Chief EditorT. Mohapatra
Print ISSN 0367-8245
Online ISSN 0976-058X
NAAS Rating 5.20
Full Research Article
Studies on Morpho-physiological Fingerprints of Rice Cultivars in Rice Crop in Rice-Rice-Rice, Maize-Maize-Rice and Vegetable-Vegetable-Rice Cropping Systems
- Email firstname.lastname@example.org
First Online 23-12-2022|
Methods: Twelve rice cultivars were evaluated in terms of their morpho-physiological in three crop rotation systems, i.e., rice-rice-rice, maize-maize-rice (M-M-R) and vegetable-vegetable-rice, at the Agrotechnology Innovation Center, Universitas Gadjah Mada at Berbah District, Sleman Regency, Special Province of Yogyakarta, Indonesia, from December 2020 to June 2021.
Result: The results revealed an interaction between rice cultivars and the crop rotation system, with the effects observed on the nitrogen, phosphorus and potassium contents in the leaf tissue (NC, PC and KC, respectively), crop growth rate, total dry weight per clump, empty grain per clump (EG) and grain weight per clump (GWC). The increase in NC, PC and KC positively affected the increase in GWC. The GM 8 cultivar in the M-M-R crop rotation system showed lowest EG of 3% and highest GWC of 133.90 g clump-1.
Rice cultivars are one of the essential factors in increasing rice production. Rice cultivars are effective, cheap and quickly adopted by farmers. The role of plant breeders has become vital in creating cultivars that can attain high and stable productivity in all environmental conditions (Alam et al., 2019; Piepho et al., 2016). Universitas Gadjah Mada is currently conducting a multi-location test on ten promising rice lines in 15 locations throughout Indonesia. This test is one of the stages in the release of varieties (Agrotechnology Innovation Center, 2022).
The preliminary study in Sleman, Klaten and Banyumas Regencies, Indonesia showed that GM 2, GM 8 and GM 28 cultivars had high productivities by 6.17, 6.98 and 7.18 tons ha-1, respectively (Aristya et al., 2021). Crop rotation arrangement is crucial for sustainable agriculture. Rotation of different crops can be used as a strategy to overcome the negative impacts of intensive monoculture farming (Grzebisz et al., 2018). Continuous rice planting can cause a decrease in rice yield and soil quality (Meena et al., 2019). Deep et al., (2018) informed that rice-maize crop rotation produced highest rice yield of 10.20 tons ha-1 compared with rice-rice, rice-wheat, rice-pulse and rice-oilseed crop rotation with yields of 7.80, 8.40, 8.50 and 7.40 tons ha-1, respectively.
This study aimed to provide information on the morpho-physiological fingerprints of rice cultivars in different crop rotation systems. This study provides new information related to the effect of the crop rotation system on new rice cultivars.
MATERIALS AND METHODS
The three crop rotation systems were prepared using a randomized complete block design with three blocks as replications. Twelve rice cultivars consisted of ten promising rice lines sourced from Universitas Gadjah Mada, Indonesia and two rice varieties sourced from the Indonesian Center for Rice Research (ICRR), West Java, Indonesia. The ten promising rice lines consisted of V11, GM 28, GM 2, GM 8, Mutant Lampung Kuning, Mutant Rojolele 30 Pendek, Mutant Rojolele 30 Tinggi, Mutant V12T, Mutant Mayangsari and Mutant Lakatesan and the two rice varieties for the control consisted of Inpari 33 and Inpari 30 Ciherang Sub 1. The three crop rotation systems consisted of rice-rice-rice (R-R-R), maize-maize-rice (M-M-R) and vegetable-vegetable-rice (V-V-R) crop rotation.
Rice seeds were sown in a screen house and then transplanted to a paddy field after 21 days. Soil tillage was carried out using a hand tractor before the rice was transplanted. Rice planting distance was 25 cm × 25 cm and one seed was planted per planting hole. Fertilization and other cultivation techniques were applied as recommended by the ICRR (Alam et al., 2021; Faridah et al., 2021).
