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Cropping calendar options for rice–wheat production systems at high-altitudes
Field Crops Research 121 (2011) 158–167
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Cropping calendar options for rice–wheat production systems at high-altitudes Suchit Shrestha a , Folkard Asch a,∗ , Michael Dingkuhn b , Mathias Becker c a
University of Hohenheim, Institute for Plant Production and Agroecology in the Tropics and Subtropics, 380c, Garbenstr. 13, 70599 Stuttgart, Germany Agricultural Research for Developing Countries, CIRAD, TA 59/01 Avenue Agropolis, 34270 Montpellier, France c University of Bonn, Institute of Crop Science and Resource Conservation, Karlrobert-Kreiten-Straße 13, 53115 Bonn, Germany b
a r t i c l e
i n f o
Article history: Received 1 October 2010 Received in revised form 7 December 2010 Accepted 8 December 2010 Keywords: Cold sterility Nepal Oryza sativa Photo-thermal constants RIDEV
a b s t r a c t The onset of rains during dry to wet transition fallow periods in rice–wheat production systems in Nepal cause substantial losses of soil nitrogen if the system is improperly managed. To make use of available nutrients and water, this transition period can either be shortened by early rice planting, or be extended by late planting, allowing a third crop to be grown. Shifting planting dates would require rice genotypes adapted to the different environments. Crop duration is influenced by both vegetative and reproductive development, which in turn is influenced by the photo-thermal environment and genotypic responses to it. An experiment was conducted to derive genotypic photo-thermal constants from phenological observations on diverse rice cultivars, which were then applied to the concept of the phenological model RIDEV to design cropping calendar options. Environmental effects on variation of crop duration were determined by planting at different dates. The risk of yield losses to sterility caused by low temperatures was estimated by simulation. Thirty-one different genotypes of rice were planted at 8 dates in 15-day intervals starting 27 April 2004 at the experimental field of the Regional Agriculture Research Station, Lumle, Nepal. The shortest duration to flowering was observed for planting dates in late May and early June. Simulation of flowering dates with RIDEV yielded correct results only for the early planting dates. For later planting dates simulated flowering dates showed an increasing deviation from the observed. In most cultivars, minimum air temperature below 18 ◦ C during booting to heading stages caused near-total spikelet sterility and a specific delay in flowering. However, the chilling tolerant cultivars Chomrong and Machhapuchre-3 cultivated at high altitude showed less than 30% spikelet sterility even at 15 ◦ C. Simulating crop durations with the derived thermal constants allowed evaluating the different calendar options for high altitude systems. © 2010 Elsevier B.V. All rights reserved.
1. Introduction In Nepal, rice–wheat rotation systems are a major source for food (Prasad and Donald, 2005). The rice–wheat crop production system in Nepal includes a Dry-to-Wet Transition (DWT) fallow period of variable length depending on altitude between the harvest of wheat (usually starts from early March) after the cold and dry winter season and the transplanting of rice (usually starts from mid June) at the beginning of the monsoon season (Becker et al., 2007). DWT of 6–8 weeks in the Himalayan mid-hills (altitude 1500–2500 masl) renders nitrogen management difficult due to frequent changes in the aeration status of the soil caused by early rain spells (George et al., 1994; Pande and Becker, 2003). To better manage nutrient and water availability, the DWT can either be extended, thus potentially allowing a third crop (e.g. a short duration legume) to be grown during the DWT, or be shortened
∗ Corresponding author. Tel.: +49 71145922764; fax: +49 71145924207. E-mail address: [email protected] (F. Asch). 0378-4290/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.fcr.2010.12.006
to allow for a rice crop having longer duration and thus, higher yield potential. Both options require a shift in planting date for the rice, since the cropping period for wheat as winter crop cannot be changed. Shifting the planting date in the system requires rice genotypes adapted to the new growing environment. Crop duration is determined by plant development, which is known to be influenced by the photo-thermal environment (Dingkuhn et al., 1995). Shortening the DWT would require genotypes resistant to cold during the early vegetative phase of development, followed by increased thermal demand during later development stages to avoid a gap between maturity and planting of the subsequent wheat crop. Rice production at high altitude (above 1000 masl) is generally affected by chilling injury and spikelet sterility (Sthapit et al., 1997), limiting both the area of production and the length of the growing season (Sthapit and Shrestha, 1991). Thus, extending the DWT would require rice genotypes with a short duration, low thermal requirements for development, and cold tolerance during the reproductive stage in order to avoid yield losses due to cold sterility. In the late 1990s a decision support tool (RIDEV) was developed, allowing rice farmers to select genotypes based
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Fig. 1. Annual patterns of minimum, mean and maximum air temperature (◦ C), photoperiod (h), mean daily solar radiation (MJ m−2 d−1 ) averaged over a month, and precipitation (mm) during the field experimental period in 2004 at Lumle, Nepal. Square boxes in sequence are sowing dates including the recommended one, the smooth solid half sinus line represents photoperiod, the upper solid line shows maximum, the lower one minimum and in between is the mean air temperature, vertical bar lines represent rainfall and the dotted line depicts mean daily solar radiation.
on their photo-thermal requirements and on the risk of chilling induced spikelet sterility for specific cropping calendars in the Sahelian zone of Africa (Dingkuhn, 1995; Dingkuhn and Miezan, 1995; Dingkuhn et al., 1995). For 96 irrigated rice genotypes, photothermal constants describing genotypic responses to temperature and photoperiod were derived from sowing date experiments in the Sahel for use as model parameters. The scientific aim of the present study was to study, with the help of the RIDEV model, the phenology and spikelet sterility of local and introduced rice genotypes in response to the thermal environments associated with different sowing dates under irrigated altitude conditions in Nepal. The underlying applied objective was to develop a methodology to identify appropriate genotype × planting date combinations for the improved management of the DWT and develop cropping calendar options. For the parameterization of the model, a “mini rice garden” sowing date experiment was conducted to derive the photo-thermal constants for a set of 31 rice genotypes. 2. Materials and methods Experiments were conducted on research fields at the Regional Agricultural Research Station, Lumle (28◦ 18 N, 83◦ 49 E, 1740 masl) of Kaski district in Nepal between April and December 2004. This location falls within the sub-tropical summer rain zone with cool dry winters and warm humid summers. The total annual rainfall was 6094 mm with most of the rainfall concentrated in the monsoon season from May to October. The climatic pattern as recorded for the site in 2004 is shown in Fig. 1. Daily minimum and maximum temperature and rainfall data were obtained from the on-site meteorological station. Astronomical day length (photoperiod) from sunrise to sunset was calculated according to Frere and Popov (1979). 2.1. Genetic materials Thirty-one rice genotypes (Table 1) were included in the field trials, including eight cultivars (32Xuan-5C, I Kong Pao, IR28, IR64, IR31785-58-1-2-3-3, IR4630-22-2, ITA306 and Jaya) with known photothermal constants established under Sahelian conditions (Dingkuhn and Miezan, 1995). Among those IR4630-22-2 and
IR31785-58-1-2-3-3 represent references for photoperiod sensitive and day length neutral responses, respectively. CG14 a short duration traditional Oryza glaberrima cultivar from Senegal was also included for its photoperiod sensitivity, WAB 450-24-3-2-P18HB represents first generation NERICA developed by Africa Rice, and WAS 30-11-1-4-6-1-1-3 and WAS 30-11-1-4-6-1-2, selected for high yield potential under Sahelian conditions were included as cold sensitivity checks. The collection of 31 cultivars was completed by 4 long, 11 medium and 4 short duration cultivars (Table 1) from Nepal adapted to different altitudinal agro-ecosystems. Those comprised the japonica types Chaite-1 and Chaite-4, short duration spring rice, adapted to sub-tropical environments and widely cultivated in the southern low-land warm plains (Terai) of Nepal, Khumal-11 and Chainung-242, medium duration temperate japonica types commonly grown in the mid hills and the temperate japonica types Chomrong and Machhapuchre-3, long duration and cold tolerant adapted to the high altitude rice systems. The local indica types comprised short duration spring rice (Chaite-6 and Hardinath-1) and medium duration rainy season rice (Makawanpur-1, Sabitri, Masuli) cultivated in the Terai and foot hills. Makwanpur-1 was included in the trial for its resistance to gall midge; Sabitri was included for its resistance to blast and Masuli for its fine grain and straw quality. In addition, 6 indica types adapted to the cool temperate mid hill environment were included, namely Himali, Kanchan, Khumal-2, Khumal-4, Khumal6, Khmual-9, and Manjushree-2. Jethobudho (indica) known as Pokhareli was included as long duration, traditional cold-sensitive landrace adapted to low root-zone temperatures.
