Analysis of Weed Communities in Solar Farms Located in Tropical Areas—The Case of Malaysia
Abstract
:1. Introduction
2. Materials and Methods
3. Results and Discussion
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
References
- IRENA. Future of Solar Photovoltaic: Deployment, Investment, Technology, Grid Integration and Socio-Economic Aspects; Abu Dhabi, UAE, 2019. Available online: https://www.irena.org/publications/2019/Nov/Future-of-Solar-Photovoltaic (accessed on 1 October 2022).
- Lu, L.; Ya’Acob, M.E.; Anuar, M.S.; Mohtar, M.N. Comprehensive review on the application of inorganic and organic photovoltaics as greenhouse shading materials. Sustain. Energy Technol. Assess. 2022, 52, 102077. [Google Scholar] [CrossRef]
- Malaysia Energy Statistics Handbook 2020; Putrajaya, Malaysia, 2020. Available online: https://www.st.gov.my/en/contents/files/download/116/Malaysia_Energy_Statistics_Handbook_20201.pdf (accessed on 1 October 2022).
- Energy Commision of Malaysia. Available online: https://www.st.gov.my/ (accessed on 1 October 2022).
- Goetzberger, A.; Zastrow, A. On the Coexistence of Solar-Energy Conversion and Plant Cultivation. Int. J. Sol. Energy 1982, 1, 55–69. [Google Scholar] [CrossRef]
- Movellan, J. Japan Next-Generation Farmers Cultivate Crops and Solar Energy. Renew Energy World. 2013. Available online: https://www.renewableenergyworld.com/solar/japan-next-generation-farmers-cultivate-agriculture-and-solar-energy/#gref (accessed on 15 November 2022).
- Dupraz, C.; Marrou, H.; Talbot, G.; Dufour, L.; Nogier, A.; Ferard, Y. Combining solar photovoltaic panels and food crops for optimising land use: Towards new agrivoltaic schemes. Renew. Energy 2011, 36, 2725–2732. [Google Scholar] [CrossRef]
- Leon, A.; Ishihara, K.N. Assessment of new functional units for agrivoltaic systems. J. Environ. Manag. 2018, 226, 493–498. [Google Scholar] [CrossRef] [PubMed]
- Weselek, A.; Ehmann, A.; Zikeli, S.; Lewandowski, I.; Schindele, S.; Högy, P. Agrophotovoltaic systems: Applications, challenges, and opportunities. A review. Agron. Sustain. Dev. 2019, 39, 35. [Google Scholar] [CrossRef]
- Liu, W.; Liu, L.; Guan, C.; Zhang, F.; Li, M.; Lv, H.; Yao, P.; Ingenhoff, J. A novel agricultural photovoltaic system based on solar spectrum separation. Sol. Energy 2018, 162, 84–94. [Google Scholar] [CrossRef]
- Othman, N.; Ya’Acob, M.; Abdul-Rahim, A.; Othman, M.S.; Radzi, M.; Hizam, H.; Wang, Y.; Ya’Acob, A.; Jaafar, H. Embracing new agriculture commodity through integration of Java Tea as high Value Herbal crops in solar PV farms. J. Clean. Prod. 2014, 91, 71–77. [Google Scholar] [CrossRef]
- Chen, J.; Liu, Y.; Wang, L. Research on Coupling Coordination Development for Photovoltaic Agriculture System in China. Sustainability 2019, 11, 1065. [Google Scholar] [CrossRef] [Green Version]
- Rollet, C. European Agrivoltaics. PV Mag 2020. Available online: https://www.pv-magazine.com/2020/03/20/european-agrivoltaics/ (accessed on 15 November 2022).
