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Field Crops Research 260 (2021) 107977
0378-4290/© 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Genetic gain for rice yield in rainfed environments in India
Arvind Kumar
a
,
b
,
*, Anitha Raman
a
, Shailesh Yadav
a
, S.B. Verulkar
c
, N.P. Mandal
d
, O.
N. Singh
e
, P. Swain
e
, T. Ram
e
, Jyothi Badri
f
, J.L. Dwivedi
g
, S.P. Das
h
, S.K. Singh
i
, S.P. Singh
j
,
Santosh Kumar
k
, Abhinav Jain
a
,
l
, R. Chandrababu
m
, S. Robin
m
, H.E. Shashidhar
n
,
S. Hittalmani
n
, P. Satyanarayana
o
, Challa Venkateshwarlu
p
, Janaki Ramayya
p
, Shilpa Naik
p
,
Swati Nayak
q
, Manzoor H. Dar
q
, S.M. Hossain
r
, Amelia Henry
a
, H.P. Piepho
s
a
International Rice Research Institute (IRRI), DAPO Box 7777, Metro Manila, Philippines
b
IRRI South Asia Regional Center (ISARC), Varanasi, India
c
Indira Gandhi Krishi Vishwavidyalaya (IGKV), Raipur, India
d
Central Rainfed Upland Rice Research Station (CRURRS), ICAR-NRRI, Hazaribagh, India
e
National Rice Research Institute (NRRI), Cuttack, India
f
Indian Institute of Rice Research (IIRR), Hyderabad, India
g
Narendra Dev University of Agriculture and Technology (NDUAT), Ayodhya, India
h
ICAR Research Complex for NEH Region, Tripura Centre, Lembucherra, India
i
Banaras Hindu University (BHU), Varanasi, India
j
Bihar Agricultural University (BAU), Sabour, India
k
ICAR-Research Complex for Eastern Region, Patna, India
l
Barwale Foundation, Hyderabad, India
m
Tamil Nadu Agricultural University (TNAU), Coimbatore, India
n
University of Agricultural Sciences (UAS), Bangalore, India
o
Regional Rice Research Station, Maruteru, Acharya NG Ranga Agricultural University, (ANGRAU), Guntur, India
p
International Rice Research Institute, South Asia Hub, ICRISAT, Patancheru, Hyderabad, India
q
International Rice Research Institute (IRRI), New Delhi, India
r
International Rice Research Institute (IRRI), Bhubaneshwar, India
s
Universitaet Hohenheim, Biostatistics Unit, 70593, Stuttgart, Germany
ARTICLE INFO
Keywords:
Rice
Drought
Genetic gain
Yield potential
ABSTRACT
The complexity of genotype ×environment interactions under drought reduces heritability, which determines
the effectiveness of selection for drought tolerance and development of drought tolerant varieties. Genetic
progress measured through changes in yield performance over time is important in determining the efciency of
breeding programmes in which test cultivars are replaced each year on the assumption that the new cultivars will
surpass the older cultivars. The goal of our study was to determine the annual rate of genetic gain for rice grain
yield in a drought-prone rainfed system in a series of multi-environment trials conducted from 2005 to 2014
under the Drought Breeding Network of Indian sites in collaboration with the International Rice Research
Institute (IRRI). Our results show a positive trend in grain yield with an annual genetic yield increase of about
0.68 % under irrigated control, 0.87 % under moderate reproductive stage drought stress and 1.9 % under severe
reproductive stage drought stress due to breeding efforts. The study also demonstrates the effectiveness of direct
selection for grain yield under both irrigated control as well as managed drought stress screening to improve
yield in typical rainfed systems. IRRI’s drought breeding programme has exhibited a signicant positive trend in
* Corresponding author at: IRRI South Asia Regional Centre (ISARC), NSRTC Campus, G.T. Road, Collectry Farm P.O. Industrial Estate, Varanasi, 221006, Uttar
Pradesh, India.
E-mail addresses: a.kumar@irri.org (A. Kumar), a.raman@irri.org (A. Raman), shailesh.yadav@irri.org (S. Yadav), satishverulkar@gmail.com (S.B. Verulkar),
npmandal@hotmail.com (N.P. Mandal), onsingh01@yahoo.com (O.N. Singh), pswaincrri@gmail.com (P. Swain), t.ram2011@yahoo.com (T. Ram), jyothirishik@
gmail.com (J. Badri), dwivedi_jl@rediffmail.com (J.L. Dwivedi), drspdas@gmail.com (S.P. Das), shravanbhu1964@gmail.com (S.K. Singh), sps2007bau2011@
gmail.com (S.P. Singh), santosh9239@gmail.com (S. Kumar), a.jain@irri.org (A. Jain), chandrarc2000@yahoo.com (R. Chandrababu), robin.tnau@gmail.com
(S. Robin), heshashidhar@rediffmail.com (H.E. Shashidhar), shailajah_maslab@rediffmail.com (S. Hittalmani), satya_rice@yahoo.com (P. Satyanarayana), c.
venkateshwarlu@irri.org (C. Venkateshwarlu), janakibiotech007@gmail.com (J. Ramayya), shilpa0643@gmail.com (S. Naik), s.nayak@irri.org (S. Nayak),
seedmanzoor.dar@gmail.com (M.H. Dar), s.hossain@irri.org (S.M. Hossain), a.henry@irri.org (A. Henry), hans-peter.piepho@uni-hohenheim.de (H.P. Piepho).
Contents lists available at ScienceDirect
Field Crops Research
journal homepage: www.elsevier.com/locate/fcr
https://doi.org/10.1016/j.fcr.2020.107977
Received 9 December 2019; Received in revised form 1 October 2020; Accepted 5 October 2020
Field Crops Research 260 (2021) 107977
2
genetic gain for grain yield over the years under both drought stress as well as favorable irrigated control
conditions. Several drought tolerant varieties released from the programme have outperformed the currently
grown varieties under varied conditions in the rainfed environments on farmers’ elds.
1. Introduction
Rice is the most important crop in the world feeding more people
than any other crop, and plays a vital role in the Asian economy. In
1966, the release of IR8, the rst semi-dwarf high yielding modern rice
variety from a cross between Peta, a tall Indonesian variety, and Dee-
geo-woo-gen, a short statured variety from Taiwan, marked the initia-
tion of the green revolution in rice production that over time has
transformed the food decient Asia to a food self-sufcient region. The
green revolution had a remarkable impact on rice production, following
which rice farming underwent signicant transformation. The improved
short-statured varieties (such as Jaya, IR20, IR36, IR42, IR50) developed
since then for the irrigated ecosystem had high yield potential, short
growth duration, were input responsive, and were disease and insect
resistant, however they lacked improved grain quality (Khush, 1987;
Khush and Virk, 2005). IR64, released in the Philippines in 1985 had
superior grain quality as well as higher yield compared with previously
released IR varieties, and its long persistence in farmers’ elds after its
release was attributed to the excellent eating quality (Champagne et al.,
2010; Mackill and Khush, 2018). The green revolution was based upon
the philosophy that selection can be done under optimal environments
assuming that an increased yield potential will have a carryover effect
under water shortage conditions. However, in the regions chronically
affected by drought, that approach failed to improve yield under
drought conditions (Ceccarelli, 2010). Thus, by focusing on technologies
for very favorable, usually irrigated environments, millions of hectares
of land in rainfed areas were not able to fully benet from yield gains
made in irrigated areas (Ziegler, 1998).