2.2. Mini rice garden The recommended sowing date for rice at mid hills altitude is mid June (corresponding to sowing date number 4 in this study). Thus to allow testing options for earlier and later sowing; 3 earlier sowing dates, predating recommended sowing by a maximum of 6 weeks and 4 sowing dates delaying sowing by a maximum of 8 weeks were chosen. Therefore, all 31 genotypes were sown in 15-day intervals at eight consecutive dates starting 27 April 2004. At each sowing date a block including the 31 genotypes (Table 1) was sown. The design was a non replicated, completely randomized block design. Seed was pre-soaked for 24 h and kept in a ger-
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Table 1 Characteristics of the genetic materials used in the study. Cultivars in bold are check cultivars. Abbreviations: sat., Oryza sativa; gla., Oryza glaberrima; pro., Oryza sativa X Oryza glaberrima progeny; Impr., improved; Trad., traditional. SN
Cultivar
Species
Sub-species
Type
Duration
Country of origin
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
32Xuan-5C CG14 Chainung-242 Chaite-2 Chaite-4 Chaite-6 Chomrong Hardinath-1 (BG-14-42) Himali I KONG PAO IR 28 IR 31785-58-1-2-3-3 IR 4630-22-2 IR 64 ITA 306 (Sahel 202) JAYA Jethobudho Kanchan Khumal-11 Khumal-2 Khumal-4 Khumal-6 Khumal-9 Machhapuchre-3 Makawanpur-1 Manjushree-2 Masuli Sabitri WAB 450-24-3-2-P18-HB WAS 30-11-1-4-6-1-1-3 WAS 30-11-1-4-6-1-2
sat gla sat sat sat sat sat sat sat sat sat sat sat sat sat sat sat sat sat sat sat sat sat sat sat sat sat sat pro sat sat
indica glaberrima japonica japonica japonica indica japonica indica indica indica indica indica indica indica indica indica indica indica japonica indica indica indica indica japonica indica indica indica indica nerica indica indica
Impr Trad Impr Impr Impr Impr Trad Impr Impr Impr Impr Impr Impr Impr Impr Impr Trad Impr Impr Impr Impr Impr Impr Impr Impr Impr Impr Impr Impr Impr Impr
Short Short Medium Short Short Short Short Short Medium Short Short Short Medium Short Medium Medium Long Medium Medium Medium Medium Medium Medium Short Medium Long Medium Medium Short Medium Medium
China Senegal Taiwan Philippines Philippines Nepal Nepal Nepal Philippines Taiwan Philippines Philippines Philippines Philippines Nigeria India Nepal Philippines Nepal Nepal Nepal Nepal Philippines Nepal Sri Lanka Nepal Malaysia Philippines Cote d’Ivoire Senegal Senegal
mination chamber for 48 h. Pre-germinated sprouted seeds were sown. Ninety seeds of each genotype per planting date were dibbled seeded into 45 pockets of equal distance in one square meter plot size. The soil was a well drained acidic sandy loam. To all plots fertilizer was applied at a rate of 120:80:60 (N:P:K) kg ha−1 using urea, diammonium phosphate, and muriate of potasssium as recommended for the region. Nitrogen was applied as 1 basal dose (40 kg ha−1 ) and 2 top dressings of 40 kg ha−1 each at tillering and flowering stages. P and K were applied as basal dose. Hand weeding was done as required. Sowing and harvesting were done manually. Water supply was mainly rainfall with additional irrigation to keep at least 2 cm of ponded water layer. Pesticides were not used. Crop ontogeny, characterized by seedling emergence, first tillering, panicle initiation, booting, heading, 50% flowering, 100% flowering, and physiological maturity were visually assessed on a daily basis on all plots throughout the experimental period. All observations including yield components were made in each plot on the 25 central hills. To avoid bias in the determination of spikelet sterility ratio (SSR), panicles from 10 randomly selected plants out of the 25 central hills were bulk sampled. Numbers of filled and unfilled grains were counted and weighted. 2.3. RIDEV model Based on eight planting dates, photo-thermal constants were determined for each genotype following the procedure described by Dingkuhn et al. (1995) and the RIDEVCAL parameter optimization procedure (Dingkuhn and Wopereis, unpublished) which is a field-based photo-thermal model for flowering. In the model, crop duration is considered a function of thermal time measured in growing degree days. A genotype specific thermal time needs to be accumulated to progress from germination to panicle initiation (Tsum ) coinciding with BVP if photoperiod (PP) is inductive. This
thermal time requirement increases if the genotype is photoperiod sensitive (CPP) and if day length is non-inductive (long) at the time of the end of the basic vegetative phase (BVP). A short photoperiod thus accelerates and a long photoperiod delays flowering (Vergara and Chang, 1985). Rice plants do not respond to photoperiod during the entire vegetative period from sowing to flowering (Vergara and Chang, 1985), if the plants are insensitive to photoperiod. Roberts and Summerfield (1987), divided the vegetative period into three phases: the pre-inductive, the inductive, and the post-inductive phase. Plants are sensitive to photoperiod only during the inductive phase (Yin and Kropff, 1998). The basic concept of the RIDEV model is the summation of heat units and a linear thermal response of development rate. Thermal time is calculated on the basis of cardinal temperatures base temperature (Tbase , lower limit for development) and optimum temperature (Topt , upper limit). Although only daily min and max temperatures are used as input, diurnal temperature profiles are estimated by the model, which is necessary because ambient temperature may exceed the Tbase and Topt limits only during part of the day. Photoperiodism is modeled via a slope constant (CPP) and a basic vegetative phase (BVP). BVP is the shortest period to flowering across tested environments expressed in days. Photoperiod and transplanting shock act as modifiers of total heat requirement (Tsum ), thereby having greater effects on duration at low than at high temperatures. All these photo-thermal constants (Tbase , Topt , Tsum , BVP and CPP) were considered as genotypic and calibrated by optimization (RIDEVCAL). The model uses air temperature at 2m as input and transforms it into a physiological temperature (state variable Tphys ), which is the estimated heat (within the Tbase − Topt interval) sensed by shoot apex, whose position within the canopy depends on the development stage (submerged until internode elongation, then moving up and finally located at top of the canopy at flowering). Floodwater temperature is calculated with an empirical model considering weather and ground cover as described by
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Table 2 Maximum and minimum crop duration from sowing to 50% flowering (fmax and fmin ) in Ndiaye (1991–1993) and Lumle (April–August 2004), fmax and fmin in ◦ C d is the thermal degree days using air temperature and critical temperature equals to 10 for all genotypes and the n is the number of observations. Cultivar
Ndiaye
32Xuan-5C CG14 Chainung-242 Chaite-2 Chaite-4 Chaite-6 Chomrong Hardinath-1 Himali I KONG PAO IR 28 IR 64 IR31785-58-1-2-3-3 IR4630-22-2 ITA306 JAYA Jethoburo Kanchan Khumal-11 Khumal-2 Khumal-4 Khumal–6 Khumal-9 Machhapuchre-3 Makawanpur-1 Manjushree-2 Masuli Sabitri WAB450-24-3-2-P18-HB WAS30-11-1-4-6-1-1-3 WAS30-11-1-4-6-1-2
Lumle
fmax (d)
fmin (d)
fmax (d)
fmin (d)
fmax (◦ C d)
124 – – – – – – – – 122 120 120 125 134 129 137 – – – – – – – – – – – – – – –
70 – – – – – – – – 76 69 74 65 88 78 81 – – – – – – – – – – – – – – –
134 120 142 132 113 120 120 125 157 152 156 151 142 201 171 187 213 126 137 134 142 152 152 133 152 160 167 167 107 153 176
125 108 107 111 91 110 99 97 119 134 146 112 117 137 139 149 158 116 106 105 111 105 121 97 137 119 150 149 95 134 124
1021 377 1222 845 619 857 1398 481 1184 1309 1445 950 1125 1379 1458 1315 1493 1014 1144 1229 932 1180 1228 1233 861 1162 1394 1277 919 656 1037
Dingkuhn et al. (1995). RIDEV is implemented in GW-Basic 3.23 (Microsoft) language. The basic equations used for the study are: Tmean − Tbase Tsum
(1)
DR =
Tmean − Tbase (Tsum ∗ CPPA) + {Tsum ∗ CPPB ∗ (PPpi − PPcrit)}
(2)
DR =
Tmean − Tbase TsumPPcrit ∗ {1 + CPP ∗ (PPpi − PPcrit)}
(3)
3. Results
(4)
3.1. Duration to flowering
DR
Eq. (1) considers thermal parameters to estimate development rate (DR), where Tmean is the mean air temperature. Higher deviation in estimated duration to flowering using thermal equation (1) can be adjusted by incorporating the effect of photoperiod as in Eq. (2). Eq. (3) is a simplified photothermal equation to estimate development rate considering critical photoperiod. In this equation, Tsum is multiplied by the intercept (CPPA) of the regression for photoperiodic correction (TsumPPcrit ) and the slope (CPPB) was considered to be the photoperiod slope constant (CPP). For every 1 h increase in photoperiod, the expression of the Eq. (1) was improved. Hourly increase in photoperiod was expressed as the difference of photoperiod during panicle initiation (PPpi) and critical photoperiod (PPcrit). PPcrit is the photoperiod below which rice genotypes are insensitive. It is estimated as quadratic polynomial relation between prediction error (PE) of duration to panicle initiation and PPpi (Dingkuhn et al., 1995). Eq. (4) estimates development stage (DS) as a summation of development rate. Crop duration (where, DS = 1 for flowering) and spikelet sterility (mean Tmin from booting to heading is considered) due to cold and heat were simulated using MicroSoft Excel 2007 for all 31 geno-
No. obs. (n) 4 5 6 5 6 5 6 5 5 4 3 5 5 4 4 3 3 5 6 6 5 6 5 6 3 5 3 3 6 3 4
types for 36 years (from 1970 to 2005) based on the RIDEV concept. Spikelet sterility due to cold for chilling tolerant genotypes were considered as 100%, 50% and 0% if mean Tmin from booting to heading are 14, 15 and 20 ◦ C, respectively, whereas for other genotypes 16, 18 and 20 ◦ C for cold and 42, 37 and 32 ◦ C for heat were considered as in RIDEV. Chomrong (short duration) and IR64 (medium duration) were selected for the development of cropping calendar options.
