# Comparing the Grain Yields of Direct-Seeded and Transplanted Rice: A Meta-Analysis

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. Data Collection

^{2}. For each study, all comparisons between DSR and TPR on yield were separately included in our meta-analysis. Multi-factorial studies (i.e., studies in which DSR and TPR treatments were combined with other treatments in a factorial design) and studies that reported results for multiple years contribute more than one comparison to our dataset. To evaluate how the effect size varies with management and environmental factors, we recorded and categorized the following moderating variables: (1) dry seeding or wet seeding, i.e., if the crop establishment was completed by sowing dry seeds into dry soil or pre-germinated seeds into wet puddled soil (there was no available water-seeded experiment to include in the meta-analysis); (2) dibble seeding, row seeding, or manual broadcast; (3) intensive weed control, i.e., when weeds were intensively controlled to avoid any yield loss, moderate weed control, i.e., when experimental plots implemented a weed control practice, but weed infestation still remained, or no weed control, i.e., when no control practice was implemented; (4) zero, reduced, or conventional tillage; (5) no water stress, i.e., when the soil moisture of the plots maintained a continuous level of flooding or remained above the field holding capacity, mild water stress, i.e., when the soil water potential was not allowed to drop below −20 kPa, or severe water stress, i.e., when the soil water potential was allowed to drop below −20 kPa; (6) low, moderate or high nitrogen input, i.e., when field nitrogen input was <60, 60–120, or >120 kg ha

^{−1}, respectively; (7) soil pH < 7 or ≥7; (8) soil organic carbon content (SOC) ≤1% or >1%; (9) soil texture divided into clay or non-clay. The seeding time of TPR and DSR is usually different, which may lead to unbalanced climatic stress and cause different yield losses between TPR and DSR. Therefore, we categorized (10) the presence or absence of climatic stress induced by improper seeding time, i.e., if DSR and TPR sowed rice seeds on the same day, they experienced the same climatic condition, and then these observations were classified as “climatic stress absented”. In contrast, if the papers reported unbalanced climatic stress occurring due to different seeding times, the observations were classified as “climatic stress occurred”. Transplanting time was not considered here. The observations of “climatic stress occurred” were excluded from the database, and the rest was divided into three groups based on TP yield (i.e., below 6 t ha

^{−1}, from 6 to 8 t ha

^{−1}, or above 8 t ha

^{−1}) to investigate how DSR yield performance varies at different yielding and management levels. Yielding level is considered to be dependent on the knowledge- and input-intensiveness of management practices.

#### 2.2. Data Analysis

_{DSR}is the mean yield of DSR, and X

_{TPR}is the mean yield of TPR. The variance of the ln R for study (i) (V

_{i}) was approximated using the following formula:

_{1}and SD

_{2}are the standard deviation for the TPR and DSR treatment in study (i), respectively; n

_{1}and n

_{2}are the sample sizes for the TPR and DSR treatments, respectively. Next, a weight was assigned to each study under the inverse scheme (Equation (3)), as follows:

_{i}is the weight assigned to study (i), V

_{i}is the within-study variance for study (i), and T

^{2}is the between-study variance that is common to all studies. The T

^{2}estimation is made using the DerSimonian and Laird method [17]. Given that some studies fail to provide standard deviation (SD) for their outcomes, we imputed the SD for each study by calculating the pooled SD from all other studies in this meta-analysis that provided the SD in their results, according to the established method [18]. The pooled SD was determined by using the formula below:

_{j}and n

_{j}are the standard deviation and replication number of study (j), respectively. This practice may result in a biased point estimate of the treatment effect if the studies with missing SD information are not a random subset of all the available studies. Therefore, we examined the validity of this imputation practice using the method of Furukawa [18]. First, the studies that reported SD information were selected, and we calculated the actual effect size of each individual study in the random model. Secondly, 27 studies that missed SD were substituted with the imputed SD to estimate the hypothetical effect size of each individual study. Then, the concordance between individual effect sizes was examined by using the analysis of variance (ANOVA) intraclass correlation coefficient. The determination coefficient turned out to be 0.87 (Figure S3), implying that this imputation was less likely to change the overall effect size. Thus, the imputed SD was used in the subsequent analysis. The weighted summary effect size was then computed as follows:

