Abstract
Estimating coffee yield by measuring components of yield rather than complete harvests can improve the ability to account for heterogeneous conditions common in shade-grown farms and other agroforestry systems. We developed a rapid estimation technique that predicts yield per plant from (1) a visual estimation of the number of fruits per lateral summed across all laterals per plant, and (2) an average fruit dry mass derived from the random harvest of 50 fruits per plant. The technique was then applied to an experiment investigating three levels of managed tree shade (0, 40, and 60%) on Arabica coffee yield. Visual estimates of fruits per lateral proved to be good predictors of counts of fruits summed for each vertical (R2 = 0.97) and yield of the whole plant (R2 = 0.90). Simulations showed this was potentially better than existing methods to account for variable yield per vertical shoot at the whole-plant level and took less time or effort when applied to the plants in our study. We propose that fruits per lateral is an effective alternative component of yield that can account for heterogeneity in shade level, other environmental variables or management practices that can affect yield at small spatial scales in agroforestry systems.
Similar content being viewed by others
References
Bernardes T, Moreira MA, Adami M, Giarolla A, Rudorff BFT (2012) Monitoring biennial bearing effect on coffee yield using MODIS remote sensing imagery. Remote Sens 4:2492–2509. https://doi.org/10.3390/rs4092492
Bittenbender HC, Smith VE (2008) Growing coffee in Hawaii, 3rd edn. University of Hawaii, College of Tropical Agriculture and Human Resources, Honolulu
Bosselmann AS, Dons K, Oberthur T, Olsen CS, Ræbild A, Usma H (2009) The influence of shade trees on coffee quality in small holder coffee agroforestry systems in Southern Colombia. Agr Ecosyst Environ 129:253–260. https://doi.org/10.1016/j.agee.2008.09.004
Bote AD (2016) Examining growth, yield and bean quality of Ethiopian coffee trees: towards optimizing resources and tree management, Dissertation, Wageningen University
Bote AD, Jan V (2016) Branch growth dynamics, photosynthesis, yield and bean size distribution in response to fruit load manipulation in coffee trees. Trees 30:1275–1285. https://doi.org/10.1007/s00468-016-1365-x
Bote AD, Struik PC (2011) Effect of shade on growth, production and quality of coffee (Coffea arabica) in Ethiopia. J Hortic For 3(11):336–341
Buchanan S, Isaac ME, Meersche KV, Martin AR (2018) Functional traits of coffee along a shade and fertility gradient in coffee agroforestry systems. Agrofor Syst 1:10. https://doi.org/10.1007/s10457-018-0239-1
Campanha MM, Santos RHS, de Freitas GB, Martinez HEP, Garcia SLR, Finger FL (2004) Growth and yield of coffee plants in agroforestry and monoculture systems in Minas Gerais, Brazil. Agrofor Syst 63:75–82. https://doi.org/10.1023/B:AGFO.0000049435.22512.2d
Cannell MGR (1976) Crop physiological aspects of coffee bean yield—a review. Kenya Coffee 41:245–253
Cannell MGR (1985) Physiology of coffee crop. In: Clifford MN, Willson KC (eds) Coffee: botany, biochemistry and production of beans and beverage. Croom Helm, London, pp 108–134
Cerda R, Allinne C, Gary C, Tixier P, Harvey CA, Krolczyk L, Mathiot C, Clément E, Aubertot J-N, Avelino J (2017) Effects of shade, altitude and management on multiple ecosystem services in coffee agroecosystems. Eur J Agron 82:308–319. https://doi.org/10.1016/j.eja.2016.09.019
Cilas Ch, Descroix F (2009) Yield estimation and harvest period. In: Wintgens JN (ed) Coffee: growing, processing, sustainable production. A guidebook for growers, traders, and researchers, 2nd edn. Wiley, Weinheim, pp 601–609
CTAHR (2017) Pruning. In: Farmer’s bookshelf. College of tropical agriculture and human resources, University of Hawaii-Manoa, Honolulu, HI. https://www.ctahr.hawaii.edu/fb/coffee/coffee_pruning.html. Last Accessed 10 Oct 2017
DaMatta FM, Ronchi CP, Maestri M, Barros RS (2007) Ecophysiology of coffee growth and production. Braz J Plant Physiol 19:485–510. https://doi.org/10.1590/S1677-04202007000400014
Elevitch CR, Idol T, Friday JB, Lepczyk C, Smith E, Nelson SC (2009) Shade-grown coffee in Hawaii: results of a twelve farm study in Kona. Permanent Agriculture Resources, Holualoa
FAO (1970) Coffee. Food and Agriculture Organization of the United Nations, Abidjan, Ivory Coast