- Ya’acob, M.E.; Othman, N.F.; Buda, M.; Jani, E.; Mat Su, A.S. Field Assessment on Agrivoltaic Misai Kucing Techno-Economical Approach in Solar Farming; IEEE: Washington, DC, USA, 2021; pp. 1–6. [Google Scholar] [CrossRef]
- Mishra, A.M.; Gautam, V. Weed Species Identification in Different Crops Using Precision Weed Management: A Review; CEUR Workshop Proc.: Tenerife, Spain, 2021; Volume 2786, pp. 180–194. [Google Scholar]
- Rahman, M.; Blackwell, B.; Banerjee, N.; Saraswat, D. Smartphone-based hierarchical crowdsourcing for weed identification. Comput. Electron. Agric. 2015, 113, 14–23. [Google Scholar] [CrossRef]
- Zhang, Y.; Staab, E.S.; Slaughter, D.C.; Giles, D.K.; Downey, D. Automated weed control in organic row crops using hyperspectral species identification and thermal micro-dosing. Crop Prot. 2012, 41, 96–105. [Google Scholar] [CrossRef]
- Veeranampalayam Sivakumar, A.N.; Li, J.; Scott, S.; Psota, E.; Jhala, A.J.; Luck, J.D.; Shi, Y. Comparison of Object Detection and Patch-Based Classification Deep Learning Models on Mid- to Late-Season Weed Detection in UAV Imagery. Remote Sens. 2020, 12, 2136. [Google Scholar] [CrossRef]
- Naidu, V.S.G.R. Hand Book on Weed Identification; Directorate of Weed Science Research: Jabalpur, India, 2012. [Google Scholar]
- Sabzi, S.; Abbaspour-Gilandeh, Y.; García-Mateos, G. A fast and accurate expert system for weed identification in potato crops using metaheuristic algorithms. Comput. Ind. 2018, 98, 80–89. [Google Scholar] [CrossRef]
- Tang, J.; Wang, D.; Zhang, Z.; He, L.; Xin, J.; Xu, Y. Weed identification based on K-means feature learning combined with convolutional neural network. Comput. Electron. Agric. 2017, 135, 63–70. [Google Scholar] [CrossRef]
- MacCracken, J.G.; Uresk, D.W.; Hansen, R.M. Rodent-Vegetation Relationships in Southeastern Montana. Northwest Sci. 1985, 57, 272–278. [Google Scholar]
- Tietje, W.D.; Lee, E.E.; Vreeland, U.K. Survival and abundance of three species of mice in relation to density of shrubs and prescribed fire in understory of an oak woodland in California. Southwest Nat. 2008, 53, 357–369. [Google Scholar] [CrossRef]
- Hyundai Solar Energy Co. Ltd. Solar Power Plant O & M plan—Impact of Weeds on Solar Power Plants; Hyundai Sol Energy Co. Ltd.: Gyeonggi-do, Republic of Korea, 2018; Available online: http://www.hdso-lar.co.kr/solar-power-informations/?lang=en&ckattempt=2&mod=document&uid=381 (accessed on 1 October 2022).
- Marshall. How Pests Damage Solar PV Systems. Slick Tools Llc 2021. Available online: https://slicktoolsllc.com/how-pests-damage-solar-pv-systems/ (accessed on 18 January 2022).
- Ekpenyong, E.; Anyasi, F. Effect of Shading on Photovoltaic Cell. IOSR J. Electr. Electron. Eng. 2013, 8, 1–6. [Google Scholar] [CrossRef]
- Alzahrani, G.S.; Alzahrani, F.S.; Nahhas, A.M. Study of the Specific Factors Effecting the PV Solar Cell’s Efficiency in Saudi Arabia. Sustain. Energy 2020, 8, 6–11. [Google Scholar] [CrossRef]
- Dilipkumar, M.; Chuah, T.S.; Goh, S.S.; Sahid, I. Weed management issues, challenges, and opportunities in Malaysia. Crop Prot. 2020, 134, 104347. [Google Scholar] [CrossRef]
- Sa, I.; Popović, M.; Khanna, R.; Chen, Z.; Lottes, P.; Liebisch, F.; Nieto, J.; Stachniss, C.; Walter, A.; Siegwart, R. WeedMap: A Large-Scale Semantic Weed Mapping Framework Using Aerial Multispectral Imaging and Deep Neural Network for Precision Farming. Remote Sens. 2018, 10, 1423. [Google Scholar] [CrossRef] [Green Version]
- Mohidem, N.A.; Che’Ya, N.N.; Juraimi, A.S.; Ilahi, W.F.F.; Roslim, M.H.M.; Sulaiman, N.; Saberioon, M.; Noor, N.M. How Can Unmanned Aerial Vehicles Be Used for Detecting Weeds in Agricultural Fields? Agriculture 2021, 11, 1004. [Google Scholar] [CrossRef]