The observations consisted of morphological and physiological characteristics. The physiological characteristics consisted of chlorophyll a (CA), chlorophyll b (CB), total chlorophyll (TC), nitrate reductase activity (NRA), nitrogen content in the leaf tissue (NC), phosphorus content in the leaf tissue (PC) and potassium content in the leaf tissue (KC). The growth analysis consisted of leaf area index (LAI), net assimilation rate (NAR), crop growth rate (CGR). The physiological characteristics and growth analysis were observed in maximum vegetative phase. The morphological characteristics consisted of total dry weight per clump (TDW), empty grain per clump (EG) and grain weight per clump (GWC) (Gross, 1991; Horwitz and Latimer Jr, 2006; Hunt, 1990; Jones, 1984; Krywult and Bielec, 2013). The morphological characteristics were analysed during harvesting. The observations were made at the General Soil Laboratory and the Crop Production and Management Laboratory, Faculty of Agriculture, Universitas Gadjah Mada, Indonesia.
The data must have normal distribution and homogeneous variance prior to analysis of variance (ANOVA). The data were analyzed by ANOVA (p<0.05), followed by HSD-Tukey test (p<0.05) as a post hoc test (Welham et al., 2015). The relationship between parameters and the similarity of characteristics between rice cultivars were analyzed by correlation and cluster analyses, respectively (Widyawan et al., 2020). The ANOVA was performed using SAS software version 9.4 for Windows with PROC MIXED (SAS Institute Inc, 2013). Correlation and cluster analyses were performed using Rstudio software with the crorrplot, GGally and Pheatmap packages (Raivo, 2019; R Core Team, 2017).
RESULTS AND DISCUSSION
The results of ANOVA for the NC, PC and KC showed an interaction between rice cultivars and the crop rotation systems. The GM 8 cultivar in the M-M-R crop rotation system manifested the highest NC value of 1.93%, but this result was not significantly different from that of the GM 8 cultivar in the V-V-R crop rotation system (1.75%). Inpari 33 cultivar in the R-R-R crop rotation system revealed the lowest NC of 1.16% (Fig 1a).
The GM 8 cultivar in the M-M-R crop rotation system exhibited the highest PC value of 0.26%, but it was not significantly different from those of the Mutant Lampung Kuning and Mutant Rojolele 30 Tinggi cultivars in the M-M-R crop rotation system and GM 8 in the V-V-R crop rotation system, with values of 0.22%, 0.22% and 0.23%, respectively. The lowest PC (0.09%) was observed in Inpari 30 Ciherang Sub 1 in the V-V-R crop rotation system (Fig 1b). The GM 8 cultivar in the M-M-R crop rotation system showed the highest KC at 0.72%. By comparison, the lowest KC was 0.19%, which was observed in Inpari 30 Ciherang Sub 1 in the V-V-R crop rotation system (Fig 1c).
The difference in NC, PC and KC values is due to variations in genetic factors for each rice cultivar and crop rotation system. The continuous R-R-R had low NC, PC and KC values. Thus, the GM 8 cultivar can potentially become a cultivar with more optimal nitrogen/phosphorus/potassium absorption capability than other cultivars. Lu et al., (2018) informed that the short-term (4 years) and long-term (30 years) nitrogen content of the tissue in the rice-rice-fallow crop rotation system is lower than that in the rice-rice-rapeseed crop rotation system.