DR =
DS =
fmin (◦ C d) 1011 332 1159 799 553 839 1181 457 1114 1247 1423 876 1066 1276 1406 1281 1395 942 1079 1148 854 1057 1155 1107 845 1116 1379 1215 873 646 935
Crop duration varied among cultivars and planting dates. On average the difference between the longest and shortest duration to flowering was 28 days, whereas duration to flowering varied among the eight planting dates between 10 and 55 days for the local and between 9 and 64 days for the international check varieties. All international check varieties showed longer durations to flowering in Lumle (Nepal) as compared to Ndiaye (Senegal). On average, duration to flowering observed in Lumle was increased by factor 1.3 for maximum and factor 1.8 for minimum duration (Table 2). Duration to flowering was shortest for most of the genotypes when sown between May 12 and June 11. Cultivars Sabitri, Masuli, Jaya, and Jethobudho showed the shortest duration to flowering when sown on April 27. For the cold tolerant cultivars Machhapuchre3 and Chomrong, duration to flowering was shortest when sown between June 11 and July 11. 3.2. RIDEV model The RIDEV model needs genotypic photothermal constants (PTC) to predict the duration of phenological stages. PTC of the rice
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Table 3 Photo-thermal constants (Tbase and Tsum ) determined with RIDEVCAL for Lumle conditions. Previously published values obtained with the same method under semi-arid Sahel conditions are in parenthesis (Dingkuhn and Miezan, 1995). Thermal constants (Tbase and Tsum ) derived from linear regression approach based on number of observation (n) and coefficient of determination (r2 ) are tabulated on the right hand side column which were used for prediction of days to flowering. Cultivar name
32Xuan-5C CG14 Chainung-242 Chaite-2 Chaite-4 Chaite-6 Chomrong Hardinath-1 Himali I KONG PAO IR 28 IR 64 IR31785-58-1-2-3-3 IR4630-22-2 ITA306 JAYA Jethobudho Kanchan Khumal-11 Khumal-2 Khumal-4 Khumal-6 Khumal-9 Machhapuchre-3 Makawanpur-1 Manjushree-2 Masuli Sabitri WAB450-24-3-2-P18-HB WAS30-11-1-4-6-1-1-3 WAS30-11-1-4-6-1-2
RIDEVCAL
Derived (linear regression)
Tbase (◦ C)
Tsum (◦ C d)
Tbase (◦ C)
(9.8) 10.2 11.3 6.7 10.3 11.1 9.4 6.3 13.3 8.7 (11.8) 8.4 (11.0) 9.0 (9.6) 10.1 (13.6) 9.1 (11.7) 8.1 (13.3) 7.5 (11.2) 8.7 8.0 8.6 7.8 7.0 10.2 7.4 8.5 6.0 11.8 8.3 8.6 9.3 8.2 12.7 10.1
(1191) 834 541 1213 798 626 849 908 310 511 (1016) 1165 (1067) 1218 (1194) 854 (744) 920 (1048) 1142 (814) 1336 (1240) 1211 1083 1035 683 1143 700 1009 710 1101 812 1091 1320 1009 791 614 727
12.3 17.1 9.7 13.2 14.4 12.8 8.1 15.9 11.0 10.8 10.4 12.6 11.3 10.9 10.1 11.5 11.1 12.0 10.3 9.6 12.7 10.5 10.8 9.1 14.1 11.0 10.9 11.9 11.3 15.4 12.8
cultivars Jaya, ITA306, IR4630-22-2, IR31785-58-1-2-3-3, I Kong Pao, IR28, IR64, 32Xuan5C and Sahel108 were already published based on the experiments conducted in Sahelian environments (Dingkuhn and Miezan, 1995). Using those PTC to simulate duration to flowering for Lumle conditions with the RIDEV model was not successful. Particularly for the later planting dates RIDEV failed to simulate the observed durations, due to a large effect of minor minimum temperature changes on the simulation of heat unit accumulation. Therefore, new PTC were derived from data observed in the field experiment. The optimization program RIDEVCAL using procedures described by Dingkuhn and Miezan (1995) was used to optimize base temperature (Tbase ), thermal time required to progress from germination to flowering (Tsum ), optimum temperature for the maximum rate of development (Topt ), duration of the basic vegetative phase (BVP), and the photoperiodic slope constant indicating the increase for Tsum as percentage per hour increase in daylength (CPP) for the climatic conditions in Lumle. Tbase and Tsum estimation based on RIDEVCAL were shown in Table 3. For most of the international check varieties photo thermal constants derived from the field data in Nepal deviated from those established under Sahelian conditions (e.g. lower Tbase and Topt , higher Tsum ). Based on RIDEVCAL, Tbase ranged from 6 to 13 ◦ C with most of the cultivars showing Tbase between 8 and 9 ◦ C. Chomrong and Machhapuchre3, recommended for cultivation in the high hills were found to have the lowest Tbase (6 ◦ C). Hardinath-1 recommended for the hot lower plain areas (Terai) had the highest Tbase (13 ◦ C) and the lowest Tsum (310 ◦ C) for flowering at 12 h daylength. Topt ranged from 20 to 26 ◦ C. For many cultivars Topt was between 20 and 22 ◦ C. CPP ranged from 0.357 to 27.53 in cultivar Makawanpur-1 and Himali, respectively. Simulation of crop duration with RIDEV using PTC derived from RIDEVCAL for Lumle conditions under-estimated crop duration
Tsum (◦ C d) 1026 375 1198 831 605 860 1246 482 1160 1289 1443 923 1107 1332 1442 1307 1440 989 1119 1209 909 1124 1195 1180 865 1148 1397 1252 905 661 992
r2
No. obs. (n)
0.990 0.934 0.967 0.967 0.959 0.984 0.801 0.996 0.962 0.907 0.960 0.970 0.939 0.977 0.973 0.994 0.969 0.821 0.942 0.890 0.942 0.915 0.956 0.834 0.994 0.991 0.994 0.917 0.900 0.997 0.972
4 5 6 5 6 5 6 5 5 4 3 5 5 4 4 3 3 5 6 6 5 6 5 6 3 5 3 3 6 3 4
Fig. 2. Relationship between observed and RIDEV simulated duration to flowering for all cultivars and sowing for which flowering occurred. Selected genotypes represent the genetic diversity in terms of duration of the total set of cultivars.
from sowing to flowering and over-estimated duration to maturity for some genotypes. However, chilling tolerant cultivars had less variation in duration to flowering in later planting dates. About 60% of the simulated crop duration to flowering showed less than 5 days differences from observed and about 40% showed more than 5 days differences (Fig. 2). Differences between observed and simulated duration to flowering increased linearly as a function of mean Tmin from booting to heading for mean Tmin lower than 17 ◦ C (Fig. 3). In all the planting dates, genotypes were exposed to Tmin below 18 ◦ C
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ommendations. Therefore, the core concept of the RIDEV model was adapted to a simplified simulation using spread sheet calculation procedures (Microsoft Excel 2007) to predict phenological stages and spikelet sterility for cropping calendar development. 3.3. Simplified simulation of photothermal effects on phenology
Fig. 3. Relationship between errors in the simulation of flowering (observed minus simulated duration) and the mean, minimum daily air temperature actually experienced by the crop between booting and heading stages, Lumle 2004.
between booting and heading, constituting a major difference to most of the Sahelian planting dates (Dingkuhn et al., 1995). As RIDEV was developed in a Sahelian environment, the application of the model for cropping calendar development in the high hills of Nepal would need further validation, as the duration to flowering and maturity is not sufficiently accurate for calendar rec-
Development rate can be defined as progress to a singular developmental event such as flowering, provided that other factors such as photoperiod sensitivity are absent. If that is the case, and if the observed thermal variation is small enough to remain within the range of linear response of plant development, flowering can be predicted with two genotypic constants Tbase and Tsum . Thereby, Tbase is the temperature at which progress to flowering is zero and Tsum is the accrued number of heat units required for flowering, with the daily heat dose equaling Tmean − Tbase . A linear thermal effect on development is commonly observed if Tmean does not exceed the genotypic optimum temperature Topt . We assume that Topt had only little effect in the present study, since maximum air temperature in the field trials (Fig. 1) rarely exceeded genotypic Topt . Thus, Tbase and Tsum can be estimated from the linear regression of thermal duration to flowering (in terms of accrued ◦ C to the basis of zero) against the accrued number of days (Fig. 4). The intercept of this relationship thereby provides an estimate of Tsum (which is assumed to remain constant despite the variation in absolute time to flowering) and the slope provides an estimate of Tbase . Furthermore, the degree of linearity of the observed correlation across sowing dates permits to evaluate whether for the
Fig. 4. Linear relationships between sum of mean air temperature from sowing to flowering (Tsum ) in dd and duration from sowing to flowering stage in days (d). In the equation shown, the slope (a) represents Tbase (accumulation of heat per day) in ◦ C and the intercept (b) signifies Tsum (dd) of BVP without taking photoperiod into consideration. The number of data points in the graphs corresponds to the number of sowing dates for which flowering occurred.
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Fig. 5. Relationship between observed and estimated days to flowering. (a) Based on experimental derived Tbase and Tsum (see also Fig. 4); (b) based on Tbase reduced by 0.25 ◦ C. (b inset) Differences between observed (f obs) and simulated (f sim) days to flowering from (a) were subjected to a stepwise decrement of 0.1 ◦ C (X) for Tbase , reducing the simulation error to smaller than 10 for the late planting dates at Tbase – 0.25 ◦ C.
environments considered, photoperiodism had a significant effect or not. Tbase and Tsum were estimated by linear regression as described above for all 31 genotypes. Fig. 4 exemplarily shows the results for six contrasting genotypes. Derived Tbase and Tsum were entered into Eqs. (1) and (4) and used to predict flowering as days after sowing with the simplified simulation procedure. The simulation yielded accurate estimations of duration to flowering for the early sowing dates but strongly over-estimated duration to flowering for the later planting dates (Fig. 5a). A detailed analysis of the amount of heat units accumulated in the simulation as compared to the Tsum required for flowering in the later sowing dates showed only minor differences in Tsum . This was due to minor differences in daily accumulation of heat units due to a slightly over estimated Tbase , easily within the range of 4–6 ◦ C reported by Dingkuhn et al. (1995) and most probably due to accuracy of the field based observations. An analysis of the simulation error showed substantial over estimation of days to flowering for the latest planting dates. Stepwise reduction of Tbase showed that a small decrement of 0.25 ◦ C from the derived Tbase was sufficient to predict flowering for almost all cultivars and planting dates (Fig. 5b inset) except for three outliers (Fig. 5b). These corrected thermal parameters Tbase and Tsum (Table 3) were used to simulate days to flowering for all genotypes for different planting dates to develop appropriate cropping calendar option. 3.4. Thermal effects on spikelet sterility High spikelet sterility was observed in late planting dates. Chilling at booting to heading stage (DS 0.85–1.00) is associated with spikelet sterility (Dingkuhn et al., 1995). The critical minimum temperature below which spikelets are sterile at this stage was about 18 ◦ C for all except two cultivars, namely Chomrong and Machhapuchre-3 (Fig. 6). In Lumle, average Tmin between booting and heading was below 18 ◦ C for all planting dates. Spikelet sterility was thus generally high and frequently close to 100%. Chilling tolerant cultivars Chomrong showed 30% spikelet sterility even at mean Tmin of 15 ◦ C between booting and heading (Fig. 6). 4. Discussion Rice–wheat crop rotation system is characterized by more or less pronounced transition period between dry and wet seasons. Management of transition period, by growing seasonal crops,
Fig. 6. Relationship between spikelet sterility and the mean, minimum air temperature actually observed between booting and heading stages, individually determined for each cultivar and date in Lumle, 2004. Except for Chomrong and Machhapuchre-3, other cultivars were broken down into two parts and the relations were linearly regressed taking Khumal-11 as a reference cultivar due to its spikelet sterility variation from less than 30% to 100% within the range of less than 14–18 ◦ C mean Tmin from booting to heading.
leads to nutrient conservation, increased nutrient use efficiency, and increased sustainability of the system (Pande and Becker, 2003). In the mid hills, transition period is not sufficiently long to grow seasonal crops. Growing transition seasonal crops and/or the replacement of the dry season wheat crop by a crop, like potato, can result in a substantial deviation from the recommended planting dates of rice (Shrestha et al., 2007). The potential option may be shifting of rice planting dates to earlier for better yield and manage soil nutrients by incorporating wheat straw for temporary immobilization of soil nitrogen during DWT periods (Becker et al., 2005; Shrestha et al., 2005) in the mid hills. 4.1. Constraints to RIDEV as a tool to adapt rice cropping calendars in the high hills of Nepal Rice phenology depends on the photo-thermal environment and changes in planting date influence the vegetative development and
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Fig. 7. Simulated seasonal options for rice–wheat cropping calendars as limited by chilling induced spikelet sterility (solid lines with vertical bars for standard deviation for 36 years) and crop duration (horizontal bars indicating days from sowing to maturity) for Lumle. Simulation was based on 36 years (1970–2005) with daily increments for minimum and maximum temperatures. PPSD, period of potential sowing dates; WWSS, warm and wet summer season; CDWS, cold and dry winter season. Test varieties were Chomrong and IR64 for spikelet sterility and the calendar was based on Chomrong alone due to its lower percentage of spikelet sterility.