- Gašparović, M.; Zrinjski, M.; Barković, Đ.; Radočaj, D. An automatic method for weed mapping in oat fields based on UAV imagery. Comput. Electron. Agric. 2020, 173, 105385. [Google Scholar] [CrossRef]
- Alexandridis, T.K.; Tamouridou, A.A.; Pantazi, X.E.; Lagopodi, A.L.; Kashefi, J.; Ovakoglou, G.; Polychronos, V.; Moshou, D. Novelty Detection Classifiers in Weed Mapping: Silybum marianum Detection on UAV Multispectral Images. Sensors 2017, 17, 2007. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Alvarez-Hess, P.; Thomson, A.; Karunaratne, S.; Douglas, M.; Wright, M.; Heard, J.; Jacobs, J.; Morse-McNabb, E.; Wales, W.; Auldist, M. Using multispectral data from an unmanned aerial system to estimate pasture depletion during grazing. Anim. Feed Sci. Technol. 2021, 275, 114880. [Google Scholar] [CrossRef]
- Liao, K.-C.; Lu, J. Using UAV to Detect Solar Module Fault Conditions of a Solar Power Farm with IR and Visual Image Analysis. Appl. Sci. 2021, 11, 1835. [Google Scholar] [CrossRef]
- Gammill, M.; Sherman, M.; Raissi, A.; Hassanalian, M. Energy Harvesting Mechanisms for a Solar Photovoltaic Plant Monitoring Drone: Thermal Soaring and Bioinspiration. In Proceedings of the AIAA Scitech 2021 Forum, Nashville, TN, USA, 11–15 January 2021; p. 1053. [Google Scholar] [CrossRef]
- Jã¡uregui, J.M.; Delbino, F.G.; Bonvini, M.I.B.; Berhongaray, G. Determining yield of forage crops using the Canopeo mobile phone app. J. New Zealand Grasslands 2019, 81, 41–46. [Google Scholar] [CrossRef]
- Nobilly, F.; Atikah, S.N.; Yahya, M.S.; Jusoh, S.; Cun, G.S.; Norhisham, A.R.; Tohiran, K.A.; Zulkifli, R.; Azhar, B. Rotational cattle grazing improves understory vegetation biodiversity and structural complexity in oil palm plantations. Weed Biol. Manag. 2022, 22, 13–26. [Google Scholar] [CrossRef]
- Egbadzor, K.F. Studies on baobab diversity, seed germination and early growth. South Afr. J. Bot. 2020, 133, 178–183. [Google Scholar] [CrossRef]
- Achieng, A.O.; Masese, F.O.; Kaunda-Arara, B. Fish assemblages and size-spectra variation among rivers of Lake Victoria Basin, Kenya. Ecol. Indic. 2020, 118, 106745. [Google Scholar] [CrossRef]
- González-Esquiva, J.M.; Oates, M.J.; García-Mateos, G.; Moros-Valle, B.; Molina-Martínez, J.M.; Ruiz-Canales, A. Development of a visual monitoring system for water balance estimation of horticultural crops using low cost cameras. Comput. Electron. Agric. 2017, 141, 15–26. [Google Scholar] [CrossRef]
- Nobilly, F.; Maxwell, T.M.R.; Yahya, M.S.; Azhar, B. Application of Targeted Goat Grazing in Oil Palm Plantations: Assessment of Weed Preference, Spatial Use of Grazing Area and Live Weight Change. J. Oil Palm. Res. 2021, 34, 289–299. [Google Scholar] [CrossRef]
- Patrignani, A.; Ochsner, T.E. Canopeo: A Powerful New Tool for Measuring Fractional Green Canopy Cover. Agron. J. 2015, 107, 2312–2320. [Google Scholar] [CrossRef] [Green Version]
- Tohiran, K.A.; Nobilly, F.; Zulkifli, R.; Ashton-Butt, A.; Azhar, B. Cattle-grazing in oil palm plantations sustainably controls understory vegetation. Agric. Ecosyst. Environ. 2019, 278, 54–60. [Google Scholar] [CrossRef]
- Yellareddygari, S.; Gudmestad, N. Bland-Altman comparison of two methods for assessing severity of Verticillium wilt of potato. Crop Prot. 2017, 101, 68–75. [Google Scholar] [CrossRef]
- Singapore Natural Parks Board. Explore Our Parks and Gardens; Singapore Nat Park Board 2022. Available online: https://www.nparks.gov.sg/ (accessed on 22 January 2022).