Rice production systems can be classied into lowland and upland.
Rainfed rice elds that are not irrigated but in which the soil is ooded
for at least part of the crop cycle are commonly known as rainfed low-
lands. Water availability is unpredictable, as the crop is rainfed. Asia has
about 46 million hectares of rainfed lowland rice or almost 30 % of the
total world rice area (Maclean et al., 2002). One-third of South and
South East Asian rice lands lie within this ecosystem, which dominates
rice areas of Bangladesh, Cambodia, Myanmar, Nepal, and Thailand,
and is important in India, Indonesia, Laos, and Vietnam (CGIAR, 1998).
More than 75 % of the region’s poor rice farmers depend on rainfed
agriculture. The uncertain water supply in rainfed lowland areas,
together with the infertile soil that can be acidic or saline and the
varying crop management methodologies, provide a highly heteroge-
neous set of breeding targets with a range of environmental conditions
that inuence the phenotypic response of the genotypes. This complex
and diverse situation very often lies within small geographical regions.
Genetic improvement under such complex situations would be a key
challenge for any plant breeding program.
In the rainfed environments characterized by lack of water control,
drought and ooding are regular problems. Drought is the most signif-
icant constraint affecting rice production in rainfed lowland rice. The
intensity, duration and timing of drought may vary from location to
location and in a given location from year to year. At reproductive stage,
drought causes a reduction in the number of grains per panicle, increases
grain sterility, and reduces grain weight (Pantuwan et al., 2002).
Drought during the reproductive stage leads to major yield reduction
(Kumar et al., 2007). Even moderate drought stress at reproductive stage
can result in substantial reduction in grain yield (O’Toole, 1982; Huke
and Huke, 1997; Zhang et al., 2018). Moreover, drought seldom occurs
in isolation; it often interacts with other abiotic and biotic stresses
(Ceccarelli and Grando, 1996) such as soil texture, pH, soil fertility,
diseases and insects. With the effect of climate change, the recurrence
and intensity of drought is likely to change (Ceccarelli et al., 2004) and
possibly becomes more frequent (World Bank, 2009). Recurrent drought
linked with climate change would have an adverse effect on crop pro-
ductivity and on millions of poor farmers’ livelihood. Therefore,
drought-prone rice systems require stress tolerant rice varieties and
improved management strategies.
However, breeding for drought resistance has been slow. In addition
to drought tolerance being a complex trait, it is reported to be controlled
by several genes each with small effects, and signicant genoty-
pe ×environment interactions (G ×E) under stress (Dixit et al., 2014)
lead to variable performance of genotypes across locations. Conse-
quently, yield under drought has been reported to have low heritability
(H) which complicates selection of superior, drought tolerant, stable
genotypes. In turn, this limits genetic gain for grain yield under drought.
Genetic gain, although a less commonly used measure of breeding ef-
ciency (Ceccarelli, 2015), is dened as the amount of increase in per-
formance that is achieved annually through articial selection (Xu et al.,
2017). Genetic gain estimation is vital for any crop breeding program to
analyze its strengths and weaknesses and to plan future breeding ac-
tivities. In stress environments, improving selection efciency and
increasing genetic gain requires a proper understanding of the target
environment, a selection environment that is representative of the target
environment, a population with large genetic variance, proper trial
management, and appropriate screening procedures.
Genetic gain is usually estimated using multi-environment trials
(METs) that are routinely conducted as a part of breeding program
(Muralidharan et al., 2002; Fufa et al., 2005). Traditionally, popular
check cultivars are grown in all trials in METs to minimize the inuences
of location and year. Genetic progress has been estimated from the
difference between checks and top-yielding cultivars (Feyerherm et al.,
1984) or by subtracting the trend line tted to a set of checks common
across many years from the trend line tted to the year-wise means
(Sharma et al., 2012). However, choice of a representative check
cultivar becomes complicated and with the introduction of new crop
genotypes, the check cultivar may become unrepresentative and a new
check cultivar has to be chosen (Friesen et al., 2016). In previous ana-
lyses, the genotype yields were often expressed as a percentage of the
long-term check yield, assuming that this would minimize G ×E, which
may not be realistic. Moreover, data generated from METs aimed at
screening of new cultivars are highly unbalanced due to variation in the
genotypes tested from year to year, variation in the number of replicates
at each site and variation in the sites that are included from year to year.
In addition, stress-susceptible varieties may contribute to missing values
when exposed to severe stress (Raman et al., 2012). Therefore, the
analytical methods for METs need to be adaptable to unbalanced data
(Liu et al., 2015). Mixed model analysis is an alternative to the tradi-
tional MET analysis based on checks. Missing values, parameter esti-
mation and prediction of genotype performances are effectively handled
by mixed models (Piepho and M¨
ohring, 2006). Treating genotypes as
random effect in a mixed model allows their genetic value to be pre-
dicted using the best linear unbiased predictors (BLUPs). Recently,
mixed models have been used to dissect genetic and non-genetic trends
in multi-environment trial data (Mackay et al., 2011; Piepho et al., 2017;
Laidig et al., 2014, 2017; Ahrends et al., 2018). The contribution of the
cultivars (genetic trend) and of the environments (non-genetic trends) to
yield improvement over time is quantied by regression coefcients.
The genotypes screened in this study are breeding lines developed
from crosses involving a high yielding, but drought susceptible recipient
parent that possesses good grain quality, and a low yielding but highly
drought-tolerant donor parent. The lines were advanced through direct
A. Kumar et al.
Field Crops Research 260 (2021) 107977
3
selection for grain yield (Kumar et al., 2007) under both favorable
conditions as well as variable levels of reproductive-stage drought stress
in different generations in a pedigree generation advancement breeding
program. The selected lines were evaluated for their performance for
grain yield under favorable irrigated condition as well as moderate and
severe levels of reproductive stage drought stresses in the years
2005–2014 (Verulkar et al., 2010; Mandal et al., 2010). The experiments
were conducted in the Indian sites in collaboration with the Interna-
tional Rice Research Institute (IRRI) under the Drought Breeding
Network of the STRASA (Stress-Tolerant Rice for Africa and South Asia)
project. The STRASA project, supported by the Bill and Melinda Gates
Foundation, has helped millions of farmers who produce their crop
under predominantly rainfed conditions to achieve signicantly higher
yield despite abiotic stresses such as drought, ood, cold, salinity and
iron toxicity (Ismail et al., 2013; Dar et al., 2014; Arora et al., 2019).