The ANOVA results showed no interaction between rice cultivars and the crop rotation system in terms of CA, CB, TC and NRA (Table 1). The rice cultivars and crop rotation systems did not show significant differences in terms of CA, CB, TC and NRA. The CA values in rice cultivars ranged from 0.37 g g leaf-1 to 0.42 g g leaf-1 and those of CB ranged from 0.33 g g leaf-1 to 0.46 g g leaf-1. The TC and NRA values in rice cultivars were between 0.62-0.88 g g leaf-1 and 2.23-3.39 µmol NO2- g-1 h-1, respectively. The CA and CB values in the crop rotation system were in the range of 0.38-0.43 and 0.39-0.40 g g leaf-1, respectively. The TC and NRA values were in the range of 0.74-0.79 g g leaf-1 and 2.61-3.25 NO2- g-1 h-1, respectively.
The ANOVA result on LAI and NAR showed no interaction between rice cultivars and crop rotation systems (Table 2). The Mutant Lakatesan cultivar had the highest LAI value of 0.89, which was significantly different from those of cultivars V11, GM 28, GM 2 and GM 8 (0.49, 0.49, 0.50 and 0.53, respectively). The M-M-R crop rotation system was not significantly different from that of V-V-R. However, both were significantly different from the R-R-R crop rotation system. The LAI values of M-M-R and V-V-R were 0.64 and 0.75, respectively. The NAR values for rice cultivars and crop rotation systems did not differ significantly. The NAR values for rice cultivar ranged from 0.37 g cm-2 week-1 to 0.56 g cm-2 week-1 and it was between 0.38-0.42 g cm-2 week-1 in the crop rotation systems.
The ANOVA result on the CGR, TDW, EG and GWC showed an interaction between the rice cultivars and crop rotation systems (Fig 2). The GM 8 cultivar in the M-M-R crop rotation system showed the highest value of 61.73 g g-1 week-1 and the lowest was that of the Inpari 30 Ciherang Sub 1 cultivar in the R-R-R crop rotation system (34.68 g g-1 week-1) (Fig 2a). V11, GM 28, GM 2, GM 8, Mutant Lampung Kuning, Mutant Rojolele 30 Pendek and Mutant Rojolele 30 Tinggi cultivars in the M-M-R and V-V-R crop rotation systems generally showed the highest TDW (Fig 2b).
The GM 28 cultivar in R-R-R crop rotation system showed the highest EG of 30.41% and the lowest was observed on the GM 8 cultivar in the M-M-R crop rotation system (3%) (Fig 2c). The GM 8 cultivar in the M-M-R crop rotation system had the highest GWC of 133.90 g clump-1 and the lowest values were observed in GM 8 and Inpari 30 Ciherang Sub 1 cultivars in the R-R-R crop rotation system (34.49 and 35.40 g clump-1, respectively) (Fig 2d). Cropping system berbasis jagung.
In the study rice-rice rotation showed low mean values for all parameters, resulting in decreased soil fertility and an uninterrupted cycle of pests and plant diseases (Ashworth et al., 2017). Suprihatin et al., (2020) stated that R-R-R crop rotation showed the lowest rice productivity compared with rice-rice-maize and rice-rice-soybean crop rotations. This is due to multi-nutrient deficiencies (Baishya et al., 2017).
Fig 3a shows the results of the correlation analysis. The closer to the red color, higher is the positive correlation. GWC was significantly positively correlated with TDW, NC, PC and KC, with correlation values of 0.9**, 0.8**, 0.7** and 0.7**, respectively. The cluster analysis provided information on the proximity of rice cultivar characteristics in the various crop rotation systems (Fig 3b). Three clusters formed and each group had similar characteristics. The first cluster consisted of Mutant Lakatesan, Inpari 33 and Inpari 30 Ciherang Sub 1 cultivars. The second cluster included GM 2, Mutant Lampung Kuning, Mutant Rojolele 30 Pendek and GM 8 cultivars and the third cluster comprised V12T, Mutant Rojolele 30 Tinggi, V11, GM 28 and Mutant Mayangsari cultivars.