_{i}and W

_{i}are the response ratio and the weight for study (i), respectively. Finally, the variance of the summary effect was estimated as the reciprocal of the sum of the weights, and the 95% confidence interval (CI) for the effect was calculated as follows:

_{M}and UL

_{M}are the 95% lower and upper limits for the summary effect, respectively, and M and V

_{M}are the summary effect and its variance, respectively. For ease of interpretation, the response effects were expressed as the percentage yield change of DSR relative to TPR using the equation A = $\left({e}^{\mathrm{lnM}}-1\right)\times 100\%$, which we also refer to in the text as “DSR relative yield”.

_{Total}) was divided into within-group (Q

_{W}) and between-group (Q

_{B}) heterogeneity. Under the null hypothesis that the effect size is the same for all groups, 1 to p, Q

_{B}would be distributed as chi-squared with a degree of freedom equal to p-1. Q

_{B}rather than Q

_{W}is of considerable scientific interest [19]. The significance of Q

_{B}was tested by comparing it against the critical value of the χ2 distribution. A significant Q

_{B}denotes that the cumulative effect size is not the same for different groups. Differences were considered to be statistically significant when p < 0.05.

#### 2.3. Publication Bias

## 3. Results

^{2}showed a positive and significant response to direct rice seeding, whereas the spikelet number per panicle was significantly reduced. There was no significant difference in the spikelet number per m

^{2}and the grain filling percentage between DSR and TPR. Grain weight (the weight of one kernel seed) exhibited a slightly negative but significant response to direct seeding (Figure 1).

## 4. Discussion

^{2}and spikelet number per panicle, here the increase in panicle number m

^{−2}was not large enough to compensate for the decrease in spikelet number per panicle in DSR, and grain weight under DSR was slightly but significantly lower than that under TPR. This might be attributed to heavy shading before the heading reduced the hull size in DSR due to its higher plant density than TPR [25].

^{−1}to about 25 kg ha

^{−1}, thereby helping to overcome spikelet sterility and lodging due to high plant density [9]. The development of precise seeding technology is able to guarantee desired DSR yield performance, especially on a farm scale [40].