the reproductive development, consequently results different crop durations. The RIDEV model developed at WARDA, Senegal to predict crop duration using PTC of rice genotypes when applied to Lumle conditions either over or under estimated the duration of phenological stages of the local rice genotypes. The RIDEV model requires PTC of the rice genotypes to be grown in the system. Establishing PTC from field experiments can lead to difficulties predicting the exact crop duration with the RIDEV model if the thermal and photoperiodic conditions (micro climate) are not closely monitored and are insufficiently variable among planting dates. Field based studies would either result in wrong genotypic constants, wrong predictions of crop duration, or both (Dingkuhn et al., 1995). This approach requires optimization programs for respective genotypic constants to determine best-fitting models. For this, a rice garden experiment should be conducted over a year with different planting dates. To derive PTC more accurately, many successive dates are required (Craufurd et al., 2003) for better regression output. The RIDEV model assumes that genotypes have genetically fixed PTC. Extrapolation beyond the period of the experiment shows possible errors. Determination of PTC under laboratory conditions is easier than in the field condition as temperature and photoperiod varies over time. However, field based studies enable the characterization of large numbers of genotypes which is not possible in growth chambers (Dingkuhn et al., 1995). Tbase is usually determined by extrapolation. Under the experimental conditions, phenology studies based exclusively on air temperature over estimated Tbase by 4–6 ◦ C. This bias was greatest at mid season when the leaf canopy was developed but culms not yet elongated, and greater in the dry than in the wet season (Dingkuhn et al., 1995). The effective temperature for development in RIDEV is based on the actual temperature at the growing point, which in paddy rice is located in the flood water for development stages up to booting. Thus, for development to flowering, the largest share of time, the water temperature is more important than the air temperature. In Lumle, the relationship between daily air temperature amplitude, ground cover, and water temperature as established for the RIDEV model may not have been valid due to the fact that water temperature in the field trial was in general low (Fig. 7) due to the higher altitude and close proximity to the Himalaya ice belt range and lower energy input owing to the lower angle of the sun. Energy needs to heat up water depend on the specific heat capacity of water (4.19 kJ kg−1 ◦ C). Thus, estimations of flood water temperature need to take into account the minimum water temperature as a starting point, the water
depth and the potential heat absorption at the current latitude. Minimum water temperature in Lumle was about 10 ◦ C less than in Ndiaye in Senegal during entire cropping season. Heat energy required to raise water temperature up to 10 ◦ C requires 43 kJ for 1 m2 area of 1 mm (equivalent to 1 kg) water table without any canopy cover. To increase water temperature up to 10 ◦ C of 5 cm water table for 1 m2 area heat energy required will be 2 MJ, which is further dissipated to the soil and atmosphere. In Lumle, average solar radiation during the rice cropping season is 17 MJ m−2 d−1 . This energy is further dissipated due to reflection, sensible heat and latent heat causing insufficient radiation to increase the water temperature in Lumle. Under the experimental condition, incorrect calculation of daily temperature means biased Tbase by about 1–2 ◦ C (Dingkuhn et al., 1995). Except for cultivar 32Xuan-5C, Tbase derived for Lumle (using RIDEVCAL) was lower than under Sahelian conditions. Including an energy balance taking into account the predominant water temperature, insulation, and the physical properties of water in combination with whether data and groundcover most likely would render RIDEV better suitable to estimate crop duration in higher altitude cropping systems. Photoperiod in Senegal is longer during summer time (June–July) and shorter in winter as compared to Nepal (Fig. 7). Few days in March and September have same hour of photoperiod in both locations. Dingkuhn and Asch (1999) have assumed PPcrit to be 11.5 h and photoperiod sensitivity a linear function of photoperiod above PPcrit. Variation of PPcrit with temperature has been reported (Roberts and Summerfield, 1987). Genotypic variation in PPcrit has not been adequately determined. As for temperature, at least four photoperiod sensitivity levels (insensitive; less, medium and highly sensitive) would determine the response more accurately due to genotypic variation. Photoperiod-sensitivity among traditional cultivars is greatest at lower latitudes, decreasing as latitude increases (Craufurd and Qi, 2001). As in the high hills in Nepal, temperature had a much stronger effect on crop duration than any deviation in photoperiod, PP was ignored when simulating the duration of phenological stages for potential adaptation of cropping calendars in those systems. 4.2. Cropping calendar options In the high hills of Nepal, rice is cultivated in the summer wet season and wheat in the dry season with the residual soil moisture during fall and winter. Both crops receive little irrigation and depend strongly on rainfall, thus, grain yields in theses areas are
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Fig. 8. Minimum water temperature in the paddy field and photoperiod in Ndiaye, Senegal and Lumle Nepal.
low. The common transplanting time of a month old rice seedling starts from 15 June. Chomrong (local adapted genotype, short duration) and IR64 (international check variety, medium duration) were selected to simulate seasonal patterns of crop duration and spikelet sterility for several planting dates in Lumle (Fig. 8). Simulations were performed with Microsoft Excel using 36 years (from 1970 to 2005) of weather data. In the adapted model, PP was not included and critical temperatures for 50% spikelet sterility were set to 18 and 16 ◦ C for IR64 and Chomrong, respectively. Simulations generally resulted in a higher percentage of spikelet sterility due to chilling in IR64 than in Chomrong. For Chomrong, simulation showed a potentially save period of planting dates between mid-April and end of July with cold induced spikelet sterility not exceeding 50% and a total length of crop duration of 135 days when transplanted before 15 July. This would theoretically allow for two different options of introducing a third crop into the calendar: either a 90 day duration legume crop at the beginning of the year between the wheat and the rice or a similar crop of 90 day duration at the end of the growing period between the rice and the wheat. The first option would require a short duration legume such as arrow leaf clover or mucuna as green manure whereas the second option would allow for a cold tolerant grain legume to be grown with the residual moisture. Both options will need to be tested within the system for their effects on nutrient and water availability as well as for their economic viability. In addition, a third option would allow increasing the duration of rice in the system by early transplanting of long duration rice such as Jethoburo. Such a genotype would require a high Tsum , a well developed chilling and drought tolerance during the early vegetative stage as well as some degree of cold tolerance at the early generative stage to avoid yield loses due to spikelet sterility. Such early sown long duration rice would take full advantage of the mineralization peak induced by the early rains (Pande and Becker, 2003) and would decrease erosion losses induced by the long fallow period.
5. Conclusions We have shown that applying a non-modified version of RIDEV for the development of cropping calendar option in the rice–wheat system in the high hills in Nepal was not possible due to an overem-
phasis on photoperiod, an insufficiently sensitive Tbase simulation and an inaccurate simulation of water temperature and, thus, the effective temperature at the meristem. Nonetheless, applying the principles on which RIDEV is based in a simplified simulation allowed to accurately simulate the duration of phenological stages of local rice varieties as well as the level of chilling induced spikelet sterility for variable planting dates in Lumle. Locally adapted cultivars with high chilling tolerance exist (e.g. Chomrong), allowing shifting of planting dates for rice for additional options of including a third crop in the calendar. Future research efforts are needed to evaluate and validate the proposed options for their effects on the cropping system with regard to soil fertility, water availability and economic viability.
Acknowledgements German Academic Exchange Program (DAAD) is gratefully acknowledged by Suchit Shrestha for providing the scholarship to support this research. We thank the Regional Agriculture Research Center (RARS, Lumle), Soil Science Division (SSD, Khumaltar) and the Nepal Agricultural Research Council (NARC, Singhadarbar Plaza) for providing administrative and logistic support.
References Becker, M., Asch, F., Maskey, S.L., Pande, K.R., Shah, S.C., Shrestha, S.P., 2007. Effects of transition season management on soil N dynamics and system N balances in rice–wheat rotations of Nepal. Field Crops Research 103, 98–108. Becker, M., Maskey, S.L., Shah, S.C., 2005. Managing rice–wheat cropping systems of Nepal for a more efficient soil N use. In: Tielkes, E., Hülsebusch, C., Häuser, I., Deininger, A., Becker, K. (Eds.), The Global Food and Product Chain—Dynamics, Innovations, Conflicts, Strategies. MDD Media Digitaldruck Copy Shop Büromaschinen GmbH, Stuttgart-Hohenheim, p. 289. Craufurd, P.Q., Hauser, I.E., Dingkuhn, M., 2003. Photothermal responses of O. sativa and O. glaberrima varieties and interspecific progenies from West Africa. Field Crops Research 83, 313–324. Craufurd, P.Q., Qi, A., 2001. Photothermal adaptation of sorghum (Sorghum bicolour) in Nigeria. Agricultural and Forest Meteorology 108, 199–211. Dingkuhn, M., 1995. Climatic determinants of irrigated rice performance in the Sahel. III. Characterizing environments by simulating the crop’s photothermal responses. Agricultural Systems 48, 435–456. Dingkuhn, M., Asch, F., 1999. Phenological responses of Oryza sativa, O. glaberrima and inter-specific rice cultivars on a toposquence in West Africa. Euphytica 110, 109–126.