The objective of the present study under STRASA was to assess and
monitor yield trends and to estimate the annual rate of increase in yield
for breeding lines in the highly variable drought prone rainfed lowland
environments of eastern and southern India. The trends were dissected
to quantify the contributions of varietal selection and crop management
to the improvement in grain yield. Comparisons draw on yield perfor-
mance and/or tolerance to different biotic/abiotic stresses, of the
released stress tolerant rice varieties (STRVs) as well as the popular
farmer varieties.
2. Material and methods
2.1. Field sites, genotypes and years of screen
The data comprise yield measurements from rainfed lowland trials
conducted under three pre-dened conditions of water and agronomic
management; 1. Irrigated control (maintaining the plants under well-
watered conditions until physiological maturity) conditions, 2. rainfed
with supplementary irrigation and, 3. rainfed conditions.
Approximately 50–70 selected genotypes of 100–120 days duration
were pooled to constitute an advanced yield trial (AYT). These AYT were
conducted during wet seasons for ten years from 2005 to 2014 at four-
teen on station test sites in India (Table 1,2).
All trials were laid out in alpha-lattice designs with three replica-
tions. Wet-season trials followed a standardized drought-screening
protocol at all sites (Verulkar et al., 2010; Kumar et al., 2014). In
brief, the stress was imposed by draining out water from the eld at 50
DAS (days after seeding) and the cyclic soil moisture decit stress was
maintained till the maturity stage. Perforated PVC pipes of 1.1 m length
was installed at regular places across the eld to measure depth of water
table. A life-saving irrigation was provided as soon as the water table
level in the PVC fell below 1.0 m from soil surface and all the susceptible
checks started showing severe leaf rolling and dying at 10 am. Water was
drained out immediately after 24 h to initiate the next cycle of stress.
The irrigated control trial was planted at each of the sites with
similar crop management practices to determine grain yield reduction
under stress. Standing water was maintained in the eld of the irrigated
control trials from transplanting until maturity. Under reproductive
stage drought, the seeding and transplanting for stress trials was delayed
by approximately 20–25 days to increase the probability of the repro-
ductive stage of the crop to coincide with the end of monsoon rains. A
detailed description for a list of typical planting delays at each site was
given previously by Singh et al. (2017).
Most stress trials were conducted in light-soiled elds high in top-
osequence with the crop fully irrigated for one month after transplanting
and then drained to ensure rapid drying. The rainfed condition trials
were planted normally but were never irrigated, and unbunded elds
were chosen to increase the probability of water stress. All three con-
ditions were judiciously designed to reect and simulate conditions of
farmers’ elds.
To eliminate the bias of border effects, we created our eld layout in
such a way that the planted area was continuous, without leaving space
between plots. The two hills (borders) neighboring the bund (edge of the
eld) were excluded from the harvest for yield assessment in all plots.
All the hills in the plot area described above were harvested, and we
discarded measurements made on plots with more than 20 % missing
hills. A recommended agronomical practice previously reported by
Table 1
Salient features of the STRASA network sites.
Sites Location Target environment Soil type Field topography Drought Presently grown varieties
Indira Gandhi Krishi
Vishwavidyalaya (IGKV),
Raipur, Chhattisgarh
21◦14′N,
81◦38′E
Rainfed low land
ecosystem
Clay, clay loam, low
organic carbon
Bunded, shallow
lowland to mid lowland
Reproductive stage
and early season
drought
MTU 1010, IR 64,
Mahamaya, Swarna
Narendra Dev University of
Agriculture and Technology
(NDUAT), Faizabad, Uttar
Pradesh
26◦47′N,
82◦12′E
Rainfed shallow,
mid and deep low
land
Clay, clay loam, low
organic carbon
Bunded, shallow, mid
and deep lowland
Early season
drought and
reproductive stage
Sarjoo 52, Swarna, NDR
97, NDR 359, Baranideep
Central Rainfed Upland Rice
Research Station (CRURRS),
Hazaribag, Jharkhand
23◦59′N,
82◦25′E
Bunded uplands
and rainfed shallow
low land
Acidic in nature, very
poor in fertility, low in
available N and
organic carbon
Highly undulating,
unbunded, bunded
uplands and bunded
shallow lowland
Drought at all
stages of growth
IR 36, IR 64, MTU 1010,
Hazaridhan, Sadabahar,
Birsa 201, Some other land
races
Central Rice Research Institute
(CRRI), Cuttack, Odisha
20◦28′N,
85◦54′E
Rainfed upland and
shallow lowland in
dry season
Sandy loam and clay
loam soil
Bunded, shallow, mid
and deep lowlands
Seedling, and
vegetative stage
Lalat, Swarna,
Varshadhan, Naveen, MTU
1010, Khandagiri,
Vandana,
TamilNadu Agricultural
University (TNAU),
Coimbatore, TamilNadu
11◦00′N,
77◦00′E
Irrigated lowland Clay Bunded, shallow and
mid lowlands
Reproductive stage IR 64, Co. 47
Tamil Nadu Agricultural
University (TNAU),
Paramakudi, TamilNadu
09◦31′N,
78◦39′E
Rainfed upland Clay loam Bunded Upland Reproductive stage PMK 3, ADT 38, Local
races
University of Agricultural
Sciences (UAS), Bangalore,
Karnatakka
12◦58′N,
77◦38′E
Rainfed upland and
shallow lowland
Red, loamy and light Bunded, shallow
lowland
Reproductive stage MTU 1001, IR 64, Jyoti,
MTU 1010, BPT 5204,
Rasi,
Barwale Foundation (BF),
Hyderabad, Telengana
17◦20′N,
78◦30′E
Rainfed Shallow
low land
Clay loam Bunded, shallow
lowland
Reproductive stage Sambha Mahsuri, Swarna,
MTU 1010
Birsa Agricultural University
(BAU), Ranchi, Jharkhand
23◦17′N,
85◦19′E
Rainfed bunded
uplands and
shallow low land
Acidic in nature, very
poor in fertility, low in
available N and
organic carbon
Highly undulating,
bunded uplands and
shallow lowland
Drought at all
stages of growth
Lalat, IR 36, IR 64, MTU
1010, Birsa 201, Some
other land races
A. Kumar et al.
Field Crops Research 260 (2021) 107977
4
Kumar et al. (2014) was followed for irrigated control as well as drought
trials conducted at IRRI, SA-Hyderabad. In brief, the row-to-row and
plant-to-plant spacing was maintained at 20 cm ×20 cm. Nitrogen,
phosphorus and potassium (NPK) were applied at the rate of
120:60:40 kg ha
−1
. N was applied in three doses, rst as basal, second at
tillering stage and third at panicle initiation while P and K were applied
as basal in a single dose. In order to control weeds, Sot (pretila-
chlor ±safene, 0.3 kg a.i. ha
−1
) was sprayed just after transplanting
followed by Furadan (carbofuran 1 kg a.i. ha
−1
) and Dimotrin (cartap
hydrochloride, 0.25 kg a.i. ha
−1
) at 5 and 16 days after transplanting,
respectively. The detailed information on the trials conducted at various
sites in India (location, year of conduct, harvested area, dose of fertil-
izers applied, rainfall during the reproductive-stage stress period) is
presented in Supplementary Table S1.