## Supplementary Materials

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Khush, G.S. Strategies for increasing the yield potential of cereals: Case of rice as an example. Plant Breed.
**2013**, 132, 433–436. [Google Scholar] [CrossRef] - Avnery, S.; Mauzerall, D.L.; Fiore, A.M. Increasing global agricultural production by reducing ozone damages via methane emission controls and ozone-resistant cultivar selection. Glob. Chang. Biol.
**2013**, 19, 1285–1299. [Google Scholar] [CrossRef] [PubMed] - Peng, S.; Tang, Q.; Zou, Y. Current status and challenges of rice production in China. Plant Prod. Sci.
**2009**, 12, 3–8. [Google Scholar] [CrossRef] - Chakraborty, D.; Ladha, J.K.; Rana, D.S.; Jat, M.L.; Gathala, M.K.; Yadav, S.; Rao, A.N.; Ramesha, M.S.; Raman, A. A global analysis of alternative tillage and crop establishment practices for economically and environmentally efficient rice production. Sci. Rep.
**2017**, 7, 9342. [Google Scholar] [CrossRef] [PubMed] - Tuong, T.P.; Castillo, E.G.; Cabangon, R.C.; Boling, A.; Singh, U. The drought response of lowland rice to crop establishment practices and N-fertilizer sources. Field Crops Res.
**2002**, 74, 243–257. [Google Scholar] [CrossRef] - Yuan, S.; Nie, L.; Wang, F.; Huang, J.; Peng, S. Agronomic performance of inbred and hybrid rice cultivars under simplified and reduced-input practices. Field Crops Res.
**2017**, 210, 129–135. [Google Scholar] [CrossRef] - Pandey, S.; Velasco, L.; Toriyama, K.; Heong, K.L.; Hardy, B. Trends in crop establishment methods in Asia and research issues. In Rice is Life: Scientific Perspectives for the 21st Century; International Rice Research Institute: Los Baños, Philippines, 2005; pp. 178–181. [Google Scholar]
- Tuong, T.; Bouman, B. Rice production in water scarce environments. In Water Productivity in Agriculture: Limits and Opportunities for Improvement; Kijne, J., Barker, R., Molden, D., Eds.; CABI Publishing: Wallingford, UK, 2003; pp. 53–67. [Google Scholar]
- Kumar, V.; Ladha, J.K. Direct Seeding of Rice: Recent Developments and Future Research Needs. Adv. Agron.
**2011**, 111, 297–413. [Google Scholar] - Sun, L.; Hussain, S.; Liu, H.; Peng, S.; Huang, J.; Cui, K.; Nie, L. Implications of low sowing rate for hybrid rice varieties under dry direct-seeded rice system in central China. Field Crops Res.
**2015**, 175, 87–95. [Google Scholar] [CrossRef] - Farooq, M.; Siddique, K.H.M.; Rehman, H.; Aziz, T.; Lee, D.J.; Wahid, A. Rice direct seeding: Experiences, challenges and opportunities. Soil Tillage Res.
**2011**, 111, 87–98. [Google Scholar] [CrossRef] - Pandey, S.; Velasco, L. Economics of Direct Seeding in Asia: Patterns of Adoption and Research Priorities; International Rice Research Institute: Los Banos, Philippines, 2002; pp. 6–7. [Google Scholar]
- Peng, S. Reflection on China’s rice production strategies during the transition period. Sci. Sin. Vitae
**2014**, 44, 845–850. [Google Scholar] [CrossRef] - Bhushan, L.; Ladha, J.K.; Gupta, R.K.; Singh, S.; Tirolpadre, A.; Saharawat, Y.S.; Gathalaa, M.; Pathaka, H. Saving of water and labor in a rice–wheat system with no-tillage and direct seeding technologies. Agron. J.
**2007**, 99, 1288–1296. [Google Scholar] [CrossRef] - Liu, H.; Hussain, S.; Zheng, M.; Peng, S.; Huang, J.; Cui, K.; Nie, L. Dry direct-seeded rice as an alternative to transplanted-flooded rice in central China. Agron. Sustain. Dev.
**2015**, 35, 285–294. [Google Scholar] [CrossRef] - Chen, S.; Ge, Q.; Chu, G.; Xu, C.; Yan, J.; Zhang, X.; Wang, D. Seasonal differences in the rice grain yield and nitrogen use efficiency response to seedling establishment methods in the middle and lower reaches of the Yangtze River in China. Field Crops Res.