S. Shrestha et al. / Field Crops Research 121 (2011) 158–167 Dingkuhn, M., Miezan, K.M., 1995. Climatic determinants of irrigated rice performance in the Sahel. II. Validation of photothermal constants and characterization of genotypes. Agricultural Systems 48, 411–434. Dingkuhn, M., Sow, A., Samb, A., Diack, S., Asch, F., 1995. Climatic determination of irrigated rice performance in the Sahel. I. Photothermal and micro climatic responses of flowering. Agricultural Systems 48, 385–410. Frere, M., Popov, G., 1979. Agrometeorological crop monitoring and forecasting. FAO, Plant Production and Protection Paper No. 17, p. 64. George, T., Ladha, J.K., Garrity, D.P., Buresh, R.J., 1994. Legumes as nitrate catch crops during the dry-to-wet transition in lowland rice cropping systems. Agronomy Journal 86, 267–273. Pande, K.R., Becker, M., 2003. Seasonal soil nitrogen dynamics in rice–wheat cropping systems of Nepal. Journal of Plant Nutrition and Soil Science 166, 499–506. Prasad, R., Donald, L.S., 2005. Rice–Wheat Cropping Systems. Advances in Agronomy. Academic Press, pp. 255–339. Roberts, E.H., Summerfield, R.J., 1987. Measurement and prediction of flowering in annual crops. In: Antherton, J.G. (Ed.), Manipulation of Flowering. Butterworths, London, pp. 17–50. Shrestha, S.P., Diwani, T., Becker, M., Pande, K.R., 2005. Wheat straw application can reduce N losses from rice–wheat cropping systems in Nepal. In: Tielkes, E., Hülsebusch, C., Häuser, I., Deininger, A., Becker, K. (Eds.), The Global
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Food and Product Chain—Dynamics, Innovations, Conflicts, Strategies. MDD Media Digitaldruck Copy Shop Büromaschinen GmbH, Stuttgart-Hohenheim, p. 290. Shrestha, S.P., Asch, F., Becker, M., 2007. Phenological responses of rice genotypes to varying thermal environments in Nepal. In: Barkmann, J., Bürkert, A., Hensel, O., Hörstgen-Schwark, G., Hülsebusch, C., Kleinn, C., Kühne, R., Muuss, U., Ploeger, A., Schlecht, E., Schwarze, S., Worbes, M. (Eds.), Utilisation of Diversity in Land Use Systems: Sustainable and Organic Approaches to Meet Human Needs. Cuvillier Verlag Göttingen, Witzenhausen-Kassel, p. 448. Sthapit, B.R., Joshi, K.D., Witcombe, J.R., 1997. Farmers participatory high altitude rice breeding in Nepal: providing choice and utilizing farmers’ expertise. In: Sperling, L., Loevinsohn, M. (Eds.), Using Diversity: Enhancing and Maintaining Genetic Resources On-Farm. The International Development Research Centre, pp. 27–38. Sthapit, B.R., Shrestha, K.P., 1991. Breeding for cold tolerance at reproductive phase in the high hills of Nepal. International Rice Research Newsletter 16 (5), p14. Vergara, B.S., Chang, T.T., 1985. The Flowering Response of the Rice Plant to Photoperiod: A Review of the Literature. The International Rice Research Institute, ˜ Los Banos, Laguna, Philippines, p. 61. Yin, X., Kropff, M.J., 1998. The effect of photoperiod on interval between panicle initiation and flowering in rice. Field Crops Research 57, 301–307.
Contents lists available at ScienceDirect
Field Crops Research journal homepage: www.elsevier.com/locate/fcr
Cropping calendar options for rice–wheat production systems at high-altitudes Suchit Shrestha a , Folkard Asch a,∗ , Michael Dingkuhn b , Mathias Becker c a
University of Hohenheim, Institute for Plant Production and Agroecology in the Tropics and Subtropics, 380c, Garbenstr. 13, 70599 Stuttgart, Germany Agricultural Research for Developing Countries, CIRAD, TA 59/01 Avenue Agropolis, 34270 Montpellier, France c University of Bonn, Institute of Crop Science and Resource Conservation, Karlrobert-Kreiten-Straße 13, 53115 Bonn, Germany b
a r t i c l e
i n f o
Article history: Received 1 October 2010 Received in revised form 7 December 2010 Accepted 8 December 2010 Keywords: Cold sterility Nepal Oryza sativa Photo-thermal constants RIDEV
a b s t r a c t The onset of rains during dry to wet transition fallow periods in rice–wheat production systems in Nepal cause substantial losses of soil nitrogen if the system is improperly managed. To make use of available nutrients and water, this transition period can either be shortened by early rice planting, or be extended by late planting, allowing a third crop to be grown. Shifting planting dates would require rice genotypes adapted to the different environments. Crop duration is influenced by both vegetative and reproductive development, which in turn is influenced by the photo-thermal environment and genotypic responses to it. An experiment was conducted to derive genotypic photo-thermal constants from phenological observations on diverse rice cultivars, which were then applied to the concept of the phenological model RIDEV to design cropping calendar options. Environmental effects on variation of crop duration were determined by planting at different dates. The risk of yield losses to sterility caused by low temperatures was estimated by simulation. Thirty-one different genotypes of rice were planted at 8 dates in 15-day intervals starting 27 April 2004 at the experimental field of the Regional Agriculture Research Station, Lumle, Nepal. The shortest duration to flowering was observed for planting dates in late May and early June. Simulation of flowering dates with RIDEV yielded correct results only for the early planting dates. For later planting dates simulated flowering dates showed an increasing deviation from the observed. In most cultivars, minimum air temperature below 18 ◦ C during booting to heading stages caused near-total spikelet sterility and a specific delay in flowering. However, the chilling tolerant cultivars Chomrong and Machhapuchre-3 cultivated at high altitude showed less than 30% spikelet sterility even at 15 ◦ C. Simulating crop durations with the derived thermal constants allowed evaluating the different calendar options for high altitude systems. © 2010 Elsevier B.V. All rights reserved.
1. Introduction In Nepal, rice–wheat rotation systems are a major source for food (Prasad and Donald, 2005). The rice–wheat crop production system in Nepal includes a Dry-to-Wet Transition (DWT) fallow period of variable length depending on altitude between the harvest of wheat (usually starts from early March) after the cold and dry winter season and the transplanting of rice (usually starts from mid June) at the beginning of the monsoon season (Becker et al., 2007). DWT of 6–8 weeks in the Himalayan mid-hills (altitude 1500–2500 masl) renders nitrogen management difficult due to frequent changes in the aeration status of the soil caused by early rain spells (George et al., 1994; Pande and Becker, 2003). To better manage nutrient and water availability, the DWT can either be extended, thus potentially allowing a third crop (e.g. a short duration legume) to be grown during the DWT, or be shortened
∗ Corresponding author. Tel.: +49 71145922764; fax: +49 71145924207. E-mail address: [email protected] (F. Asch). 0378-4290/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.fcr.2010.12.006
to allow for a rice crop having longer duration and thus, higher yield potential. Both options require a shift in planting date for the rice, since the cropping period for wheat as winter crop cannot be changed. Shifting the planting date in the system requires rice genotypes adapted to the new growing environment. Crop duration is determined by plant development, which is known to be influenced by the photo-thermal environment (Dingkuhn et al., 1995). Shortening the DWT would require genotypes resistant to cold during the early vegetative phase of development, followed by increased thermal demand during later development stages to avoid a gap between maturity and planting of the subsequent wheat crop. Rice production at high altitude (above 1000 masl) is generally affected by chilling injury and spikelet sterility (Sthapit et al., 1997), limiting both the area of production and the length of the growing season (Sthapit and Shrestha, 1991). Thus, extending the DWT would require rice genotypes with a short duration, low thermal requirements for development, and cold tolerance during the reproductive stage in order to avoid yield losses due to cold sterility. In the late 1990s a decision support tool (RIDEV) was developed, allowing rice farmers to select genotypes based
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Fig. 1. Annual patterns of minimum, mean and maximum air temperature (◦ C), photoperiod (h), mean daily solar radiation (MJ m−2 d−1 ) averaged over a month, and precipitation (mm) during the field experimental period in 2004 at Lumle, Nepal. Square boxes in sequence are sowing dates including the recommended one, the smooth solid half sinus line represents photoperiod, the upper solid line shows maximum, the lower one minimum and in between is the mean air temperature, vertical bar lines represent rainfall and the dotted line depicts mean daily solar radiation.
on their photo-thermal requirements and on the risk of chilling induced spikelet sterility for specific cropping calendars in the Sahelian zone of Africa (Dingkuhn, 1995; Dingkuhn and Miezan, 1995; Dingkuhn et al., 1995). For 96 irrigated rice genotypes, photothermal constants describing genotypic responses to temperature and photoperiod were derived from sowing date experiments in the Sahel for use as model parameters. The scientific aim of the present study was to study, with the help of the RIDEV model, the phenology and spikelet sterility of local and introduced rice genotypes in response to the thermal environments associated with different sowing dates under irrigated altitude conditions in Nepal. The underlying applied objective was to develop a methodology to identify appropriate genotype × planting date combinations for the improved management of the DWT and develop cropping calendar options. For the parameterization of the model, a “mini rice garden” sowing date experiment was conducted to derive the photo-thermal constants for a set of 31 rice genotypes. 2. Materials and methods Experiments were conducted on research fields at the Regional Agricultural Research Station, Lumle (28◦ 18 N, 83◦ 49 E, 1740 masl) of Kaski district in Nepal between April and December 2004. This location falls within the sub-tropical summer rain zone with cool dry winters and warm humid summers. The total annual rainfall was 6094 mm with most of the rainfall concentrated in the monsoon season from May to October. The climatic pattern as recorded for the site in 2004 is shown in Fig. 1. Daily minimum and maximum temperature and rainfall data were obtained from the on-site meteorological station. Astronomical day length (photoperiod) from sunrise to sunset was calculated according to Frere and Popov (1979). 2.1. Genetic materials Thirty-one rice genotypes (Table 1) were included in the field trials, including eight cultivars (32Xuan-5C, I Kong Pao, IR28, IR64, IR31785-58-1-2-3-3, IR4630-22-2, ITA306 and Jaya) with known photothermal constants established under Sahelian conditions (Dingkuhn and Miezan, 1995). Among those IR4630-22-2 and
IR31785-58-1-2-3-3 represent references for photoperiod sensitive and day length neutral responses, respectively. CG14 a short duration traditional Oryza glaberrima cultivar from Senegal was also included for its photoperiod sensitivity, WAB 450-24-3-2-P18HB represents first generation NERICA developed by Africa Rice, and WAS 30-11-1-4-6-1-1-3 and WAS 30-11-1-4-6-1-2, selected for high yield potential under Sahelian conditions were included as cold sensitivity checks. The collection of 31 cultivars was completed by 4 long, 11 medium and 4 short duration cultivars (Table 1) from Nepal adapted to different altitudinal agro-ecosystems. Those comprised the japonica types Chaite-1 and Chaite-4, short duration spring rice, adapted to sub-tropical environments and widely cultivated in the southern low-land warm plains (Terai) of Nepal, Khumal-11 and Chainung-242, medium duration temperate japonica types commonly grown in the mid hills and the temperate japonica types Chomrong and Machhapuchre-3, long duration and cold tolerant adapted to the high altitude rice systems. The local indica types comprised short duration spring rice (Chaite-6 and Hardinath-1) and medium duration rainy season rice (Makawanpur-1, Sabitri, Masuli) cultivated in the Terai and foot hills. Makwanpur-1 was included in the trial for its resistance to gall midge; Sabitri was included for its resistance to blast and Masuli for its fine grain and straw quality. In addition, 6 indica types adapted to the cool temperate mid hill environment were included, namely Himali, Kanchan, Khumal-2, Khumal-4, Khumal6, Khmual-9, and Manjushree-2. Jethobudho (indica) known as Pokhareli was included as long duration, traditional cold-sensitive landrace adapted to low root-zone temperatures.