The experimental sites included in this study represented the major
drought-prone rice-growing regions in India. A description of the land-
use history, soil and environmental characteristics of these sites,
including the 2012–2014 AYT trials in the present study, was reported
by Singh et al. (2017).
After harvest, each stress trial was classied as moderately stressed if
the yield reduction as compared to the irrigated control trial was 30–65
%, and severely drought-stressed if the yield reduction was more than 65
% (Table 2). Trials with less than 30 % yield reduction were classied as
mild stress and were not included in our analysis, since mild drought
stress did not clearly distinguish the lines with high yield had potential
from drought tolerant lines (Verulkar et al., 2010). Thus, the irrigated
control category has 54 trials, 28 trials were classied as moderately
stressed and 31 trials were categorized as severely stressed.
Table 2
Details of the Advanced Yield Trials (AYT) trials assigned to two stress levels based on the reduction in trial mean yield as compared to irrigated control during 2005 –
2014.
Irrigated control Moderate stress Severe Stress
Trial Location year Trial mean
yield
Trial
H
Trial Location Year Trial mean
yield
Trial
H
Trial Location Year Trial mean
yield
Trial
H
1 Faizabad 2005 6951 0.95 1 Raipur 2006 2796 0.94 1 Faizabad 2006 1265 0.94
2 Hazaribag 2005 4997 0.87 2 Faizabad 2007 2192 0.96 2 Hazaribag 2006 994 0.86
3 Raipur 2005 3814 0.87 3 Hazaribag 2007 3383 0.83 3 Raipur 2006 1223 0.91
4 Faizabad 2006 3658 0.98 4 Raipur 2007 3270 0.77 4 Raipur 2007 1889 0.8
5 Hazaribag 2006 2998 0.51 5 Barwale 2008 2013 0.92 5 TNAU 2007 1025 0.32
6 Raipur 2006 4750 0.61 6 Hazaribag 2008 2234 0.85 6 Barwale 2008 991 0.92
7 Barwale 2007 5480 0.48 7 Raipur 2008 2226 0.84 7 DRR 2008 1004 0.86
8 Faizabad 2007 4098 0.97 8 Faizabad 2009 2369 0.87 8 Faizabad 2008 1639 0.98
9 Hazaribag 2007 4567 0.72 9 Hazaribag 2009 2222 0.83 9 Hazaribag 2008 1605 0.91
10 Raipur 2007 3634 0.73 10 Raipur 2009 2470 0.68 10 Raipur 2008 1009 0.93
11 Barwale 2008 4108 0.84 11 Tripura 2009 2367 0.39 11 TNAU 2008 1126 0.74
12 Faizabad 2008 3183 0.99 12 Barwale 2010 2340 0.72 12 Barwale 2009 1178 0.96
13 Hazaribag 2008 5194 0.84 13 Raipur 2010 2708 0.67 13 Paramakudi 2009 1499 0.46
14 Raipur 2008 3929 0.84 14 TNAU 2010 3711 0.61 14 Raipur 2009 1895 0.7
15 Ranchi 2008 5268 0.73 15 Hazaribag 2011 2507 0.82 15 TNAU 2009 1935 0.98
16 Barwale 2009 3618 0.96 16 Patna 2011 2865 0.92 16 DRR 2010 833 0.96
17 Faizabad 2009 3940 0.97 17 Raipur 2011 2708 0.93 17 Ranchi 2010 1478 0.94
18 Hazaribag 2009 4475 0.93 18 Ranchi 2011 2482 0.7 18 TNAU 2010 1415 0.94
19 Raipur 2009 5258 0.68 19 Barwale 2012 3537 0.82 19 DRR 2011 1341 0.71
20 UAS 2009 4648 0.94 20 Patna 2012 2997 0.93 20 Raipur 2011 1786 0.69
21 Barwale 2010 3904 0.9 21 Tripura 2012 3514 0.98 21 Rewa 2011 1797 0.89
22 Faizabad 2010 3925 0.9 22 DRR 2013 3243 0.87 22 TNAU 2011 1635 0.65
23 Hazaribag 2010 4326 0.9 23 Faizabad 2013 3783 0.91 23 Hazaribag 2012 1815 0.94
24 Raipur 2010 5610 0.81 24 Patna 2013 3436 0.82 24 Rewa 2012 1714 0.59
25 Ranchi 2010 3405 0.97 25 Raipur 2013 3294 0.91 25 Hazaribag 2013 1537 0.66
26 TNAU 2010 4047 0.5 26 Hazaribag 2014 2953 0.89 26 Ranchi 2013 1850 0.55
27 DRR 2011 5371 0.45 27 Raipur 2014 2455 0.93 27 Sabour 2013 1438 0.97
28 Faizabad 2011 4101 0.96 28 Ranchi 2014 2523 0.74 28 TNAU 2013 528 0.97
29 Hazaribag 2011 5488 0.8 29 DRR 2014 958 0.9
30 Patna 2011 4093 0.93 30 Faizabad 2014 602 0.96
31 Raipur 2011 5185 0.78 31 Tripura 2014 1747 0.61
32 Barwale 2012 6035 0.57
33 Faizabad 2012 4445 0.92
34 Hazaribag 2012 5252 0.94
35 Patna 2012 5918 0.94
36 Raipur 2012 6183 0.82
37 Sabour 2012 5135 0.77
38 TNAU 2012 4609 0.73
39 Tripura 2012 5620 0.7
40 DRR 2013 5453 0.89
41 Faizabad 2013 5061 0.87
42 Hazaribag 2013 4804 0.74
43 Patna 2013 5745 0.7
44 Raipur 2013 5151 0.96
45 TNAU 2013 4318 0.84
46 DRR 2014 5645 0.69
47 Faizabad 2014 4087 0.92
48 Hazaribag 2014 6002 0.87
49 IRRI-SA 2014 8336 0.36
50 Maruteru 2014 4451 0.98
51 Raipur 2014 6382 0.92
52 Sabour 2014 4775 0.6
53 Tripura 2014 5896 0.63
54 Varanasi 2014 8284 0.79
A. Kumar et al.
Field Crops Research 260 (2021) 107977
5
The genotypes screened included: (i) advanced breeding lines of
medium (100–120 days) duration generated using crosses of popular
high yielding varieties with a diverse array of donors for drought
tolerance, (ii) popular varieties as check, and (iii) traditional drought
tolerant landraces. Most of the popular checks and landraces used in this
study were medium duration, similar to developed breeding lines.