**2017**, 205, 1–13. [Google Scholar] [CrossRef] - DerSimonian, R.; Laird, N. Meta-analysis in clinical trials. Control. Clin. Trials.
**1986**, 7, 177. [Google Scholar] [CrossRef] - Furukawa, T.A.; Barbui, C.; Cipriani, A.; Brambilla, P.; Watanabe, N. Imputing missing standard deviations in meta-analyses can provide accurate results. J. Clin. Epidemiol.
**2006**, 59, 7–10. [Google Scholar] [CrossRef] [PubMed] - Gurevitch, J.; Hedges, L.V. Statistical Issues in Ecological Meta-Analyses. Ecology
**1999**, 80, 1142–1149. [Google Scholar] [CrossRef] - Kallenbach, C.; Grandy, A.S. Controls over soil microbial biomass responses to carbon amendments in agricultural systems: A meta-analysis. Agric. Ecosyst. Environ.
**2011**, 144, 241–252. [Google Scholar] [CrossRef] - Kline, R.B. Data preparation and screening. In Principles and Practice of Structural Equation Modeling; The Guilford Press: New York, NY, USA, 1998; pp. 67–94. [Google Scholar]
- Rosenthal, R. The file drawer problem and tolerance for null results. Psychol. Bull.
**1979**, 86, 638–641. [Google Scholar] [CrossRef] - Duval, S.; Tweedie, R. Trim and fill: A simple funnel-plot–based method of testing and adjusting for publication bias in meta-analysis. Biometrics
**2000**, 56, 455. [Google Scholar] [CrossRef] - Xiong, D.; Ling, X.; Huang, J.; Peng, S. Meta-analysis and dose-response analysis of high temperature effects on rice yield and quality. Environ. Exp. Bot.
**2017**, 141, 1–9. [Google Scholar] [CrossRef] - Yoshida, S. Fundamentals of Rice Crop Science; International Rice Research Institute: Los Baños, Philippines, 1981; pp. 238–239. [Google Scholar]
- Singh, V.P.; Dhyani, V.C.; Singh, S.P.; Kumar, A.; Manalil, S.; Chauhan, B.S. Effect of herbicides on weed management in dry-seeded rice sown under different tillage systems. Crop Prot.
**2016**, 80, 118–126. [Google Scholar] [CrossRef] - Rao, A.N.; Johnson, D.E.; Sivaprasad, B.; Ladha, J.K.; Mortimer, A.M. Weed management in direct-seeded rice. Adv. Agron.
**2007**, 93, 153–255. [Google Scholar] - Wu, H.; Pratley, J.; Lemerle, D.; Haig, T. Crop cultivars with allelopathic capability. Weed Res.
**1999**, 39, 171–180. [Google Scholar] [CrossRef] - Narwal, S.S. Weed management in rice: Wheat rotation by allelopathy. Crit. Rev. Plant Sci.
**2000**, 19, 249–266. [Google Scholar] [CrossRef] - Sudianto, E.; Beng-Kah, S.; Ting-Xiang, N.; Saldain, N.E.; Scott, R.C.; Burgos, N.R. Clearfield rice: Its development, success, and key challenges on a global perspective. Crop Prot.
**2013**, 49, 40–51. [Google Scholar] [CrossRef] - Teosdale, J.R.; Beste, C.E.; Potts, W.E. Response of weeds to tillage and cover crop residues. Weed Sci.
**1991**, 39, 195–199. [Google Scholar] [CrossRef] - Zimdahl, R.L. Fundamentals of Weed Science; Academic Press Inc.: San Diego, CA, USA, 1999. [Google Scholar]
- Sudhir-Yadav Gill, G.; Humphreys, E.; Kukal, S.S.; Walia, U.S. Effect of water management on dry seeded and puddled transplanted rice. Part 1: Crop performance. Field Crops Res.
**2011**, 120, 112–122. [Google Scholar] [CrossRef] - Timsina, J.; Singh, U.; Badaruddin, M.; Meisner, C.; Amin, M.R. Cultivar, nitrogen, and water effects on productivity, and nitrogen-use efficiency and balance for rice-wheat sequences of Bangladesh. Field Crops Res.
**2001**, 72, 143–161. [Google Scholar] [CrossRef] - Ahmed, S.; Humphreys, E.; Salim, M.; Chauhan, B.S. Optimizing sowing management for short duration dry seeded aman rice on the High Ganges River Floodplain of Bangladesh. Field Crops Res.