2.2. Mini rice garden The recommended sowing date for rice at mid hills altitude is mid June (corresponding to sowing date number 4 in this study). Thus to allow testing options for earlier and later sowing; 3 earlier sowing dates, predating recommended sowing by a maximum of 6 weeks and 4 sowing dates delaying sowing by a maximum of 8 weeks were chosen. Therefore, all 31 genotypes were sown in 15-day intervals at eight consecutive dates starting 27 April 2004. At each sowing date a block including the 31 genotypes (Table 1) was sown. The design was a non replicated, completely randomized block design. Seed was pre-soaked for 24 h and kept in a ger-
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Table 1 Characteristics of the genetic materials used in the study. Cultivars in bold are check cultivars. Abbreviations: sat., Oryza sativa; gla., Oryza glaberrima; pro., Oryza sativa X Oryza glaberrima progeny; Impr., improved; Trad., traditional. SN
Cultivar
Species
Sub-species
Type
Duration
Country of origin
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
32Xuan-5C CG14 Chainung-242 Chaite-2 Chaite-4 Chaite-6 Chomrong Hardinath-1 (BG-14-42) Himali I KONG PAO IR 28 IR 31785-58-1-2-3-3 IR 4630-22-2 IR 64 ITA 306 (Sahel 202) JAYA Jethobudho Kanchan Khumal-11 Khumal-2 Khumal-4 Khumal-6 Khumal-9 Machhapuchre-3 Makawanpur-1 Manjushree-2 Masuli Sabitri WAB 450-24-3-2-P18-HB WAS 30-11-1-4-6-1-1-3 WAS 30-11-1-4-6-1-2
sat gla sat sat sat sat sat sat sat sat sat sat sat sat sat sat sat sat sat sat sat sat sat sat sat sat sat sat pro sat sat
indica glaberrima japonica japonica japonica indica japonica indica indica indica indica indica indica indica indica indica indica indica japonica indica indica indica indica japonica indica indica indica indica nerica indica indica
Impr Trad Impr Impr Impr Impr Trad Impr Impr Impr Impr Impr Impr Impr Impr Impr Trad Impr Impr Impr Impr Impr Impr Impr Impr Impr Impr Impr Impr Impr Impr
Short Short Medium Short Short Short Short Short Medium Short Short Short Medium Short Medium Medium Long Medium Medium Medium Medium Medium Medium Short Medium Long Medium Medium Short Medium Medium
China Senegal Taiwan Philippines Philippines Nepal Nepal Nepal Philippines Taiwan Philippines Philippines Philippines Philippines Nigeria India Nepal Philippines Nepal Nepal Nepal Nepal Philippines Nepal Sri Lanka Nepal Malaysia Philippines Cote d’Ivoire Senegal Senegal
mination chamber for 48 h. Pre-germinated sprouted seeds were sown. Ninety seeds of each genotype per planting date were dibbled seeded into 45 pockets of equal distance in one square meter plot size. The soil was a well drained acidic sandy loam. To all plots fertilizer was applied at a rate of 120:80:60 (N:P:K) kg ha−1 using urea, diammonium phosphate, and muriate of potasssium as recommended for the region. Nitrogen was applied as 1 basal dose (40 kg ha−1 ) and 2 top dressings of 40 kg ha−1 each at tillering and flowering stages. P and K were applied as basal dose. Hand weeding was done as required. Sowing and harvesting were done manually. Water supply was mainly rainfall with additional irrigation to keep at least 2 cm of ponded water layer. Pesticides were not used. Crop ontogeny, characterized by seedling emergence, first tillering, panicle initiation, booting, heading, 50% flowering, 100% flowering, and physiological maturity were visually assessed on a daily basis on all plots throughout the experimental period. All observations including yield components were made in each plot on the 25 central hills. To avoid bias in the determination of spikelet sterility ratio (SSR), panicles from 10 randomly selected plants out of the 25 central hills were bulk sampled. Numbers of filled and unfilled grains were counted and weighted. 2.3. RIDEV model Based on eight planting dates, photo-thermal constants were determined for each genotype following the procedure described by Dingkuhn et al. (1995) and the RIDEVCAL parameter optimization procedure (Dingkuhn and Wopereis, unpublished) which is a field-based photo-thermal model for flowering. In the model, crop duration is considered a function of thermal time measured in growing degree days. A genotype specific thermal time needs to be accumulated to progress from germination to panicle initiation (Tsum ) coinciding with BVP if photoperiod (PP) is inductive. This
thermal time requirement increases if the genotype is photoperiod sensitive (CPP) and if day length is non-inductive (long) at the time of the end of the basic vegetative phase (BVP). A short photoperiod thus accelerates and a long photoperiod delays flowering (Vergara and Chang, 1985). Rice plants do not respond to photoperiod during the entire vegetative period from sowing to flowering (Vergara and Chang, 1985), if the plants are insensitive to photoperiod. Roberts and Summerfield (1987), divided the vegetative period into three phases: the pre-inductive, the inductive, and the post-inductive phase. Plants are sensitive to photoperiod only during the inductive phase (Yin and Kropff, 1998). The basic concept of the RIDEV model is the summation of heat units and a linear thermal response of development rate. Thermal time is calculated on the basis of cardinal temperatures base temperature (Tbase , lower limit for development) and optimum temperature (Topt , upper limit). Although only daily min and max temperatures are used as input, diurnal temperature profiles are estimated by the model, which is necessary because ambient temperature may exceed the Tbase and Topt limits only during part of the day. Photoperiodism is modeled via a slope constant (CPP) and a basic vegetative phase (BVP). BVP is the shortest period to flowering across tested environments expressed in days. Photoperiod and transplanting shock act as modifiers of total heat requirement (Tsum ), thereby having greater effects on duration at low than at high temperatures. All these photo-thermal constants (Tbase , Topt , Tsum , BVP and CPP) were considered as genotypic and calibrated by optimization (RIDEVCAL). The model uses air temperature at 2m as input and transforms it into a physiological temperature (state variable Tphys ), which is the estimated heat (within the Tbase − Topt interval) sensed by shoot apex, whose position within the canopy depends on the development stage (submerged until internode elongation, then moving up and finally located at top of the canopy at flowering). Floodwater temperature is calculated with an empirical model considering weather and ground cover as described by
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Table 2 Maximum and minimum crop duration from sowing to 50% flowering (fmax and fmin ) in Ndiaye (1991–1993) and Lumle (April–August 2004), fmax and fmin in ◦ C d is the thermal degree days using air temperature and critical temperature equals to 10 for all genotypes and the n is the number of observations. Cultivar
Ndiaye
32Xuan-5C CG14 Chainung-242 Chaite-2 Chaite-4 Chaite-6 Chomrong Hardinath-1 Himali I KONG PAO IR 28 IR 64 IR31785-58-1-2-3-3 IR4630-22-2 ITA306 JAYA Jethoburo Kanchan Khumal-11 Khumal-2 Khumal-4 Khumal–6 Khumal-9 Machhapuchre-3 Makawanpur-1 Manjushree-2 Masuli Sabitri WAB450-24-3-2-P18-HB WAS30-11-1-4-6-1-1-3 WAS30-11-1-4-6-1-2
Lumle
fmax (d)
fmin (d)
fmax (d)
fmin (d)
fmax (◦ C d)
124 – – – – – – – – 122 120 120 125 134 129 137 – – – – – – – – – – – – – – –
70 – – – – – – – – 76 69 74 65 88 78 81 – – – – – – – – – – – – – – –
134 120 142 132 113 120 120 125 157 152 156 151 142 201 171 187 213 126 137 134 142 152 152 133 152 160 167 167 107 153 176
125 108 107 111 91 110 99 97 119 134 146 112 117 137 139 149 158 116 106 105 111 105 121 97 137 119 150 149 95 134 124
1021 377 1222 845 619 857 1398 481 1184 1309 1445 950 1125 1379 1458 1315 1493 1014 1144 1229 932 1180 1228 1233 861 1162 1394 1277 919 656 1037
Dingkuhn et al. (1995). RIDEV is implemented in GW-Basic 3.23 (Microsoft) language. The basic equations used for the study are: Tmean − Tbase Tsum
(1)
DR =
Tmean − Tbase (Tsum ∗ CPPA) + {Tsum ∗ CPPB ∗ (PPpi − PPcrit)}
(2)
DR =
Tmean − Tbase TsumPPcrit ∗ {1 + CPP ∗ (PPpi − PPcrit)}
(3)
3. Results
(4)
3.1. Duration to flowering
DR
Eq. (1) considers thermal parameters to estimate development rate (DR), where Tmean is the mean air temperature. Higher deviation in estimated duration to flowering using thermal equation (1) can be adjusted by incorporating the effect of photoperiod as in Eq. (2). Eq. (3) is a simplified photothermal equation to estimate development rate considering critical photoperiod. In this equation, Tsum is multiplied by the intercept (CPPA) of the regression for photoperiodic correction (TsumPPcrit ) and the slope (CPPB) was considered to be the photoperiod slope constant (CPP). For every 1 h increase in photoperiod, the expression of the Eq. (1) was improved. Hourly increase in photoperiod was expressed as the difference of photoperiod during panicle initiation (PPpi) and critical photoperiod (PPcrit). PPcrit is the photoperiod below which rice genotypes are insensitive. It is estimated as quadratic polynomial relation between prediction error (PE) of duration to panicle initiation and PPpi (Dingkuhn et al., 1995). Eq. (4) estimates development stage (DS) as a summation of development rate. Crop duration (where, DS = 1 for flowering) and spikelet sterility (mean Tmin from booting to heading is considered) due to cold and heat were simulated using MicroSoft Excel 2007 for all 31 geno-
No. obs. (n) 4 5 6 5 6 5 6 5 5 4 3 5 5 4 4 3 3 5 6 6 5 6 5 6 3 5 3 3 6 3 4
types for 36 years (from 1970 to 2005) based on the RIDEV concept. Spikelet sterility due to cold for chilling tolerant genotypes were considered as 100%, 50% and 0% if mean Tmin from booting to heading are 14, 15 and 20 ◦ C, respectively, whereas for other genotypes 16, 18 and 20 ◦ C for cold and 42, 37 and 32 ◦ C for heat were considered as in RIDEV. Chomrong (short duration) and IR64 (medium duration) were selected for the development of cropping calendar options.