However, some checks like Swarna are long duration in maturity and
were planted 10 days earlier to synchronize owering of breeding lines
in the reproductive-stage. Each year, newly nominated lines from each
participating center and IRRI were jointly evaluated in an augmented-
design in observational yield trials (OYTs), both in irrigated control
conditions and under severe reproductive-stage stress, along with the
checks. Promising lines from OYT screens were promoted to the AYTs
(Verulkar et al., 2010). The criteria for selection of genotypes was based
on grain yield advantage of the genotype shown under irrigated control
as well as under moderate and severe drought stress conditions. The
details of the number of genotypes and their connectivity across years
are presented in Table 3.
2.2. Dissecting genetic and non-genetic trends
The analysis was based on the model dissecting genetic trends from
non-genetic trends in a two-stage analysis provided by Piepho et al.
(2014). Genetic trends are due to breeding efforts, which can be assessed
based on the year a genotype rst entered the trials (birth). Non-genetic
trends are due to agronomic efforts or the external factors that equally
affect all genotypes (trial year), and they can be assessed based on cal-
endar years.
The trials (site ×year combinations) were analyzed using a two-
stage analysis. In the rst stage, data from individual trials was
analyzed using a mixed model that considered genotypes as xed and
replicate and block effects as random to calculate the adjusted means for
the genotypes in the eth trial with an estimated variance-covariance
matrix Ve. If the adjusted means are sorted by trials (by site, year) and
then by genotypes within each trial, V is of the block-diagonal form
V= ⊕m
e=1Ve=
V10 … 0
0V2… 0
⋮ ⋮ ⋱ ⋮
0 0 … Vm
where ⊕is the direct sum operator, m is the number of trials and V
e
is the
estimated variance-covariance matrix of adjusted means in the eth trial.
A weighting matrix is given by DV−1
e, a diagonal matrix with diagonal
elements equal to those of V−1
e (Smith et al., 2001 and 2005).
In the second stage, for a given stress level, the basic model for the
adjusted means is given by
Yijk =
μ
+gi+yk+ (gy)ik +lj+ (ly)jk + (gl)ij + (gyl)ijk +
ε
ijk − − − − −
>
(1)
where
Yijk is the adjusted mean of the ith genotype in the jth site and kth
year,
μ
is the overall mean, gi is the main effect of the ith genotype, lj is
the main effect of the jth site, yk is the main effect of the kth year, (ly)jk is
the interaction of the jth site with the kth year, (gy)ik is the interaction of
the ith genotype with the kth year, (gl)ij is the interaction of the ith
genotype with the jth site, (gyl)ijk is the interaction of the ith genotype
with the jth site in the kth year and
ε
ijk is the error. The regression terms
for the time trends for the main effects of the genotype effect gi and the
year effect yk are then incorporated into the model. The genotypic main
effect gi is modeled as a function of the year of rst testing (ri) for the ith
variety
gi=βri+Hi
where ,β is the xed regression coefcient for the “genetic” trend and Hi
is the random deviation of from the genetic trend line, Hi∼N0,
σ
2
H
The year main effect can be modeled using the calendar year t
k
of the kth
year as
yk=γtk+Zk
where γ is the xed regression coefcient for the “agronomic” trend and
Zk is the random deviation from the agronomic trend line, Zk∼N0,
σ
2
Z.The model was tted using PROC HPMIXED of SAS (SAS 9.3).
Damesa et al. (2017) provided a SAS macro to calculate the weights
proposed by Smith et al. (2001) for the two-stage analysis. Percentage
change in genetic gain per year for each stress level due to the genetic
and non-genetic causes were estimated as;
reg.coeff
μ
+ (reg.coeff x start year mean)x 100
where
μ
is the overall intercept (the grand mean across all the groups
with no predictors), regression coefcient is β and γ for genetic and non-
genetic causes, respectively and start year is start of the period over
which gain was assessed.
2.3. Head-to-head trials
Head-to-head trials are a method of eld demonstration initiated in
the STRASA project, which allow the farmers to assess the yield per-
formance of new variety with the already existing one under variable
growing conditions in a rainfed system. The STRVs developed under
STRASA were evaluated in head-to-head trials in farmers’ elds in the
eastern Indian state of Odisha under two ecosystems: (i) the lowland and
(ii) mid -and -shallow lowland. In the current study, within the lowland
ecosystem, in addition to the topography, areas prone to submergence
are referred to lowland, areas prone to occurrence of both drought and/
or submergence in the same or different seasons have been referred to
mid lowland and areas prone to frequent drought stress are referred to
shallow lowland.
In the head-to-head trials, the pair of varieties to be compared were
grown in the same eld (half of the eld for each variety) or in two
adjacent elds with similar conditions. The head-to-head trials were
conducted using the farmers management practices in the particular
locations to assess the extent of yield gain achieved on the farmers’ elds
(Dar et al., 2018). The data was analyzed separately for the lowland and
shallow-and-medium lowland ecosystems.
The data was subjected to analysis of variance with xed effects for
variety, and random effects for districts and farmers nested within dis-
tricts. Yield of the ith variety from the kth farmer in the jth district is
Table 3
Details of the number of genotypes and their connectivity across years under
irrigated control and the two stress levels in AYT trials.
No. of years tested
No. of genotypes
Irrigated control Moderate
stress
Severe
stress
1 242 214 214
2 72 128 58
3 30 69 19
4 15 64 14
5 3 5 14
6 1 12 3
7 1 8
8 9 1
9 1 1
10 1
Total no. of genotypes 366 325 324
A. Kumar et al.
Field Crops Research 260 (2021) 107977
6
modelled by
yijk =
μ
+ti+ej+fjk + (te)ij +
ε
ijk (2)
where,
μ
is the overall mean, ti is the effect of the ith variety, ej is the
effect of the jth district, fjk is the effect of the kth farmer in the jth dis-
trict, teij is the effect of the district by variety interaction,
ε
ijk is the error.
Heterogeneity of error variances among environments was investigated
by examining a plot of the standardized residuals against the tted
response for both models (Raman et al., 2012; Hu et al., 2013). The
models with homogeneous and heterogeneous error variances for dis-
tricts were compared. The best model was chosen depending on the AIC
values. In each ecosystem, the individual varieties were then classied
into (i) STRVs and (ii) farmer’s variety. The model was rerun to compare
the performances of the two categories.