**2014**, 169, 77–88. [Google Scholar] [CrossRef] - Villegas, A.N.; Zandstra, H.G. Identification of soil moisture requirement for germination and emergence of dry seeded rice. Philipp. J. Crop Sci.
**1979**, 4, 142–145. [Google Scholar] - Wmw, W.; Mmp, M.; Bandara, C.; Rao, A.N.; Bhandari, D.C.; Ladha, J.K. Direct-seeded rice culture in Sri Lanka: Lessons from farmers. Field Crops Res.
**2011**, 121, 53–63. [Google Scholar] - Zheng, M.; Tao, Y.; Hussain, S.; Jiang, Q.; Peng, S.; Huang, J.; Cui, K.; Nie, L. Seed priming in dry direct-seeded rice: Consequences for emergence, seedling growth and associated metabolic events under drought stress. Plant Growth Regul.
**2016**, 78, 167–178. [Google Scholar] [CrossRef] - Yadav, D.B.; Yadav, A.; Malik, R.K.; Gurjeet, G. Efficacy of PIH 2023, penoxsulam and azimsulfuron for post-emergence weed control in wet direct seeded rice. In Proceedings of the Biennial Conference, Indian Society of Weed Science, Hisar, India, 2–3 November 2007; Department of Agronomy, CCS Haryana Agricultural University: Hisar, India, 2007. [Google Scholar]
- Gopal, R.; Jat, R.K.; Malik, R.K.; Kumar, V.; Alam, M.M.; Jat, M.L.; Mazid, M.A.; Saharawat, Y.S.; McDonald, A.; Gupta, R. Direct Dry Seeded Rice Production Technology and Weed Management in Rice Based Systems; Technical Bulletin; International Maize and Wheat Improvement Center: New Delhi, India, 2010; p. 28. [Google Scholar]
- Miro, B.; Ismail, A.M. Tolerance of anaerobic conditions caused by flooding during germination and early growth in rice (Oryza sativa L.). Front. Plant Sci.
**2013**, 4, 269. [Google Scholar] [CrossRef] [PubMed] - Xu, L.; Zhan, X.; Yu, T.; Nie, L.; Huang, J.; Cui, K.; Wang, F.; Li, Y.; Peng, S. Yield performance of direct-seeded, double-season rice using varieties with short growth durations in central China. Field Crops Res.
**2018**, 227, 49–55. [Google Scholar] [CrossRef] - Schnier, H.F.; Dingkuhn, M.; De Datta, S.K.; Mengel, K.; Faronilo, J.E. Nitrogen fertilization of direct-seeded flooded vs. transplanted rice: I. nitrogen uptake, photosynthesis, growth, and yield. Crop Sci.
**1990**, 30, 1276–1284. [Google Scholar] [CrossRef] - Murphy, B. Effects of Soil Organic Matter on Functional Soil Properties—Review of the Literature and Underlying Data; Department of the Environment: Canberra, Australia, 2014.
- Yao, S.H.; Zhang, B.; Hu, F. Soil biophysical controls over rice straw decomposition and sequestration in soil: The effects of drying intensity and frequency of drying and wetting cycles. Soil Biol. Biochem.
**2011**, 43, 590–599. [Google Scholar] [CrossRef] - Suriyagoda, L.; Costa, W.A.J.M.D.; Lambers, H. Growth and phosphorus nutrition of rice when inorganic fertiliser application is partly replaced by straw under varying moisture availability in sandy and clay soils. Plant Soil
**2014**, 384, 53–68. [Google Scholar] [CrossRef] - Saleque, M.A.; Kirk, G.J.D. Root-induced solubilization of phosphate in the rhizosphere of lowland rice. New Phytol.
**1995**, 129, 325–336. [Google Scholar] [CrossRef] - Peng, S.; Cassman, K.G.; Virmani, S.S.; Sheehy, J.; Khush, G.S. Yield potential trends of tropical rice since the release of ir8 and the challenge of increasing rice yield potential. Crop Sci.
**1999**, 39, 1552–1559. [Google Scholar] [CrossRef][Green Version] - Zhang, Y.; Tang, Q.; Zou, Y.; Li, D.; Qin, J.; Yang, S.; Chen, L.; Xia, B.; Peng, S. Yield potential and radiation use efficiency of “super” hybrid rice grown under subtropical conditions. Field Crops Res.
**2009**, 114, 91–98. [Google Scholar] [CrossRef] - FAOSTAT. FAO Statistical Databases; Food and Agriculture Organization (FAO) of the United Nations: Rome, Italy, 2019; Available online: www.fao.org/faostat/en/#data (accessed on 24 October 2019).