- CABI. Invasive Species Compendium. CABI 2022. Available online: https://www.cabi.org/isc/ (accessed on 22 January 2022).
- Malaysia Biodiversity Centre. Malaysia Biodiversity Information System. Malaysia Biodivers Cent 2016. Available online: https://www.mybis.gov.my/one/ (accessed on 22 January 2022).
- Botanical Institutions. The Plant List. Bot Institutions 2013. Available online: http://www.theplantlist.org/ (accessed on 22 January 2022).
- Ministry of Natural Resources and Environment Malaysia. Frim Flora Database. Minist Nat Resour Environ Malaysia 2016. Available online: https://mycites.frim.gov.my/en/ (accessed on 22 January 2022).
- Australian Centre for International Agricultural Research. Tropical Forages. Aust. Cent. Int. Agric. Res. 2020. Available online: https://www.tropicalforages.info/text/intro/index.html (accessed on 22 January 2022).
- Morad, A.F. Flickr 2021. Available online: https://www.flickr.com/people/adaduitokla/ (accessed on 16 January 2022).
- Tohiran, K.A.; Nobilly, F.; Zulkifli, R.; Maxwell, T.; Moslim, R.; Azhar, B. Targeted cattle grazing as an alternative to herbicides for controlling weeds in bird-friendly oil palm plantations. Agron. Sustain. Dev. 2017, 37, 62. [Google Scholar] [CrossRef] [Green Version]
- Chung, G.F.; Lee, C.T.; Chiu SBin Chee, K.H. New book: Pictorial guide to common weeds of plantations and their control. Agric. Sci. J. 2015, 1, 59–61. [Google Scholar]
- Huang, Z.; Cao, H.; Liang, X.; Ye, W.; Feng, H.; Cai, C. The growth and damaging effect of Mikania micrantha in different habitats. J. Trop. Subtrop. Bot. 2000, 8, 131–138. [Google Scholar] [CrossRef]
- Moyo, C. Improving the Efficiency of Herbicide Application to Pasture Weeds by Weed-Wiping and Spot-Spraying. Ph.D. Thesis, Massey University, Palmerston North, New Zealand, 2008. [Google Scholar]
- Tian, J.; Yu, L.; Xue, R.; Zhuang, S.; Shan, Y. Global low-carbon energy transition in the post-COVID-19 era. Appl. Energy 2021, 307, 118205. [Google Scholar] [CrossRef] [PubMed]
- Poonia, S.; Jat, N.; Santra, P.; Singh, A.; Jain, D.; Meena, H. Techno-economic evaluation of different agri-voltaic designs for the hot arid ecosystem India. Renew. Energy 2021, 184, 149–163. [Google Scholar] [CrossRef]
- Jatoi, A.S.; Hashmi, Z.; Adriyani, R.; Yuniarto, A.; Mazari, S.A.; Akhter, F.; Mubarak, N.M. Recent trends and future challenges of pesticide removal techniques—A comprehensive review. J. Environ. Chem. Eng. 2021, 9, 105571. [Google Scholar] [CrossRef]
Parameter | Number of Weed Species | Abundance of Weeds | Vegetation Coverage (%) | Vegetation Height (cm) |
---|---|---|---|---|
Mean | 9.083 | 141.9 | 31.25 | 29.34 |
Median | 9 | 104 | 27.93 | 24.75 |
Minimum | 3 | 9 | 1.72 | 4.5 |
Maximum | 16 | 608 | 84.3 | 148.5 |
Lower quartile | 7 | 75.5 | 17.69 | 14.25 |
Upper quartile | 11 | 198 | 44.94 | 34 |
Standard deviation | 2.782 | 108.6 | 18.03 | 24.85 |
Coefficient of variation | 30.63 | 76.53 | 57.68 | 84.68 |
