Expounding the Effect of Harvest Management on Rice (Oryza sativa L.) Yield and Latent Loss Based on the Accurate Measurement of Grain Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Plot Location and Test Materials
2.2. Data Processing
2.2.1. Determination of the Standard Moisture Weight
2.2.2. Determination of the Sample’s Optimal Harvest Days
2.2.3. Latent Dry Matter Loss and the Loss Rate
2.3. Experimental Design
2.3.1. The Method Used to Determine the Thousand-Grain Weight of Rice
2.3.2. Unit Area Determination Method
2.3.3. One-Hundred-Spike Weight Determination Method
2.3.4. Fixed Area Tracking Method
2.3.5. Shattering Weight Determination Method
3. Results
3.1. Determination Results of 1000-Grain Weight
3.1.1. Experiments in 2019
3.1.2. Experiments in 2020
3.2. Determination Results of Unit Area Method
3.2.1. Experiments in 2020
3.2.2. Experiments in 2021
3.3. Determination of the Results of the 100-Spike Weight Method
3.4. Determination of the Results of the Fixed Area Tracking Method
3.5. Determination of the Results of the Shattering Weight Method
4. Discussion
4.1. The Latent Loss of Rice Exists
4.2. Latent Loss and Optimal Harvest Days
4.3. Value of Latent Loss Management
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Arvanitoyannis, I.S.; Tserkezou, P. Cereal Waste Management: Treatment Methods and Potential Uses of Treated Waste. Environmental Science, Agricultural and Food Sciences. In Waste Management for the Food Industries; Academic Press: Cambridge, MA, USA, 2008; pp. 629–702. [Google Scholar]
- Maclean, J.L.; Dawe, D.; Hardy, B.; Hettel, G. Rice Almanac: Source Book for the Most Important Economic Activity on Earth; CABI Publishing: Wallingford, UK, 2002; Volume 3. [Google Scholar]
- Srivastava, Y. Climate change: A challenge for postharvest management, food loss, food quality, and food security. In Climate Change and Agricultural Ecosystems: Current Challenges and Adaptation; Elsevier: Amsterdam, The Netherlands, 2019; pp. 355–377. [Google Scholar]
- Calzadilla, A.; Rehdanz, K.; Betts, R.; Falloon, P.; Wiltshire, A.; Tol, R.S.J. Climate change impacts on global agriculture. Clim. Chang. 2013, 120, 357–374. [Google Scholar] [CrossRef]
- Zhu, P.; Burney, J.; Chang, J.F.; Jin, Z.N.; Mueller, N.D.; Xin, Q.C.; Xu, J.L.; Yu, L.; Makowski, D.; Ciais, P. Warming reduces global agricultural production by decreasing cropping frequency and yields. Nat. Clim. Chang. 2022, 12, 1016–1023. [Google Scholar] [CrossRef]
- Gummert, M.; Nguyen, V.H.; Singleton, G.; Cabardo, C.; Thant, A.; Htwe, N.M.; Labios, R.; Quilloy, R. Assessment of post-harvest losses and carbon footprint in intensive lowland rice production in Myanmar. Sci. Rep. 2020, 10, 19797. [Google Scholar] [CrossRef] [PubMed]
- Laborde, D.; Martin, W.; Swinnen, J.; Vos, R. COVID-19 risks to global food security. Science 2020, 369, 500–502. [Google Scholar] [CrossRef] [PubMed]
- Olabisi, M.; Tschirley, D.L.; Nyange, D.; Awokuse, T. Does trade protectionism promote domestic food security? Evidence from Tanzanian edible oil imports? Glob. Food Secur. Agric. Policy Econ. Environ. 2021, 28, 100470. [Google Scholar] [CrossRef]
- Cui, Q.; Wang, X.; Zhong, Y.; Pu, K. Strategic Thinking on China’s Food Security during the “Fourteenth Five-Year Plan” Period. J. Xinjiang Norm. Univ. (Philos. Soc. Sci.) 2021, 42, 134–144. [Google Scholar] [CrossRef]
- Anagha, K.S.; Kuttippurath, J.; Sharma, M.; Cuesta, J. A comprehensive assessment of yield loss in rice due to surface ozone pollution in India during 2005-2020: A great concern for food security. Agric. Syst. 2024, 215, 103849. [Google Scholar] [CrossRef]