3. Results
3.1. Single trial analysis of AYT
The mean grain yield ranged from 2.5–8.0 t/ha in the irrigated
control trials, 2–3.7 t/ha in the moderate drought stress trials and
0.5–1.9 t/ha under severe drought stress trials. The trials with herita-
bility less than 0.30 were excluded from the subsequent analysis
(Johnson et al., 1955). This included 7 trials under control, 6 under
moderate stress and 5 under severe stress. Heritability of the trials
included in the analysis ranged from 0.36 to 0.99 under non-stress, 0.39
to 0.98 under moderate stress and 0.32 to 0.98 under severe stress
Fig. 1. Adjusted mean yields from single trial-wise analysis of AYT.
A. Kumar et al.
Field Crops Research 260 (2021) 107977
7
(Table 2). A plot of the trial-wise adjusted means under the irrigated
control and two stress levels is shown in Fig. 1(a, b & c).
3.2. Analysis across years and sites
Comparing the regression estimates for the genetic and the non-
genetic trends, it is observed that the genetic trend was positive with
an increase in yield at the rate of 0.034 t/ha (condence interval, CI:
0.007, 0.060) per year under irrigated control where the numbers in
parenthesis indicate the lower and upper condence limits at
α
=0.05.
The trend under moderate stress was 0.025 t/ha (CI: -0.004, 0.053) and
0.027 t/ha (CI: 0.004, 0.053) under severe stress. This can be inter-
preted as an increase of about 0.68 % p.a. under non-stress, 0.87 % p.a.
under moderate stress and 1.9 % p.a. under severe stress (Table 4). The
non-genetic trend was positive with an increase of about 0.122 t/ha (CI:
0.005, 0.239) under irrigated control and 0.05 t/ha (CI: -0.062, 0.135)
in moderate stress, respectively. Under severe stress, a negative trend
was found with a yield decrease of 0.021 t/ha (CI: -0.083, 0.051)
(Table 4). The percentage increase was about 2.3 % p.a. under non-
stress, 1.89 % p.a. under moderate stress and 1.5 % p.a. decrease
under severe stress. A plot of marginal means of the variety groups based
on the year of rst testing (r
i
) and the calendar year (t
k
) for the irrigated
control and two stress levels are presented in Fig. 2. In all the conditions,
the year ×site interaction variance was relatively large compared to the
variances of all effects involving genotype. The genotype ×year and
genotype ×site interaction variances were small compared to the gen-
otype ×site ×year interaction variance (Table 5).
3.3. Analysis of head-to-head trials
The varietal differences were non-signicant (p <0.05) in WS2017
and signicant in WS2018 in lowland trials while it was highly signi-
cant in both years in the shallow and medium lowland trials based on
model 3 (Supplementary Table S2). Swarna -Sub1 had produced the
highest yield in the lowland ecosystem in both years while DRR dhan 44
had yielded highest in the shallow and medium lowland situation
(Table 6). The STRVs outperformed the currently grown varieties on
farmers’ elds in all situations in both years (Fig. 3. a,b,c & d).
4. Discussion
The increase in frequency and intensity of drought due to climate
change necessitates the use of varieties that possess (i) drought resis-
tance together with high yield under favorable conditions (Serraj et al.,
2011), (ii) appropriate growth duration based on toposequence of the
land, (iii) resistance to biotic stresses, (iv) improved grain quality and
(v) other traits of economic importance. These goals can be achieved
with large-scale breeding programs targeted to achieve a continuous
cycle of varietal improvement, wherein older varieties are replaced with
new varieties that can meet farmers’ needs and thus deliver genetic
gains.
Genetic gain assessment is vital for evaluating genetic improvement,
selection efciency and for designing future breeding strategies (Fufa
et al., 2005). Breeding programs designed for the rainfed ecosystem
targeting the developing world will need to deliver higher rates of ge-
netic gain to cope with the 21 st century challenges of 50 %–60 %
greater demands for food commodities, climate change and natural
resource constraints (Genetic Gains Working Group, 2016). IRRI, in
collaboration with national research partners under STRASA, initiated
efforts for developing drought-tolerant rice varieties suitable for rainfed
lowland drought-prone areas of India. Many popular high yielding rice
varieties are highly susceptible to drought, hence IRRI implemented a
breeding strategy for the development and evaluation of breeding lines
in on-station trials under control as well as moderate stress and severe
reproductive stage drought stress (Kumar et al., 2007; Venuprasad et al.,
2007; Verulkar et al., 2010; Mandal et al., 2010) with the aim to
combine drought tolerance with high yield potential. Screening under
moderate and severe drought stress exposed the breeding lines to a
range of severity of reproductive stage drought stresses likely to prevail
in the rainfed regions. The efforts led to development of several high
yielding drought tolerant lines (Kumar et al., 2014). Genetic gain
assessment of grain yield in the present study clearly indicates that the
efforts have been successful in not only increasing the yield under
moderate and severe reproductive stage drought stress but also under
irrigated control. Further, the evaluation of breeding lines at segregating
generations as well as at the advanced stage under the three situations
ensured obtaining genetic gains under all the three conditions. The ge-
netic gain of 0.68 % under control, 0.87 % under moderate stress and 1.9
% under severe reproductive stage drought stress demonstrates the
success of our breeding strategy implemented between 2004–2014.
Earlier, Xangsayasane et al., 2013 reported that screening of lines under
intermittent drought in the dry season and terminal drought in the wet
season provided the magnitude of drought severity that was appropriate
identifying promising drought tolerant lines. Based on drought response
index (DRI), genotypes shown to be drought tolerant were consistent in
performance in intermittent and terminal drought screening (Xang-
sayasane et al., 2013). Promising genotypes with good yield potential
under irrigated control as well as under moderate and severe drought
stress had been previously selected based on drought yield index (DYI)
and mean yield index (MYI) in rainfed drought prone locations in
Eastern India (Raman et al., 2012).
The genetic gains achieved by the breeding programs in on-station
trials between 2004–2014 were also observed in the on-farm trials
conducted during 2017−2018. The performance of some of the devel-
oped breeding lines released as varieties such as Swarna -Sub 1 for
lowland area, Sahbhagi dhan, DRR dhan 42, DRR dhan 44, Bina dhan 11
for mid and shallow lowland areas were evaluated in comparison with
the varieties farmers were growing (Swarna, CR1009, Khandagiri, Lalat,
MTU1010, Naveen, Pooja, Sarala) in the rainfed regions in on-farm
trials. Swarna-Sub1 showed an average yield advantage of 0.315 and
0.510 t/ha in 2017 and 2018 respectively over mean performance of
varieties farmers were growing in the lowland including Swarna. In
Table 4
Estimates of regression coefcients for the genetic and non-genetic trends and
estimates of their group means from the analysis across sites and years for the
AYT trials under the irrigated control, moderate and severe drought stress.