DR =
DS =
fmin (◦ C d) 1011 332 1159 799 553 839 1181 457 1114 1247 1423 876 1066 1276 1406 1281 1395 942 1079 1148 854 1057 1155 1107 845 1116 1379 1215 873 646 935
Crop duration varied among cultivars and planting dates. On average the difference between the longest and shortest duration to flowering was 28 days, whereas duration to flowering varied among the eight planting dates between 10 and 55 days for the local and between 9 and 64 days for the international check varieties. All international check varieties showed longer durations to flowering in Lumle (Nepal) as compared to Ndiaye (Senegal). On average, duration to flowering observed in Lumle was increased by factor 1.3 for maximum and factor 1.8 for minimum duration (Table 2). Duration to flowering was shortest for most of the genotypes when sown between May 12 and June 11. Cultivars Sabitri, Masuli, Jaya, and Jethobudho showed the shortest duration to flowering when sown on April 27. For the cold tolerant cultivars Machhapuchre3 and Chomrong, duration to flowering was shortest when sown between June 11 and July 11. 3.2. RIDEV model The RIDEV model needs genotypic photothermal constants (PTC) to predict the duration of phenological stages. PTC of the rice
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Table 3 Photo-thermal constants (Tbase and Tsum ) determined with RIDEVCAL for Lumle conditions. Previously published values obtained with the same method under semi-arid Sahel conditions are in parenthesis (Dingkuhn and Miezan, 1995). Thermal constants (Tbase and Tsum ) derived from linear regression approach based on number of observation (n) and coefficient of determination (r2 ) are tabulated on the right hand side column which were used for prediction of days to flowering. Cultivar name
32Xuan-5C CG14 Chainung-242 Chaite-2 Chaite-4 Chaite-6 Chomrong Hardinath-1 Himali I KONG PAO IR 28 IR 64 IR31785-58-1-2-3-3 IR4630-22-2 ITA306 JAYA Jethobudho Kanchan Khumal-11 Khumal-2 Khumal-4 Khumal-6 Khumal-9 Machhapuchre-3 Makawanpur-1 Manjushree-2 Masuli Sabitri WAB450-24-3-2-P18-HB WAS30-11-1-4-6-1-1-3 WAS30-11-1-4-6-1-2
RIDEVCAL
Derived (linear regression)
Tbase (◦ C)
Tsum (◦ C d)
Tbase (◦ C)
(9.8) 10.2 11.3 6.7 10.3 11.1 9.4 6.3 13.3 8.7 (11.8) 8.4 (11.0) 9.0 (9.6) 10.1 (13.6) 9.1 (11.7) 8.1 (13.3) 7.5 (11.2) 8.7 8.0 8.6 7.8 7.0 10.2 7.4 8.5 6.0 11.8 8.3 8.6 9.3 8.2 12.7 10.1
(1191) 834 541 1213 798 626 849 908 310 511 (1016) 1165 (1067) 1218 (1194) 854 (744) 920 (1048) 1142 (814) 1336 (1240) 1211 1083 1035 683 1143 700 1009 710 1101 812 1091 1320 1009 791 614 727
12.3 17.1 9.7 13.2 14.4 12.8 8.1 15.9 11.0 10.8 10.4 12.6 11.3 10.9 10.1 11.5 11.1 12.0 10.3 9.6 12.7 10.5 10.8 9.1 14.1 11.0 10.9 11.9 11.3 15.4 12.8
cultivars Jaya, ITA306, IR4630-22-2, IR31785-58-1-2-3-3, I Kong Pao, IR28, IR64, 32Xuan5C and Sahel108 were already published based on the experiments conducted in Sahelian environments (Dingkuhn and Miezan, 1995). Using those PTC to simulate duration to flowering for Lumle conditions with the RIDEV model was not successful. Particularly for the later planting dates RIDEV failed to simulate the observed durations, due to a large effect of minor minimum temperature changes on the simulation of heat unit accumulation. Therefore, new PTC were derived from data observed in the field experiment. The optimization program RIDEVCAL using procedures described by Dingkuhn and Miezan (1995) was used to optimize base temperature (Tbase ), thermal time required to progress from germination to flowering (Tsum ), optimum temperature for the maximum rate of development (Topt ), duration of the basic vegetative phase (BVP), and the photoperiodic slope constant indicating the increase for Tsum as percentage per hour increase in daylength (CPP) for the climatic conditions in Lumle. Tbase and Tsum estimation based on RIDEVCAL were shown in Table 3. For most of the international check varieties photo thermal constants derived from the field data in Nepal deviated from those established under Sahelian conditions (e.g. lower Tbase and Topt , higher Tsum ). Based on RIDEVCAL, Tbase ranged from 6 to 13 ◦ C with most of the cultivars showing Tbase between 8 and 9 ◦ C. Chomrong and Machhapuchre3, recommended for cultivation in the high hills were found to have the lowest Tbase (6 ◦ C). Hardinath-1 recommended for the hot lower plain areas (Terai) had the highest Tbase (13 ◦ C) and the lowest Tsum (310 ◦ C) for flowering at 12 h daylength. Topt ranged from 20 to 26 ◦ C. For many cultivars Topt was between 20 and 22 ◦ C. CPP ranged from 0.357 to 27.53 in cultivar Makawanpur-1 and Himali, respectively. Simulation of crop duration with RIDEV using PTC derived from RIDEVCAL for Lumle conditions under-estimated crop duration
Tsum (◦ C d) 1026 375 1198 831 605 860 1246 482 1160 1289 1443 923 1107 1332 1442 1307 1440 989 1119 1209 909 1124 1195 1180 865 1148 1397 1252 905 661 992
r2
No. obs. (n)
0.990 0.934 0.967 0.967 0.959 0.984 0.801 0.996 0.962 0.907 0.960 0.970 0.939 0.977 0.973 0.994 0.969 0.821 0.942 0.890 0.942 0.915 0.956 0.834 0.994 0.991 0.994 0.917 0.900 0.997 0.972
4 5 6 5 6 5 6 5 5 4 3 5 5 4 4 3 3 5 6 6 5 6 5 6 3 5 3 3 6 3 4
Fig. 2. Relationship between observed and RIDEV simulated duration to flowering for all cultivars and sowing for which flowering occurred. Selected genotypes represent the genetic diversity in terms of duration of the total set of cultivars.
from sowing to flowering and over-estimated duration to maturity for some genotypes. However, chilling tolerant cultivars had less variation in duration to flowering in later planting dates. About 60% of the simulated crop duration to flowering showed less than 5 days differences from observed and about 40% showed more than 5 days differences (Fig. 2). Differences between observed and simulated duration to flowering increased linearly as a function of mean Tmin from booting to heading for mean Tmin lower than 17 ◦ C (Fig. 3). In all the planting dates, genotypes were exposed to Tmin below 18 ◦ C
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ommendations. Therefore, the core concept of the RIDEV model was adapted to a simplified simulation using spread sheet calculation procedures (Microsoft Excel 2007) to predict phenological stages and spikelet sterility for cropping calendar development. 3.3. Simplified simulation of photothermal effects on phenology
Fig. 3. Relationship between errors in the simulation of flowering (observed minus simulated duration) and the mean, minimum daily air temperature actually experienced by the crop between booting and heading stages, Lumle 2004.
between booting and heading, constituting a major difference to most of the Sahelian planting dates (Dingkuhn et al., 1995). As RIDEV was developed in a Sahelian environment, the application of the model for cropping calendar development in the high hills of Nepal would need further validation, as the duration to flowering and maturity is not sufficiently accurate for calendar rec-
Development rate can be defined as progress to a singular developmental event such as flowering, provided that other factors such as photoperiod sensitivity are absent. If that is the case, and if the observed thermal variation is small enough to remain within the range of linear response of plant development, flowering can be predicted with two genotypic constants Tbase and Tsum . Thereby, Tbase is the temperature at which progress to flowering is zero and Tsum is the accrued number of heat units required for flowering, with the daily heat dose equaling Tmean − Tbase . A linear thermal effect on development is commonly observed if Tmean does not exceed the genotypic optimum temperature Topt . We assume that Topt had only little effect in the present study, since maximum air temperature in the field trials (Fig. 1) rarely exceeded genotypic Topt . Thus, Tbase and Tsum can be estimated from the linear regression of thermal duration to flowering (in terms of accrued ◦ C to the basis of zero) against the accrued number of days (Fig. 4). The intercept of this relationship thereby provides an estimate of Tsum (which is assumed to remain constant despite the variation in absolute time to flowering) and the slope provides an estimate of Tbase . Furthermore, the degree of linearity of the observed correlation across sowing dates permits to evaluate whether for the
Fig. 4. Linear relationships between sum of mean air temperature from sowing to flowering (Tsum ) in dd and duration from sowing to flowering stage in days (d). In the equation shown, the slope (a) represents Tbase (accumulation of heat per day) in ◦ C and the intercept (b) signifies Tsum (dd) of BVP without taking photoperiod into consideration. The number of data points in the graphs corresponds to the number of sowing dates for which flowering occurred.