Species | Av.Abund | Av.Sim | Sim/SD | Contrib% | Cum.% |
---|---|---|---|---|---|
Ageratum conyzoides | 6.68 | 14.25 | 1.30 | 43.05 | 43.05 |
Lindernia crustacea | 2.45 | 3.13 | 0.61 | 9.46 | 52.51 |
Eleusine indica | 1.97 | 3.08 | 0.66 | 9.31 | 61.82 |
Echinochloa colona | 1.77 | 2.66 | 0.61 | 8.03 | 69.85 |
Phyllantus amarus | 1.54 | 1.93 | 0.55 | 5.84 | 75.69 |
Paspalum scrobiculatum | 0.84 | 1.22 | 0.42 | 3.69 | 79.39 |
Fimbristylis globulosa | 1.53 | 1.18 | 0.33 | 3.57 | 82.96 |
Oldenlandia corymbosa | 0.82 | 0.80 | 0.38 | 2.40 | 85.36 |
Mimosa pudica | 0.43 | 0.73 | 0.45 | 2.19 | 87.55 |
Brachiaria mutica | 0.84 | 0.65 | 0.35 | 1.96 | 89.51 |
Habit | Family | Scientific Name | Common Name | Local Name |
---|---|---|---|---|
Climber | Asteraceae | Mikania micrantha | Mile-a-minute | Ulam tikus |
Creeper | Asteraceae | Tridax procumbens | Coat buttons | Kanching baju |
Convovulaceae | Ipomoea triloba | Little bell morning glory | Kangkung bulu | |
Merremia umbellata | Greater Malayan bindweed | Ulan tapak pelanduk | ||
Cucurbitaceae | Cucumis maderaspatanus | Madras pea pumpkin | - | |
Gymnopetalum scabrum | - | - | ||
Scopellaria marginata | - | Timun tikus | ||
Fabaceae | Alysicarpus vaginalis | Alyce clover | - | |
Calopogonium mucunoides | Calopo | Kacang asu | ||
Desmodium heterophyllum | Spanish clover | Rumput sisik naga | ||
Desmodium trifolium | Tick clover | Sisik naga | ||
Mimosa pudica | Touch-me-not | Semalu | ||
Passifloraceae | Passiflora foetida | Stinking passionflower | Timun padang | |
Passiflora suberosa | Devil’s pumpkin | - | ||
Fern | Gleicheniaceae | Dicranopteris linearis | Tropical bracken | Resam |
Lycopodiaceae | Lycopodiella cernua | Staghorn club moss | Paku serani | |
Nephrolepidaceae | Nephrolepis biserrata | Giant sword fern | Paku larat | |
Schizaeaceae | Lygodium microphyllum | Climbing maidenhair fern | Ribu-ribu | |
Grass | Poaceae | Axonopus compressus | Cow grass | Rumput parit |
Brachiaria mutica | Para grass | Suket babang | ||
Cynodon dactylon | Bermuda grass | Rumput minyak | ||
Dactyloctenium aegyptium | Egyptian crowfoot grass | Rumput jari mesir | ||
Echinochloa colona | Bird rice | Padi burung | ||
Eleusine indica | Goose grass | Suket wululang | ||
Eragrotis tenella | Bug’s egg grass | Rumput telur kutu | ||
Imperata cylindrica | Sword grass | Lalang | ||
Paspalum scrobiculatum | Bull Paspalum | Rumput ketih belalang | ||
Pennisetum purpureum | Napier grass | Rumput gajah | ||
Sacciolepis indica | Glenwood grass | Rumput bidis | ||
Sporobolus indicus | Common drop-seed | Suket sadan | ||
Herb | Acanthaceae | Asystasia gangetica | Chinese violet | Rumput Israel |
Asteraceae | Ageratum conyzoides | Billy goat weed | Rumput tahi ayam | |
Eclipta prostata | False daisy | Urang-aring | ||
Emilia sonchifolia | Sow thistle | Bayam terkukur | ||
Erigeron sumatrensis | Fleabane | Sawi bulan | ||
Cleomaceae | Cleome rutidosperma | Yellow cleome | Maman ungu | |