The relatively long harvesting age (127 days after planting) resulted in a higher EG in the GM 28 cultivar compared with other cultivars. This finding can lead to high yield losses due to global climate changes, which cause El-Nino and La-Nina (FAO, 2015). Aristya et al., (2021b) provided information that GM 2, GM 8 and GM 28 cultivars are relatively resistant to brown planthopper (Nilaparvata lugens), leaf blast (Pyricularia oryzae) and bacterial leaf blight (Xanthomonas oryzae PV. oryzae). (Neupan et al., 2021) stated that continuous maize cultivation resulted in higher bacterial and fungal contents in the soil than continuously planted rice and beans.
CONFLICT OF INTEREST
- Agrotechnology Innovation Center. (2022). Gamagora ‘Amphibious’ Rice Multilocation Test- UGM Researchers Are Ready to Release Superior Varieties. https://piat.ugm.ac.id/2022/03/ 20/uji-multilokasi-padi-amphibi-gamagora-peneliti-ugm- siap-melepas-varietas-unggul/.
- Alam, T., Kurniasih, B., Suryanto, P., Basunanda, P., Supriyanta, A.E., Widyawan, M.H., Handayani, S., Taryono. (2019). Stability analysis for soybean in agroforestry system with kayu putih. SABRAO Journal of Breeding and Genetics. 51: 405-418.
- Alam, T., Suryanto, P., Supriyanta, Basunanda, P., Wulandari, R.A., Kastono, D., Widyawan, M.H., Nurmansyah, Taryono. (2021). Rice cultivar selection in an agroforestry system through GGE-biplot and EBLUP. Biodiversitas. 22: 4750- 4757. DOI: 10.13057/biodiv/d221106.
- Aristya, V.E., Trisyono, Y.A., Mulyo, J.H., Taryono. (2021). Participatory varietal selection for promising rice lines. Sustainability. 13: 1-18. DOI: 10.3390/su13126856.
- Ashworth, A.J., Allen, F.L., Saxton, A.M., Tyler, D.D. (2017). Impact of crop rotations and soil amendments on long-term no- tilled soybean yield. Agronomy Journal. 109: 938-946. DOI: 10.2134/agronj2016.04.0224.
- Baishya, A., Gogoi, B., Bora, A.S., Hazarika, J., Borah, M., Das, A.P., Sutradhar, P. (2017). Soil fertility and on-farm crop response to NPK and Zn fertilization in rice-rice cropping sequence of Lower Brahmaputra Valley Zone of Assam. Agricultural Science Digest. 37: 87-93. DOI: 10.18805/ asd.v37i2.7980.
- Deep, M., Kumar, R.M., Saha, S., Singh, A. (2018). Rice-based cropping systems for enhancing productivity of food grains in India; decadal experience of AICRP. Indian Farming. 68: 27-30.
- Faridah, E., Suryanto, P., Nurjanto, H.H., Putra, E.T.S., Falah, M.D., Widyawan, M.H., Alam, T. (2021). Optimizing application of biochar amendment for nitrogen use efficiency in upland rice under Melaleuca cajuputi stands. Indian Journal of Agricultural Research. 55: 105-109. DOI: 10.18805/IJARe. A-601.
- FAO. (2015). Climate change and food. https://www.fao.org/3/i518 8e/I5188E.pdf.
- FAO. (2019). World food and agriculture statistical pocketbook 2019. http://www.fao.org/3/ca6463en/ca6463en.pdf.
- Gross, J. (1991). Pigmentin vegetable, chlorophyll and caretinoids. Van Nonstrand Reinhold, New York.
- Grzebisz, W., £ukowiak, R., Sassenrath, G.F. (2018). Virtual nitrogen as a tool for assessment of nitrogen management at the field scale; a crop rotation approach. Field Crops Research. 218: 182-194. DOI: 10.1016/j.fcr.2018.01.009.
- Horwitz, W., Latimer Jr, G.W. (2006). Official methods of analysis of AOAC International, AOAC International, Maryland.
- Hunt, R. (1990). Basic growth analysis. Springer, Dordrecht.
- Jones. (1984). Laboratory guide of exercises in conducting soil test and plant analysis. Benton Laboratories Inc., Athens.