- FAO. The State of Food and Agriculture 2019. Moving Forward on Food Loss and Waste Reduction; FAO: Rome, Italy, 2019. [Google Scholar]
- Kantor, L.S.; Lipton, K.L.; Manchester, A.C.; Oliveira, V.H.d. Estimating and Addressing America’s Food Losses. Food Rev./Natl. Food Rev. 1997, 20, 2–12. [Google Scholar]
- Sawicka, B. Post-Harvest Losses of Agricultural Produce. In Zero Hunger; Springer: Cham, Switzerland, 2019; pp. 654–669. [Google Scholar]
- Tefera, T. Post-harvest losses in African maize in the face of increasing food shortage. Food Secur. 2012, 4, 267–277. [Google Scholar] [CrossRef]
- Pereira, L.M.; Drimie, S.; Maciejewski, K.; Tonissen, P.B.; Biggs, R. Food System Transformation: Integrating a Political–Economy and Social–Ecological Approach to Regime Shifts. Int. J. Environ. Res. Public Health 2020, 17, 1313. [Google Scholar] [CrossRef]
- Balan, I.; Trașcă, T.; Brad, I.; Belc, N.; Tulcan, C.; Radoi, B.; Rinovetz, A. Approaches to Limiting Food Loss and Food Waste. In Zero Hunger; Springer: Cham, Switzerland, 2023; pp. 215–244. [Google Scholar]
- Amponsah, S.; Addo, A.; Dzisi, K.; Asante, B.; Afona, D. Assessment of Rice Farmers’ Knowledge and Perception of Harvest and Postharvest Losses in Ghana. Cogent Food Agric. 2018, 4, 1471782. [Google Scholar] [CrossRef]
- FAO. Global Food Losses and Food Waste—Extent, Causes and Prevention; FAO: Rome, Italy, 2011. [Google Scholar]
- Scialabba, N. Food Wastage Footprint & Climate Change; Food and Agriculture Organization of the United Nations: Rome, Italy, 2015; p. 4. [Google Scholar]
- Hodges, R.; Bernard, M.; Rembold, F. APHLIS-Postharvest Cereal Losses in Sub-Saharan Africa, Their Estimation, Assessment and Reduction; European Commission: Luxembourg, 2014. [Google Scholar]
- National Bureau of Statistics (NBS). Announcement of National Bureau of Statistics on Grain Output Data in 2022. Available online: http://www.stats.gov.cn/sj/zxfb/202302/t20230203_1901673.html (accessed on 24 February 2024).
- Gao, L.; Xu, S.; Li, Z.; Cheng, S.; Yu, W.; Zhang, Y.; Li, D.; Wang, Y.; Wu, C. Main grain crop postharvest losses and its reducing potential in China. Trans. Chin. Soc. Agric. Eng. 2016, 32, 1–11. [Google Scholar] [CrossRef]
- Zhao, X.; Tao, Y.; Cao, B. Assessment on post-harvest losses of grains in China. J. Arid Land Resour. Environ. 2022, 36, 1–7. [Google Scholar] [CrossRef]
- Weimin, T. Causes of post-production loss of grain in China and effective measures to reduce the loss. Grain Circ. Technol. 1998, 1, 1–5. [Google Scholar]
- Yin, G. Evaluation and countermeasures of grain loss after production in recent years in China. Cereal Feed Ind. 2017, 3, 1–3. [Google Scholar]
- Zhang, N.; Wu, W.; Wang, Y.; Li, S. Hazard Analysis of Traditional Post-Harvest Operation Methods and the Loss Reduction Effect Based on Five Time (5T) Management: The Case of Rice in Jilin Province, China. Agriculture 2021, 11, 877. [Google Scholar] [CrossRef]
- Wang, Y.; Wu, W.; Wu, Z.; Zhang, N.; Li, S.; Meng, X. Revealing a Significant Latent Loss of Dry Matter in Rice Based on Accurate Measurement of Grain Growth Curve. Agriculture 2022, 12, 465. [Google Scholar] [CrossRef]
- Qi, M. The practice of fine agriculture promotes the innovation and development of smart agriculture. China’s Natl. Cond. Natl. Strength 2018, 7, 6–7+5. [Google Scholar] [CrossRef]
- GB/T 20264-2006; Grain and Oilseed-Determination of Moisture Content-Twice Drying Method. General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China: Beijing, China, 2006.
