t ha-1); gross return (Rs.108.55 thousand); net return (Rs. 51.22 thousands) and B:C ratio (1.89). Therefore, variety Hardinath-1 with June 13 sowing is best suited to get higher yield, timely maturity and higher economic return in Chitwan conditions.

scholarly journals Simulating the Impact of Climate Change on Growth and Yield of Maize Using CERES-Maize Model under Temperate Kashmir

2019 ◽  
pp. 1-11 ◽  
Author(s):  
Bilal Ahmad Lone ◽  
Shivam Tripathi ◽  
Asma Fayaz ◽  
Purshotam Singh ◽  
Sameera Qayoom ◽  
...  
Keyword(s):  
Climate Variability ◽  
Minimum Temperature ◽  
Maize Yield ◽  
Climate Impact ◽  
Growth And Yield ◽  
Weather Data ◽  
Maximum Temperature ◽  
The Impact ◽  
Global Food ◽  
Process Based Models

Climate variability has been and continues to be, the principal source of fluctuations in global food production in countries of the developing world and is of serious concern. Process-based models use simplified functions to express the interactions between crop growth and the major environmental factors that affect crops (i.e., climate, soils and management), and many have been used in climate impact assessments. Average of 10 years weather data from 1985 to 2010, maximum temperature shows an increasing trend ranges from 18.5 to 20.5°C.This means there is an increase of 2°C within a span of 25 years. Decreasing trend was observed with respect to precipitation was observed with the same data. The magnitude of decrease was from 925 mm to 650 mm of rainfall which is almost decrease of 275 mm of rainfall in 25 years. Future climate for 2011-2090 from A1B scenario extracted from PRECIS run shows that overall maximum and minimum temperature increase by 5.39°C (±1.76) and 5.08°C (±1.37) also precipitation will decrease by 3094.72 mm to 2578.53 (±422.12) The objective of this study was to investigate the effects of climate variability and change on maize growth and yield of Srinagar Kashmir. Two enhanced levels of temperature (maximum and minimum by 2 and 4°C) and CO2 enhanced by 100 ppm & 200 ppm were used in this study with total combinations of 9 with one normal condition.  Elevation of maximum and minimum temperature by 4°C anthesis  and maturity of maize was earlier 14 days with a deviation of 18%  and  26 days with a deviation  of 20% respectively. Increase in temperature by 2 to 4°C alone or in combination with enhanced levels of CO2 by 100 and 200 ppm the growth and yield of maize was drastically declined with an reduction of about 40% in grain yield. Alone enhancement of CO2  at both the levels fails show any significant impact on maize yield.

Comparison of the effects of autumn and spring sowing date on growth and yield of combining peas (Pisum sativutn L.)

1985 ◽  
Vol 104 (1) ◽  
pp. 35-46 ◽  
Author(s):  
S. N. Silim ◽  
P. D. Hebblethwaite ◽  
M. C. Heath
Keyword(s):  
Seed Yield ◽  
Dry Matter ◽  
Sowing Date ◽  
Winter Survival ◽  
Growth And Yield ◽  
Yield Data ◽  
Matter Production ◽  
Sowing Dates ◽  
Seed Yields ◽  
Spring Sowing

SummaryExperiments were conducted between 1978 and 1981 to investigate the effect of autumn and spring sowing on emergence, winter survival, growth and yield of combining peas (varieties ‘Frimas’, ‘Filby’ and ‘Vedette’). Effects of growth regulator PP 333 (Paclobutrazol, ICI pic) application and defoliation on winter survival of Filby were also investigated. Field emergence of autumn-sown Frimas (winter hardy) was less than Vedette or Filby but percentage winter survival was greater. PP 333 application, but not defoliation, increased percentage winter survival of Filby sown in September. Total dry-matter production and photosynthetic area of autumn- compared with spring-sown crops varied considerably between seasons. Yield data indicated that autumn-sown crops produce similar seed yields to spring sowings when winter survival is adequate. November sowings matured 2–4 weeks before March-sown crops, depending on variety and season. Optimum sowing dates were mid-November and early March. Large seed-yield reductions occurred when sowing was delayed until mid-April.

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