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Fig. 5. Relationship between observed and estimated days to flowering. (a) Based on experimental derived Tbase and Tsum (see also Fig. 4); (b) based on Tbase reduced by 0.25 ◦ C. (b inset) Differences between observed (f obs) and simulated (f sim) days to flowering from (a) were subjected to a stepwise decrement of 0.1 ◦ C (X) for Tbase , reducing the simulation error to smaller than 10 for the late planting dates at Tbase – 0.25 ◦ C.
environments considered, photoperiodism had a significant effect or not. Tbase and Tsum were estimated by linear regression as described above for all 31 genotypes. Fig. 4 exemplarily shows the results for six contrasting genotypes. Derived Tbase and Tsum were entered into Eqs. (1) and (4) and used to predict flowering as days after sowing with the simplified simulation procedure. The simulation yielded accurate estimations of duration to flowering for the early sowing dates but strongly over-estimated duration to flowering for the later planting dates (Fig. 5a). A detailed analysis of the amount of heat units accumulated in the simulation as compared to the Tsum required for flowering in the later sowing dates showed only minor differences in Tsum . This was due to minor differences in daily accumulation of heat units due to a slightly over estimated Tbase , easily within the range of 4–6 ◦ C reported by Dingkuhn et al. (1995) and most probably due to accuracy of the field based observations. An analysis of the simulation error showed substantial over estimation of days to flowering for the latest planting dates. Stepwise reduction of Tbase showed that a small decrement of 0.25 ◦ C from the derived Tbase was sufficient to predict flowering for almost all cultivars and planting dates (Fig. 5b inset) except for three outliers (Fig. 5b). These corrected thermal parameters Tbase and Tsum (Table 3) were used to simulate days to flowering for all genotypes for different planting dates to develop appropriate cropping calendar option. 3.4. Thermal effects on spikelet sterility High spikelet sterility was observed in late planting dates. Chilling at booting to heading stage (DS 0.85–1.00) is associated with spikelet sterility (Dingkuhn et al., 1995). The critical minimum temperature below which spikelets are sterile at this stage was about 18 ◦ C for all except two cultivars, namely Chomrong and Machhapuchre-3 (Fig. 6). In Lumle, average Tmin between booting and heading was below 18 ◦ C for all planting dates. Spikelet sterility was thus generally high and frequently close to 100%. Chilling tolerant cultivars Chomrong showed 30% spikelet sterility even at mean Tmin of 15 ◦ C between booting and heading (Fig. 6). 4. Discussion Rice–wheat crop rotation system is characterized by more or less pronounced transition period between dry and wet seasons. Management of transition period, by growing seasonal crops,
Fig. 6. Relationship between spikelet sterility and the mean, minimum air temperature actually observed between booting and heading stages, individually determined for each cultivar and date in Lumle, 2004. Except for Chomrong and Machhapuchre-3, other cultivars were broken down into two parts and the relations were linearly regressed taking Khumal-11 as a reference cultivar due to its spikelet sterility variation from less than 30% to 100% within the range of less than 14–18 ◦ C mean Tmin from booting to heading.
leads to nutrient conservation, increased nutrient use efficiency, and increased sustainability of the system (Pande and Becker, 2003). In the mid hills, transition period is not sufficiently long to grow seasonal crops. Growing transition seasonal crops and/or the replacement of the dry season wheat crop by a crop, like potato, can result in a substantial deviation from the recommended planting dates of rice (Shrestha et al., 2007). The potential option may be shifting of rice planting dates to earlier for better yield and manage soil nutrients by incorporating wheat straw for temporary immobilization of soil nitrogen during DWT periods (Becker et al., 2005; Shrestha et al., 2005) in the mid hills. 4.1. Constraints to RIDEV as a tool to adapt rice cropping calendars in the high hills of Nepal Rice phenology depends on the photo-thermal environment and changes in planting date influence the vegetative development and
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Fig. 7. Simulated seasonal options for rice–wheat cropping calendars as limited by chilling induced spikelet sterility (solid lines with vertical bars for standard deviation for 36 years) and crop duration (horizontal bars indicating days from sowing to maturity) for Lumle. Simulation was based on 36 years (1970–2005) with daily increments for minimum and maximum temperatures. PPSD, period of potential sowing dates; WWSS, warm and wet summer season; CDWS, cold and dry winter season. Test varieties were Chomrong and IR64 for spikelet sterility and the calendar was based on Chomrong alone due to its lower percentage of spikelet sterility.
the reproductive development, consequently results different crop durations. The RIDEV model developed at WARDA, Senegal to predict crop duration using PTC of rice genotypes when applied to Lumle conditions either over or under estimated the duration of phenological stages of the local rice genotypes. The RIDEV model requires PTC of the rice genotypes to be grown in the system. Establishing PTC from field experiments can lead to difficulties predicting the exact crop duration with the RIDEV model if the thermal and photoperiodic conditions (micro climate) are not closely monitored and are insufficiently variable among planting dates. Field based studies would either result in wrong genotypic constants, wrong predictions of crop duration, or both (Dingkuhn et al., 1995). This approach requires optimization programs for respective genotypic constants to determine best-fitting models. For this, a rice garden experiment should be conducted over a year with different planting dates. To derive PTC more accurately, many successive dates are required (Craufurd et al., 2003) for better regression output. The RIDEV model assumes that genotypes have genetically fixed PTC. Extrapolation beyond the period of the experiment shows possible errors. Determination of PTC under laboratory conditions is easier than in the field condition as temperature and photoperiod varies over time. However, field based studies enable the characterization of large numbers of genotypes which is not possible in growth chambers (Dingkuhn et al., 1995). Tbase is usually determined by extrapolation. Under the experimental conditions, phenology studies based exclusively on air temperature over estimated Tbase by 4–6 ◦ C. This bias was greatest at mid season when the leaf canopy was developed but culms not yet elongated, and greater in the dry than in the wet season (Dingkuhn et al., 1995). The effective temperature for development in RIDEV is based on the actual temperature at the growing point, which in paddy rice is located in the flood water for development stages up to booting. Thus, for development to flowering, the largest share of time, the water temperature is more important than the air temperature. In Lumle, the relationship between daily air temperature amplitude, ground cover, and water temperature as established for the RIDEV model may not have been valid due to the fact that water temperature in the field trial was in general low (Fig. 7) due to the higher altitude and close proximity to the Himalaya ice belt range and lower energy input owing to the lower angle of the sun. Energy needs to heat up water depend on the specific heat capacity of water (4.19 kJ kg−1 ◦ C). Thus, estimations of flood water temperature need to take into account the minimum water temperature as a starting point, the water
depth and the potential heat absorption at the current latitude. Minimum water temperature in Lumle was about 10 ◦ C less than in Ndiaye in Senegal during entire cropping season. Heat energy required to raise water temperature up to 10 ◦ C requires 43 kJ for 1 m2 area of 1 mm (equivalent to 1 kg) water table without any canopy cover. To increase water temperature up to 10 ◦ C of 5 cm water table for 1 m2 area heat energy required will be 2 MJ, which is further dissipated to the soil and atmosphere. In Lumle, average solar radiation during the rice cropping season is 17 MJ m−2 d−1 . This energy is further dissipated due to reflection, sensible heat and latent heat causing insufficient radiation to increase the water temperature in Lumle. Under the experimental condition, incorrect calculation of daily temperature means biased Tbase by about 1–2 ◦ C (Dingkuhn et al., 1995). Except for cultivar 32Xuan-5C, Tbase derived for Lumle (using RIDEVCAL) was lower than under Sahelian conditions. Including an energy balance taking into account the predominant water temperature, insulation, and the physical properties of water in combination with whether data and groundcover most likely would render RIDEV better suitable to estimate crop duration in higher altitude cropping systems. Photoperiod in Senegal is longer during summer time (June–July) and shorter in winter as compared to Nepal (Fig. 7). Few days in March and September have same hour of photoperiod in both locations. Dingkuhn and Asch (1999) have assumed PPcrit to be 11.5 h and photoperiod sensitivity a linear function of photoperiod above PPcrit. Variation of PPcrit with temperature has been reported (Roberts and Summerfield, 1987). Genotypic variation in PPcrit has not been adequately determined. As for temperature, at least four photoperiod sensitivity levels (insensitive; less, medium and highly sensitive) would determine the response more accurately due to genotypic variation. Photoperiod-sensitivity among traditional cultivars is greatest at lower latitudes, decreasing as latitude increases (Craufurd and Qi, 2001). As in the high hills in Nepal, temperature had a much stronger effect on crop duration than any deviation in photoperiod, PP was ignored when simulating the duration of phenological stages for potential adaptation of cropping calendars in those systems. 4.2. Cropping calendar options In the high hills of Nepal, rice is cultivated in the summer wet season and wheat in the dry season with the residual soil moisture during fall and winter. Both crops receive little irrigation and depend strongly on rainfall, thus, grain yields in theses areas are
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Fig. 8. Minimum water temperature in the paddy field and photoperiod in Ndiaye, Senegal and Lumle Nepal.
low. The common transplanting time of a month old rice seedling starts from 15 June. Chomrong (local adapted genotype, short duration) and IR64 (international check variety, medium duration) were selected to simulate seasonal patterns of crop duration and spikelet sterility for several planting dates in Lumle (Fig. 8). Simulations were performed with Microsoft Excel using 36 years (from 1970 to 2005) of weather data. In the adapted model, PP was not included and critical temperatures for 50% spikelet sterility were set to 18 and 16 ◦ C for IR64 and Chomrong, respectively. Simulations generally resulted in a higher percentage of spikelet sterility due to chilling in IR64 than in Chomrong. For Chomrong, simulation showed a potentially save period of planting dates between mid-April and end of July with cold induced spikelet sterility not exceeding 50% and a total length of crop duration of 135 days when transplanted before 15 July. This would theoretically allow for two different options of introducing a third crop into the calendar: either a 90 day duration legume crop at the beginning of the year between the wheat and the rice or a similar crop of 90 day duration at the end of the growing period between the rice and the wheat. The first option would require a short duration legume such as arrow leaf clover or mucuna as green manure whereas the second option would allow for a cold tolerant grain legume to be grown with the residual moisture. Both options will need to be tested within the system for their effects on nutrient and water availability as well as for their economic viability. In addition, a third option would allow increasing the duration of rice in the system by early transplanting of long duration rice such as Jethoburo. Such a genotype would require a high Tsum , a well developed chilling and drought tolerance during the early vegetative stage as well as some degree of cold tolerance at the early generative stage to avoid yield loses due to spikelet sterility. Such early sown long duration rice would take full advantage of the mineralization peak induced by the early rains (Pande and Becker, 2003) and would decrease erosion losses induced by the long fallow period.
5. Conclusions We have shown that applying a non-modified version of RIDEV for the development of cropping calendar option in the rice–wheat system in the high hills in Nepal was not possible due to an overem-
phasis on photoperiod, an insufficiently sensitive Tbase simulation and an inaccurate simulation of water temperature and, thus, the effective temperature at the meristem. Nonetheless, applying the principles on which RIDEV is based in a simplified simulation allowed to accurately simulate the duration of phenological stages of local rice varieties as well as the level of chilling induced spikelet sterility for variable planting dates in Lumle. Locally adapted cultivars with high chilling tolerance exist (e.g. Chomrong), allowing shifting of planting dates for rice for additional options of including a third crop in the calendar. Future research efforts are needed to evaluate and validate the proposed options for their effects on the cropping system with regard to soil fertility, water availability and economic viability.
Acknowledgements German Academic Exchange Program (DAAD) is gratefully acknowledged by Suchit Shrestha for providing the scholarship to support this research. We thank the Regional Agriculture Research Center (RARS, Lumle), Soil Science Division (SSD, Khumaltar) and the Nepal Agricultural Research Council (NARC, Singhadarbar Plaza) for providing administrative and logistic support.
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