TY  - BOOK
AU  - Harithalekshmi V.
AU  - Ajithkumar, B (Guide)
TI  - Analyis of potential yield and yield gap of rice(Oryza sativa L.)using ceres rice model
U1  - 630.251 
PY  - 2020///
CY  - Vellanikkara
PB  - Department of agricultural meteorology, College of Horticulture
KW  - Potential yield and yield gap of rice
N1  - MSc
N2  - ABSTRACT
Rice has shaped the culture, diet and wealth of millions of people. For more
than half of population around the globe "Rice is life". It is the staple food for more
than half of the global population. A world population that will exceed 9 billion by
2050 will require an estimated 60 % more food. World production of rice was 495.9
million metric tons during 2019. There is an urgent need to boost current agricultural
productivity. Assessing the yield gap of existing cropped lands will indicate the
possible extend of yield increase from current value. Crop weather models are a
promising tool for estimating yield gap, identifying causes of yield gap and evaluating
proper management strategies to reduce the gap.
The present study was aimed to analyze potential yield and yield gap among
two rice varieties and to suggest proper management practices to reduce gap. Two
varieties of rice, Jyothi and Jaya were raised at Agricultural Research Station,
Mannuthy during kharif season by adopting split-plot design. Five planting dates such
as June 5th, June 20th, July 5th, July 20th and August 5th were used as main plot
treatments and the two varieties were used as subplot treatments. The replication
number used for this experiment was four.
During the field experiment, daily weather data were collected like maximum
temperature, minimum temperature, relative humidity, rainfall, bright sunshine hours,
wind speed and evaporation. Biometric observations like plant height, leaf area, dry
matter accumulation, number of tillers per unit area, number of panicles per unit area,
number of spikelet per panicle, number of filled grains per panicle, thousand grain
weight, straw yield and grain yield were observed. Duration of different phenophases
was noted. Duration of phenophases, yield and yield attributes were found to be
significantly influenced by weather parameters recorded during each phenophases.
Considerable variation among biometric observations was noticed during the field
experiment. Plant height was higher for Jyothi compared to Jaya and it showed
variation among different planting dates. Maximum dry matter accumulation was
recorded during 75 days after planting and it was higher for August 5th planting.
Compared to Jaya dry matter accumulation was more in Jyothi. Effect of dates of
planting was significant in all yield attributes except for the number of panicle and
straw yield. The grain yield obtained during June 5th (4418 kg ha-1) and June 20th
(4029 kg ha-1) planting were on par irrespective of variety.
The potential yield was simulated by CERES model. Attainable yield was the
yield obtained from the experimental plot, where management practices as suggested
by KAU was followed. Actual farmer's yield was collected from the survey and
ECOSTAT report, 2019. Total yield gap was calculated by taking the difference
between potential yield and actual farmer's yield. Total yield gap was split into two
components, yield gap I (YGI = Potential yield – Attainable yield) and yield gap II
(YGII = Attainable yield – Actual yield). Total yield gap estimated for Jaya was
3457 kg ha-1 and for Jyothi was 3357 kg ha-1. YGI calculated for Jaya and Jyothi was
1740 kg ha-1 and 2078 kg ha-1 respectively. YGII calculated for Jaya and Jyothi was
1717 kg ha-1 and 1279 kg ha-1 respectively.
Yield responses under various nitrogen management conditions were
simulated using CERES model. Yield simulated under three split doses of nitrogen
was more in all dates of planting in the case of Jaya. In case of Jyothi, yield increase
under 3 split nitrogen doses was more under first three dates of planting whereas
during last two dates of planting it was less. The model simulated yield was found to
be increased with an additional supply of nitrogen input. As per the model output, the
optimum dose of nitrogen to get higher yield (5572 kg ha-1 for Jaya and 5387 kg ha-1
for Jyothi ) was found to be 130 kg ha-1. The model was used to compare the fertilizer
application methods like broadcasting, using urea super granules and together with
irrigation water. As per the model output the yield simulated for general amount of
nitrogen applied (90 kg ha-1 for Jaya and 70 kg ha-1 for Jyothi was) was more when
fertilizer was applied through irrigation water
UR  - https://krishikosh.egranth.ac.in/handle/1/5810163330
ER  -