Irrigated control Moderate stress Severe stress
Regression
coefcient
est std. err est std. err est std. err
β 0.034 0.014 0.025 0.015 0.027 0.012
γ 0.122 0.059 0.055 0.055 −0.021 0.032
Group means
Year r
i
t
k
r
i
t
k
r
i
t
k
2005 4.793 5.258
2006 4.673 3.861 2.730 2.656 1.384 1.164
2007 4.765 4.476 2.630 3.042 1.270 1.537
2008 4.682 4.398 2.593 2.343 1.279 1.401
2009 4.795 4.331 2.764 2.458 1.404 1.732
2010 5.033 4.119 2.724 2.999 1.507 1.284
2011 4.939 4.681 2.796 2.672 1.434 1.653
2012 5.047 5.212 2.948 3.290 1.787 1.529
2013 4.578 5.148 2.765 3.436 1.544 1.188
2014 4.450 6.271 3.221 2.272 1.195 1.315
average 4.775 4.775 2.797 2.797 1.422 1.422
% gain
#
0.68 2. 3 0.87 1.9 1.9 −1.5
Note: est – estimate; std. err – standard error. r
i
– year of rst testing – genetic
trend ; t
k
– trial year – non genetic trend; β is the xed regression coefcient for
the “genetic” trend r
i
; γ is the xed regression coefcient for the “agronomic”
trend t
k.
#
Percentage change in genetic gain per year for each stress level due to the
genetic and non-genetic causes were estimated using the formula explained
under subheading dissecting genetic and non-genetic trends in materials and
methods section.
A. Kumar et al.
Field Crops Research 260 (2021) 107977
8
shallow and medium lowland, the drought tolerant varieties such as
Sahbhagi dhan showed an average yield advantage of 0.452 and
0.554 t/ha in 2017 and 2018 respectively over the mean performance of
varieties under cultivation by farmers. These ndings are in agreement
with the Xangsayasane et al., 2014 who have earlier reported that
drought tolerance determined in either intermittent or terminal drought
in on-station trials was effective in predicting on-farm yield.
For long-term genetic improvement of yield, evaluating the genetic
variation present in the germplasm is a pre-requisite (Allard, 1960;
Falconer and Mackay, 2004), as availability of adequate variability
provides a range of choices from which selections can be made. Genetic
variability for yield under drought stress in rainfed lowland rice in both
dry and wet seasons have been documented (Venuprasad et al., 2007;
Kumar et al., 2007; Verulkar et al., 2010; Gouda et al., 2012; Saikumar
et al., 2016; Shaibu et al., 2017). The genotypic variance component
observed for yield under irrigated control and two stress levels in the
present study was larger than that of the genotype ×year and the gen-
otype ×site variance components suggesting enough genetic variability
in the breeding material used.
When assessing cultivar performance and genetic gains at multiple
sites and across years, breeding and varietal selection may not be the
only major factors responsible the increase in grain yields (Ahrends
et al., 2018; Muralidharan et al., 2019). Long-term genetic gains for
yield under a range of drought situations can be achieved by combining
genetics and crop management (Duvick et al., 2004; Cooper et al.,
2014). This is because responses to drought stress are complex and also
involve climatic, soil and agronomic factors and therefore predictions of
phenotypic response should be made as functions of genetic and envi-
ronment factors. Selection of superior genotypes depends on the quality
of these predictions (Sadras et al., 2013; Bustos-Korts et al., 2016). In the
present study, regression estimates show that in all stress levels, yield
increase is mainly caused by new varieties rather than by the agronomic
Fig. 2. Marginal means of the variety groups based on the year of rst testing (r
i
) and the calendar year (t
k
).
Table 5
REML parameters from the analysis across sites and years for the AYT trials under irrigated control and the two stress levels.
Cov Parm Irrigated control Moderate stress Severe stress
Estimate Standard Error p-value Estimate Standard Error p-value Estimate Standard Error p-value
Year 0.0841 0.1454 0.281 0.112 0.086 0.096 0.004 0.034 0.453
Gen 0.080 0.015 <.0001 0.043 0.013 0.001 0.023 0.008 0.003
Site 0.031 0.141 0.413 0 . . 0.034 0.041 0.206
year*site 0.918 0.231 <.0001 0.132 0.045 0.002 0.147 0.057 0.005
gen*year 0.000 0.010 0.500 0.000 0.011 0.500 0.003 0.007 0.343
gen*site 0.040 0.019 0.025 0.053 0.018 0.002 0.021 0.011 0.025
gen*year*site 0.339 0.028 <.0001 0.162 0.019 <.0001 0.161 0.013 <.0001
Residual 1 0 1 0 1 0
Note: gen – genotype; Cov Parm-covariance parameter; REML: restricted maximum likelihood.
A. Kumar et al.
Field Crops Research 260 (2021) 107977
9
factors. The contribution of the environmental factors under irrigated
control and moderate stress were positive while it was negative in the
case of severe stress trials. This could be attributed to the drought stress
in these environments. Assessing the size of the contributions of genetic,
environmental and management factors can have implications for
on-farm planning, testing breeding strategies and allocation of resources
for yield improvement (Anderson, 2010).
Determination of genetic progress in grain yield over a period has
been extensively studied in other grain crops such as wheat and maize
but has been limited in the case of rice. Peng et al. (2000) determined the
trend in rice yield of cultivars developed by IRRI between 1966 and
1996. Their study indicated a gain of 1% per year. Muralidharan et al.
(2002) analysed the grain yield performance of rice genotypes devel-
oped and tested since 1976–1997 in the METs performed worldwide
under different ecosystems in the international rice improvement trials
and concluded that there was no evidence for either a genetic gain or
loss in grain yields of genotypes developed for any of the ecosystems.
Further, Muralidharan et al. (2019) observed a signicant yield gap
between the projection of human population and national rice grain
production by estimating the genetic gain for yields in genotypes tested
in 11 rice ecosystems from 1995 to 2013 in India. Integrated genomic
technologies and policy interventions is needed to seal this yield gap and
to ensure rice availability to fulll the demands of growing population.
Bastasa et al. (2015) determined the annual genetic gain in rice yield
based on 28 cultivars representing different years of varietal release over
the time period 1966–2013 and reported an annual yield gain of 1% on
the basis of yield of IR64. Saito (2018) assessed the trend in grain yield
and its associated traits of rice varieties released during the last decades
in sub-Saharan Africa. The study showed considerable genetic gains in
rainfed systems and high yielding lowland varieties released between
1986 and 2013 had around 20 % higher yield than IR64.