**Figure 1.**Percentage changes in yield and yield components comparing direct-seeded rice to transplanted rice across a wide range of management and environmental conditions. The number of paired observations/number of studies included in each dataset are presented in parenthesis.

**Figure 2.**Influence of different crop establishment, seeding methods, and tillage practices on the yield change of direct-seeded rice relative to transplanted rice. The number of paired observations/number of studies included in each dataset are presented in parenthesis. Significant differences by categories are based on randomization tests; ns denotes non-significance at 0.05, * denotes significant at p ≤ 0.05, ** denotes significant at p ≤ 0.01, and *** denotes significant at p ≤ 0.001.

**Figure 3.**Influence of different weed infestation, water regimes, and nitrogen input on the yield change of direct-seeded rice relative to transplanted rice. The number of paired observations/number of studies included in each dataset are presented in parenthesis. Significant differences by categories are based on randomization tests. ns denotes non-significance at 0.05, * denotes significant at p ≤ 0.05, ** denotes significant at p ≤ 0.01, and *** denotes significant at p ≤ 0.001.

**Figure 4.**Influence of different soil organic carbon content (SOC), soil texture, soil pH, and climatic stress occurrence on the yield change of direct-seeded rice relative to transplanted rice. The number of paired observations/number of studies included in each dataset is presented in parenthesis. Significant differences by categories are based on randomization tests. ns denotes non-significance at 0.05, * denotes significant at p ≤ 0.05, ** denotes significant at p ≤ 0.01, and *** denotes significant at p ≤ 0.001.

**Figure 5.**Relative variable importance ranking for the yield impacts of direct-seeded rice relative to transplanted rice. Significant differences by categories are based on randomization tests. ns denotes non-significance at 0.05, * denotes significant at p ≤ 0.05, ** denotes significant at p ≤ 0.01, and *** denotes significant at p ≤ 0.001.

**Figure 6.**Percentage changes in direct-seeded rice yield relative to transplanted rice for different yielding levels. The number of paired observations/number of studies included in each dataset are presented in parenthesis. Significant differences by categories are based on randomization tests.

Region | Observation Number | Proportion (%) |
---|---|---|

India | 167 | 38 |

China | 111 | 25 |

Philippines | 79 | 18 |

Pakistan | 22 | 5 |

Cambodia | 12 | 3 |

Iran | 12 | 3 |

Sri Lanka | 12 | 3 |

Korea | 8 | 2 |

Bangladesh | 4 | 1 |

Nepal | 4 | 1 |

Thailand | 3 | 1 |

Ivory Coast | 3 | 1 |

Malaysia | 2 | <1 |

Japan | 1 | <1 |

Total | 440 | 100 |

**Table 2.**Heterogeneity (Q

_{B}) and p value for the direct seeding effect size on grain yield across different categorical variables.

Variable | Q_{B} | df | p Value |
---|---|---|---|

Crop establishment | 5.85 | 1 | 0.016 |

Seeding method | 24.73 | 2 | 0.000 |

Tillage method | 1.11 | 2 | 0.573 |

Weed control | 221.41 | 2 | 0.000 |

Water management | 11.50 | 2 | 0.003 |

Nitrogen input | 4.60 | 2 | 0.100 |

Soil organic carbon | 5.07 | 1 | 0.024 |

Soil texture | 18.88 | 1 | 0.000 |

Soil pH | 8.80 | 1 | 0.003 |

Climatic stress | 63.82 | 1 | 0.000 |

Yielding level | 27.01 | 2 | 0.000 |

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## Share and Cite

**MDPI and ACS Style**

Xu, L.; Li, X.; Wang, X.; Xiong, D.; Wang, F.
Comparing the Grain Yields of Direct-Seeded and Transplanted Rice: A Meta-Analysis. *Agronomy* **2019**, *9*, 767.
https://doi.org/10.3390/agronomy9110767

**AMA Style**

Xu L, Li X, Wang X, Xiong D, Wang F.
Comparing the Grain Yields of Direct-Seeded and Transplanted Rice: A Meta-Analysis. *Agronomy*. 2019; 9(11):767.
https://doi.org/10.3390/agronomy9110767

**Chicago/Turabian Style**

Xu, Le, Xiaoxiao Li, Xinyu Wang, Dongliang Xiong, and Fei Wang.
2019. "Comparing the Grain Yields of Direct-Seeded and Transplanted Rice: A Meta-Analysis" *Agronomy* 9, no. 11: 767.
https://doi.org/10.3390/agronomy9110767