- Krywult, M., Bielec, D. (2013). Method of measurement of nitrate reductase activity in field conditions. Journal of Ecological Engineering. 14: 7-11. DOI: 10.5604/2081139X.1031524.
- Lu, S., Lepo, J.E., Song, H.X., Guan, C.Y., Zhang, ZH. (2018). Increased rice yield in long-term crop rotation regimes through improved soil structure, rhizosphere microbial communities and nutrient bioavailability in paddy soil. Biology and Fertility of Soils. 54: 909-923. DOI: 10.1007/ s00374-018-1315-4.
- Meena, R.K., Singh, Y.V., Meena, V.K., Kumar, R., Ram, H. (2019). Growth, productivity and profitability of wheat (Triticum aestivum L.) under rice-wheat cropping system as influenced by of in-situ microbial inoculated rice residue and nitrogen management. Bhartiya Krishi Anusandhan Patrika. 34: 170-177. DOI: 10.18805/BKAP194.
- Mulyani, A., Nursyamsi, D., Syakir, M. (2017). Land resource utilization strategy to achieve sustainable rice self-sufficiency. Journal Sumberdaya Lahan. 11: 11-22.
- Neupane, A., Bulbul, I., Wang, Z., Lehman, R.M., Nafziger, E., Marzano, S.L. (2021). Long term crop rotation effect on subsequent soybean yield explained by soil and root-associated microbes and soil health indicators. Scientific Reports. 11: 9200. DOI: 10.1038/s41598-021-88784-6.
- Piepho, H-P., Nazir, M.F., Qamar, M., Rattu, A., Riaz-ud-Din, Hussain, M., Ahmad, G., Fazal-e-Subhan, Ahmad, J., Abdullah, Laghari, K.B., Vistro, I.A., Kakar, M.S., Sial, M.A., Imtiaz, M. (2016). Stability analysis for a countrywide series of wheat trials in Pakistan. Crop Science. 56: 2465-2475. DOI: 10.2135/cropsci2015.12.0743.
- R Core Team. (2017). R- A language and environment for statistical computing. https://www.R-project.org/.
- Raivo, K. (2019). Pheatmap- Pretty heatmaps. https://cran.r-project. org/web/packages/pheatmap/index.html.
- SAS Institute Inc. (2013). SAS system for windows 9.4. SAS Institute Inc., North Caroline.
- Statistics Indonesia. (2022). Rice production in 2021 decreases by 0.43 percent (fixed number). https://www.bps.go.id/pressre lease/2022/03/01/1909/produksi-padi-tahun-2021-turun- 0-43-persen-angka-tetap-.html.
- Suprihatin, A., Purwanto, B.H., Hanudin, E., Nurudin, M. (2020). Effect of cropping rotation patterns on rice productivity in irrigated rice fields. IOP Conference Series: Earth and Environmental Science. 752: 012002.
- Suryanto, P., Taryono, Supriyanta, Kastono, D., Putra, E.T.S., Widyawan, M.H., Alam, T. (2020). Assessment of soil quality parameters and yield of rice cultivars in Melaleuca cajuputi agroforestry system. Biodiversitas. 21: 3463-3470. DOI: 10.13057/ biodiv/d210807.
- Welham, S.J., Gezan, S.A., Clark, S.J., Mead, A. (2015). Statistical methods in biology- Design and analysis of experiments and regression. CEC Press, Boca Raton.
- Widyawan, M.H., Hasanah, A., Taryono, Alam, T., Sayekti, R.R.R.S., Pramana, A.A.C., Wulandari, R.A. (2020). Multivariate analysis unravels genetic diversity and relationship between agronomic traits, protein and dietary fiber in yardlong bean (Vigna unguiculata subsp. sesquipedalis Verdc.). Biodiversitas. 21: 5662-5671. DOI: 10.13057/biodiv/d211211.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.