In studies assessing the genetic gain for grain yield in different crops,
yield is regressed against the year of release to monitor the gains in
released varieties over time as also reported in other crops (Sayre et al.,
1997; Lopes et al., 2012; Masuka et al., 2017). An alternate method of
assessing long-term genetic gain based on MET data is to evaluate
cultivar performance trials sequentially over a period of time in the
collaborating test sites such as in the present study. Grain yield regressed
against the year of testing and the performance of the test varieties are
compared with that of common checks with the main objective of va-
riety release (Trethowan et al., 2002; Tadesse et al., 2013).
Among the drought tolerant varieties developed and released
following strategy of evaluation of breeding lines for grain yield under
irrigated control and reproductive stress, Sahbhagi dhan, was the rst
variety released in 2009 as a result of the collaboration between IRRI
and different Indian institutions for drought-prone shallow lowland
ecosystems of India (Basu et al., 2017) as well as for bunded uplands.
Sahbhagi dhan had a comparatively stable performance and adaptable
plant types for shallow rainfed lowland drought-prone ecosystems
(Verulkar et al., 2010; Kumar et al., 2012; Raman et al., 2012) and has
shown a yield advantage of 0.8–1.0 t/ha over other varieties under
drought conditions (Yamano et al., 2013) where the yield of irrigated
control varieties such as IR 64 collapsed (Dar et al., 2014). DRR dhan 44,
released in the year 2014 for cultivation under irrigated conditions, is
characterized by high yield under water limited conditions. Other
drought-tolerant varieties released for commercial cultivation in India
include DRR dhan 42, 43, 50 and CR dhan 204, 205, 801 and 802.
In lowland, Swarna- Sub1 performed better than any other variety
closely followed by CR 1990 Sub 1. In mid lowland, in lower bunds, Bina
dhan 11 performed exceedingly well whereas in mid lowland upper
bunds as well as in shallow lowland DRR dhan 44 performed better than
other varieties including the most presently cultivated variety MTU1010
(Table 6).
5. Conclusion
This study documents the signicant genetic gain for grain yield of a
breeding program targeting rainfed lowland rice in India that was based
on direct selection for grain yield under both irrigated control and
drought conditions. The study utilized extensive multi-season evalua-
tion in target environments under irrigated control, moderate drought
stress and severe drought stress between 2004–2014 with number of
popular varieties as checks to enable accurate estimation of the genetic
gain. The yield improvement of the newly developed stress-tolerant
varieties over the best currently grown varieties was also demon-
strated on farmers’ elds. The developed STRVs have potential to pro-
tect farmers from crop losses against an increasing impact of extreme
droughts under climate change. The results of this study shall assist
governments and policy makers to replace the currently grown decades-
old varieties from the seed chain and emphasize larger efforts for seed
multiplication and dissemination of the newly developed stress-tolerant
varieties in coordination with various stakeholders. The ndings of the
Table 6
Estimated variety means from the head-to-head trials at Odisha under lowland and shallow & medium lowland conditions.
Variety type Lowland Shallow & medium lowland
WS2017 WS2018 WS2017 WS2018
Mean std err Mean std err Mean std err Mean std err
Farmer 4.540 0.410 5.818 0.248 3.788 0.266 4.154 0.217
STRV 4.855 0.410 6.328 0.248 4.240 0.266 4.708 0.217
STRV
Bina dhan 11 4.193 0.267 4.621 0.233
Swarna Sub 1 4.873 0.438 6.2157 0.1741
CR 1009 Sub -1 4.846 0.482 6.5717 0.1924
DRR dhan 42 4.203 0.270
DRR dhan 44 4.522 0.276 4.777 0.243
Sahbhagi dhan 4.068 0.286 4.593 0.246
Farmer variety
CR 1009 3.795 0.592 6.2697 0.2094
Khandagiri 3.417 0.423 4.036 0.326
Lalat 3.732 0.270 3.978 0.239
MTU 1001 4.466 0.497 4.253 0.308 4.313 0.258
MTU 1010 3.891 0.312 4.138 0.250
Naveen 3.758 0.267 4.067 0.252
Pooja 4.430 0.478 5.3043 0.1952
Sarala 4.304 0.789 5.7207 0.3094
Swarna 4.693 0.439 6.119 0.1833 3.985 0.386 4.855 0.317
Swarna Shreya 4.870 0.359
Note: STRV-stress tolerant rice varieties.
A. Kumar et al.
Field Crops Research 260 (2021) 107977
10
study shall also encourage researchers to plan for effective evaluation of
the breeding programs products with aim to assess the success of the
breeding programs in terms of genetic gains achieved.
Availability of data and materials
The datasets supporting the conclusions of this article are provided
within the article.
Authors’ contribution
AK: was involved in design of overall experiment, drafting and
critical revision of MS; AR: was involved in experimental analysis,
interpretation of data, drafting and revising the manuscript; SY: was
involved in evaluating the lines at IRRI and revising the manuscript;
SBV: conducted trials and recorded measurements at IGKV Raipur;
NPM: performed trials at Hazaribagh, ONS and PS: conducted the trials
and recorded measurements at NRRI Cuttack, TR and JB: conducted the
trial and recorded observations at IIRR-Hyderabad; JLD: performed the
Fig. 3. District-wise predicted means of the STRV and farmer varieties from head to head trials.
A. Kumar et al.
Field Crops Research 260 (2021) 107977
11
trials at NDUAT-Ayodhya; SPD: conducted the trials at ICAR-Tripura,
SKS: conducted the trial at BHU-Varanasi; SPS: conducted the trial
and recorded data at BAU-Sabour; SK: conducted the trial at ICAR-
Patna; AJ: conducted trials and recorded observations at Barwale
foundation, Hyderabad; RC and SR: conducted trials and recorded ob-
servations at TNAU Coimbatore; HES and SH: conducted the trials and
recorded data at UAS-Bangalore, PS: conducted the trials and recorded
the required data at ANGRAU-Guntur, VC, JR and SN: were involved in
multiplication of seeds conducted the trials at IRRI-SAH, Hyderabad,
and distributed the multiplied seeds to partners at various locations; SN,
MD and MH: had monitored and evaluated the trials at various locations
in India; and AH: performed screening of donors for drought tolerance at
IRRI under the STRASA project. HPP: provided suggestions regarding
the experiments and statistical models and analysis and also helped in
editing of the MS. All authors have read and approved the nal
manuscript.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Declaration of Competing Interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Acknowledgements
This study was supported by the Bill and Melinda Gates Foundation
(BMGF) through STRASA (Stress Tolerant Rice for Africa and South Asia,
phase 1-3) project. The authors thank BMGF for the nancial support to
the study. The authors are also thankful to the Indian Council of Agri-
cultural Research (ICAR), New Delhi, as well as all the State Agricultural
Universities associated with the study for support provided in con-
ducting the trials at various locations in India. HPP was supported by
DFG grant PI 377/20-1.
Appendix A. Supplementary data
Supplementary material related to this article can be found, in the
online version, at doi:https://doi.org/10.1016/j.fcr.2020.107977.
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