- GB/T 5519-2018; Cereals and Pulses-Determination of the Mass of 1000 Grains. General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China: Beijing, China, 2018.
- Khir, R.; Atungulu, G.; Chao, D.; Pan, Z. Influences of harvester and weather conditions on field loss and milling quality of rough rice. Int. J. Agric. Biol. Eng. 2017, 10, 216–223. [Google Scholar] [CrossRef]
- Gao, Y.; Zhou, J.; Cao, Y. Investigation of rice shattering character. Mod. Agric. Sci. Technol. 2012, 79, 83. [Google Scholar] [CrossRef]
- Gou, Y.; Yang, W.; Lin, S.; Gao, Y.; Luan, X. Research Progress on Rice Shattering. Chin. J. Rice Sci. 2019, 33, 479–488. [Google Scholar] [CrossRef]
- Wu, L.; Hu, Q.; Zhu, D.; Wang, J. Empirical Analysis of Main Influencing Factors of Rice Harvest Loss—Based on Ordered Multi-classification Logistic Model. China Rural Surv. 2015, 126, 22–33, 95. [Google Scholar]
- Jiang, M. Mathematical pattern for the elongation growth of rice. J. Biomath 1995, 10, 54–63. [Google Scholar]
- Yan, D.; Zhu, Y.; Cao, W.; Wang, S. A Knowledge Model for Design of Suitable Dynamics of Growth Index in Rice. Sci. Agric. Sin. 2005, 38, 38–44. [Google Scholar] [CrossRef]
- Yin, H. Physiological study on high yield of rice and wheat in Chinese. Plant Physiol. Commun. 1964, 1, 13–22. [Google Scholar] [CrossRef]
- Liu, H.; Zhou, T. The Effect on the Taste Quality from the Timely Harvesting and Drying Process of Rice. North Rice 2017, 47, 1–6. [Google Scholar] [CrossRef]
- National Food and Strategic Reserves Administration. Notice of the National Development and Reform Commission and Other Departments on Announcing the Minimum Purchase Price of Rice in 2023. Available online: http://www.lswz.gov.cn/html/ywpd/lstk/2023-03/02/content_273830.shtml (accessed on 14 March 2024).
Plot Location | Longitude | Latitude | Date | Variety |
---|---|---|---|---|
Jilin City, Jilin Province | 126.37 | 44.02 | 11 September 2019–11 October 2019 | Jijing 816, Wuyoudao 4 |
5 September 2020–17 October 2020 | Jijing 511, Jijing 816, Jinongda 667, Qinglin 611, Tianlong 619, Wokeshou 1, Fangyuan 77, Zhongke 804, Zhongkefa 5, Daohuaxiang 9, Wuyoudao 4, Longyang 13, Zaoxiang 7, Longyang 16, Songjing 29, Longyang 7, DF416, Jijing 561 | |||
19 September 2020–16 October 2020 | Songjing 16, Jihong 6 | |||
8 September 2021–28 October 2021 | Jihong 6, Chaojidao | |||
10 September 2022–20 October 2022 | Jihong 6 | |||
Changchun City, Jilin Province | 125.74 | 44.08 | 20 September 2022–10 October 2022 | Changjing 729, Nongjing 306, Deyu 317, Tongke 28 |
Gongzhuling City, Jilin Province | 124.75 | 43.47 | 12 September 2019–9 October 2019 | Jijing 528 |
Baicheng City, Jilin Province | 122.53 | 45.35 | 10 September 2022–20 October 2022 | Jihong 6, Daohuaxiang 2 |
Jiamusi City, Heilongjiang Province | 130.37 | 46.79 | 27 September 2020–18 October 2020 | Xinfeng 6, Longdun 1614 |
23 August 2021–12 October 2021 | Xinfeng 6 | |||
Wuchang City, Heilongjiang Province | 127.36 | 45.03 | 15 September 2020–12 October 2020 | Daohuaxiang 2 |