Gagliardi S, Martin AR, Virginio ED, Rapidel B, Isaac ME (2015) Intraspecific leaf economic trait variation partially explains coffee performance across agroforestry management regimes. Agric Ecosyst Environ 200:151–160. https://doi.org/10.1016/j.agee.2014.11.014
Haggar J, Barrios M, Bolaños M, Merlo M, Moraga P, Munguia R, Ponce A, Romero S, Soto G, Staver C (2011) Coffee agroecosystem performance under full sun, shade, conventional and organic management regimes in Central America. Agrofor Syst 82:285–301. https://doi.org/10.1007/s10457-011-9392-5
Idol T, Haggar J, Cox L (2011) Ecosystem services from smallholder forestry and agroforestry in the tropics. In: Campbell WB, Ortiz SL (eds) Integrating agriculture, conservation, and ecotourism: examples from the field. Springer, New York, pp 209–270
López-Bravo DF, Virginio-Filho EdM, Avelino J (2012) Shade is conducive to coffee rust as compared to full sun exposure under standardized fruit load conditions. Crop Prot 38:21–29. https://doi.org/10.1016/j.cropro.2012.03.011
Mariño YA, Pérez M-E, Gallardo F, Trifilio M, Cruz M, Bayman P (2016) Sun vs. shade affects infestation, total population and sex ratio of the coffee berry borer (Hypothenemus hampei) in Puerto Rico. Agric Ecosyst Environ 222:258–266
Mathiot C (2015) Effects of altitude, shade canopy, and management on coffee yield determinants and yield losses in Turrialba, Costa Rica. In: Internship Report. The Cascade Project. CATIE, Turrialba, Costa Rica
Nesper M, Kueffer C, Krishnan S, Kushalappa CG, Ghazoul J (2017) Shade tree diversity enhances coffee production and quality in agroforestry systems in the Western Ghats. Agric Ecosyst Environ 247:172–181. https://doi.org/10.1016/j.agee.2017.06.024
Perdoná MJ, Soratto RP (2016) Arabica coffee–macadamia intercropping: a suitable macadamia cultivar to allow mechanization practices and maximize profitability. Agron J 108:2301–2312. https://doi.org/10.2134/agronj2016.01.0024
Ritter A, Munoz-Carpena R (2013) Performance evaluation of hydrological models: statistical significance for reducing subjectivity in goodness-of-fit assessments. J Hydrol 480:33–45. https://doi.org/10.1016/j.jhydrol.2012.12.004
Somarriba E, Beer J, Muschler RG (2001) Research methods for multistrata agroforestry systems with coffee and cacao: recommendations from two decades of research at CATIE. Agrofor Syst 53:195–203. https://doi.org/10.1023/a:1013380605176
Soto-Pinto L, Perfecto I, Castillo-Hernandez J, Caballero-Nieto J (2000) Shade effect on coffee production at the northern Tzeltal zone of the state of Chiapas, Mexico. Agr Ecosyst Environ 80:61–69. https://doi.org/10.1016/S0167-8809(00)00134-1
Steiman S, Idol T, Bittenbender H, Gautz L (2011) Shade coffee in Hawaii-exploring some aspects of quality, growth, yield, and nutrition. Sci Hortic 128:152–158. https://doi.org/10.1016/j.scienta.2011.01.011
Upreti G, Bittenbender JL, Ingamells JL (1991) Rapid estimation of coffee yield. In: Proceedings of the Association Scientifique Internationale du Cafe. San Francisco, USA, pp 585–593
USDA/Foreign Agricultural Service (2017) Coffee: world markets and trade. Office of Global Analysis
Willmott CJ (1981) On the validation of models. Phys Geogr 2:184–194. https://doi.org/10.1080/02723646.1981.10642213
Wintgens JN (2009) The coffee plant. In: Wintgens JN (ed) Coffee: growing, processing, sustainable production. 2nd edn. Wiley, Weinheim, pp 3–24
Youkhana A, Idol T (2010) Growth, yield and value of managed coffee agroecosystem in Hawaii. Pac Agric Nat Resour 2:12–19
Youkhana A, Idol T (2016) Leucaena-KX2 mulch additions increase growth, yield and soil C and N in a managed full-sun coffee system in Hawaii. Agrofor Syst 90:325–337. https://doi.org/10.1007/s10457-015-9857-z
Acknowledgements
The authors thank the Waimanalo Research Station manager, Rogelio Corrales, and the agricultural technicians for their assistance with site preparation, experiment installation, and maintenance throughout the project period. The authors also thank Dr. HC “Skip” Bittenbender for his support in discussing yield estimation protocols and providing access to and training for laboratory equipment used in sample processing. This research was supported in part by a grant to the corresponding author from the US Department of Agriculture, Mcintire-Stennis program.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Idol, T.W., Youkhana, A.H. A rapid visual estimation of fruits per lateral to predict coffee yield in Hawaii. Agroforest Syst 94, 81–93 (2020). https://doi.org/10.1007/s10457-019-00370-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10457-019-00370-y