Euphorbiaceae | Croton hirtus | Hairy croton | - | |
Euphorbia hirta | Hairy spurge | Ara tanah | ||
Phyllantus amarus | Lagoon spurge | Dukung anak | ||
Fabaceae | Indigofera hirsuta | Hairy indigo | Kacang babi | |
Lamiaceae | Plectranthus monostachyus | Monkey’s potato | - | |
Linderniaceae | Lindernia crustacea | Malaysian false pimpernel | Akar kerak nasi | |
Loganiaceae | Spigelia anthelmia | Pinkroot | - | |
Onagraceae | Ludwigia hyssopifolia | Seedbox | Inai pasir | |
Polygalaceae | Polygala paniculata | Root beer plant | Akar wangi | |
Salomonia cantoniensis | Common Salomonia | Rumput buak | ||
Rubiaceae | Borreria laevicaulis | Purple-leaved button weed | Kerekah batu | |
Borreria latifolia | Broadleaf-button weed | Rumput setawar | ||
Borreria setidens | Toothed button weed | Kemangi jantan | ||
Oldenlandia corymbosa | Old world diamond-flower | Siku-siku | ||
Scrophulariaceae | Scoparia dulcis | Macao tea | Pokok delis | |
Solanaceae | Physalis minima | Bladder cherry | Letup-letup | |
Verbenaceae | Stachytarpheta jamaicensis | Light blue snake-weed | Jolok cacing | |
Sedge | Cyperaceae | Cyperus brevifolius | Shortlead spike sedge | - |
Cyperus digitatus | Digitate Cyperus | Rumput bunga satuan | ||
Fimbristylis globulosa | Globular Fimbristylis | Rumput sandang | ||
Shrub | Asteraceae | Bidens alba | Butterfly needles | Subang puteri |
Chromolaena odorata | Siam weed | Pokok kapal terbang | ||
Malvaceae | Waltheria indica | Boaterbush | - | |
Melastomaceae | Clidemia hirta | Hairy clidemia | Senduduk bulu | |
Melastoma malabathricum | Straits rhododendron | Senduduk | ||
Solanaceae | Solanum cf. coagulans | - | - | |
Tree | Moraceae | Ficus hispida | River fig | Ara nasi |
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Ya’acob, M.E.; Lu, L.; Nobilly, F.; Che’Ya, N.N.; Aziz, A.A.; Dupraz, C.; Yahya, M.S.; Atikah, S.N.; Mamun, M.A.A. Analysis of Weed Communities in Solar Farms Located in Tropical Areas—The Case of Malaysia. Agronomy 2022, 12, 3073. https://doi.org/10.3390/agronomy12123073
Ya’acob ME, Lu L, Nobilly F, Che’Ya NN, Aziz AA, Dupraz C, Yahya MS, Atikah SN, Mamun MAA. Analysis of Weed Communities in Solar Farms Located in Tropical Areas—The Case of Malaysia. Agronomy. 2022; 12(12):3073. https://doi.org/10.3390/agronomy12123073
Chicago/Turabian StyleYa’acob, Mohammad Effendy, Li Lu, Frisco Nobilly, Nik Norasma Che’Ya, Ammar Abdul Aziz, Christian Dupraz, Muhammad Syafiq Yahya, Sharifah Nur Atikah, and Mohammad Abdullah Al. Mamun. 2022. "Analysis of Weed Communities in Solar Farms Located in Tropical Areas—The Case of Malaysia" Agronomy 12, no. 12: 3073. https://doi.org/10.3390/agronomy12123073
APA StyleYa’acob, M. E., Lu, L., Nobilly, F., Che’Ya, N. N., Aziz, A. A., Dupraz, C., Yahya, M. S., Atikah, S. N., & Mamun, M. A. A. (2022). Analysis of Weed Communities in Solar Farms Located in Tropical Areas—The Case of Malaysia. Agronomy, 12(12), 3073. https://doi.org/10.3390/agronomy12123073