1 September 2021–30 October 2021 | Daohuaxiang 2 | |||
10 September 2022–20 October 2022 | Daohuaxiang 2, Zhongkefa 5 | |||
Panjin City, Liaoning Province | 122.07 | 40.75 | 15 September 2021–31 October 2021 | Yanfeng 47, Qiaoyuxietian |
10 September 2022–20 October 2022 | Tianlong 619 | |||
Mishan City, Heilongjiang Province | 131.85 | 45.53 | 20 September 2022–25 October 2022 | Qijing 10 |
Yancheng City, Jiangsu Province | 120.15 | 33.40 | 15 September 2022–10 October 2022 | Ningxiangjing 9 |
Nanchang, Jiangxi Province | 115.97 | 28.79 | 20 September 2022–31 October 2022 | Yexiangyoulisi |
Variety | Regression Equation | R2 | Variety | Regression Equation | R2 |
---|---|---|---|---|---|
Jijing 816 | y = −0.0372 × X + 22.390 | 0.6850 | Zhongke 804 | y = −0.0344 × X + 26.035 | 0.6178 |
Wuyoudao 4 | y = −0.0347 × X + 27.865 | 0.7502 | Jinongda 667 | y = −0.0295 × X + 21.654 | 0.3805 |
Longyang 16 | y = −0.0320 × X + 24.502 | 0.6337 | Wokeshou 1 | y = −0.0216 × X + 25.358 | 0.3345 |
Songjing 29 | y = −0.0182 × X + 24.218 | 0.4218 | Tianlong 619 | y = −0.0324 × X + 30.183 | 0.6548 |
Longyang 7 | y = −0.0274 × X + 25.038 | 0.5872 | Jijing 511 | y = −0.0273 × X + 21.822 | 0.8062 |
Fangyuan 77 | y = −0.0234 × X + 27.997 | 0.2620 | DF 416 | y = −0.0545 × X + 30.121 | 0.8323 |
Jijing 561 | y = −0.0370 × X + 25.728 | 0.9489 | Qinglin 611 | y = −0.0317 × X + 25.020 | 0.6190 |
Longyang 13 | y = −0.0333 × X + 25.580 | 0.7035 | Daohuaxiang 9 | y = −0.0133 × X + 25.084 | 0.3448 |
Zhongkefa 5 | y = −0.0595 × X + 29.708 | 0.3984 | Zaoxiang 7 | y = −0.0371 × X + 27.337 | 0.6381 |
Year | Experimental Method | Fallen Grain Loss Rate | Dry Matter Loss Rate | Latent Loss Rate |
---|---|---|---|---|
2019 | 1000-grain weight determination method | 3.47% | ||
2020 | 1000-grain weight determination method | 3.53% | ||
Shattering weight determination method | 3.02% | |||
Unit area determination method | 7.16% | |||
2021 | Shattering weight determination method | 0.93% | ||
Unit area determination method | 7.05% | |||
2022 | 100-spike weight determination method | 11.41% | ||
Fixed area tracking method | around 5% |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Wang, Y.; Wu, W.; Xu, J.; Wang, Y.; Wu, Z.; Liu, H. Expounding the Effect of Harvest Management on Rice (Oryza sativa L.) Yield and Latent Loss Based on the Accurate Measurement of Grain Data. Agronomy 2024, 14, 1346. https://doi.org/10.3390/agronomy14071346
Wang Y, Wu W, Xu J, Wang Y, Wu Z, Liu H. Expounding the Effect of Harvest Management on Rice (Oryza sativa L.) Yield and Latent Loss Based on the Accurate Measurement of Grain Data. Agronomy. 2024; 14(7):1346. https://doi.org/10.3390/agronomy14071346
Chicago/Turabian StyleWang, Yujia, Wenfu Wu, Jie Xu, Yong Wang, Zidan Wu, and Houqing Liu. 2024. "Expounding the Effect of Harvest Management on Rice (Oryza sativa L.) Yield and Latent Loss Based on the Accurate Measurement of Grain Data" Agronomy 14, no. 7: 1346. https://doi.org/10.3390/agronomy14071346