Skip to main content
Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Recommendations for increasing yield of the edible Pinus pinea L. pine nuts

  • Verónica Loewe-Muñoz ,

    Roles Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft

    vloewe@infor.cl

    Affiliations Chilean Forest Institute (INFOR), Santiago, Chile, Centro Nacional de Excelencia para la Industria de la Madera (CENAMAD), Pontificia Universidad Católica de Chile, Santiago, Chile

  • Claudia Delard,

    Roles Investigation, Validation, Writing – review & editing

    Affiliation Chilean Forest Institute (INFOR), Santiago, Chile

  • Rodrigo del Río,

    Roles Conceptualization, Data curation, Formal analysis, Methodology, Software, Validation, Visualization

    Affiliation Centro Nacional de Excelencia para la Industria de la Madera (CENAMAD), Pontificia Universidad Católica de Chile, Santiago, Chile

  • Mónica Balzarini

    Roles Conceptualization, Formal analysis, Methodology, Software, Supervision, Validation

    Affiliation CONICET UFYMA Biometry Unit, Universidad Nacional de Córdoba, Córdoba, Argentina

Abstract

In Pinus pinea, cone to pine nut yield (total pine nut weight expressed as percentage of cone weight), an important crop trait, is decreasing worldwide. This phenomenon is of great concern, since the nuts of this species are highly demanded. Cone weight, seed and pine nut morphometry, and pine nut yield were monitored in a non-native area in Chile for 10 years. For this purpose, 560 cones, and the seeds and pine nuts contained in them, were counted, measured and weighed in a multi-environment study involving seven plantations. Seed and pine nut damage was evaluated. Two contrasting categories of cone weight (heavy/light) were defined. Cone to pine nut yield (PY) and other traits were calculated and compared between categories using a mixed linear model. Regression trees were used to explain PY variability. Cone weight was higher than in the species’ native range (474 g vs 300 g on average). Pine nut number per cone and PY were significantly higher in the heavy cone category than in the light cone category (125 vs 89 units, and 4.05 vs 3.62%, respectively), The percentage of damaged seeds was lower in heavy than in light cones (9.0% vs 15.9%). Thus, PY depended on seed and pine nut morphometry as well as on seed health. Management practices, such as fertilization and irrigation, could be used to boost production of heavy cones and consequently increase PY.

Introduction

Pinus pinea L., commonly known as stone pine, is highly appreciated for its pine nuts; indeed, nut chemical composition includes high quality fats, proteins, vitamins, minerals and bioactive compounds [1]. The species, one of the most important nut species in the world, is harvested from native forests or plantations in Spain, Portugal, Italy, Turkey and Tunisia [2]. In has been consumed since ancient times [3]. Genetic material selection has been initiated and horticultural management techniques are being developed [4] with the aim to domesticate the species as an agronomic nut crop.

As far back as 2,800 years ago, stone pine was planted outside its native distribution range [5]. Due to the pine nut market opportunities, stone pine cropping has expanded to non-native countries, like Argentina [6], Australia [7], New Zealand [8] and Chile [9]. Significant efforts are being made to maximize pine nut production [10]. This is particularly important because only a very small fraction of cone weight corresponds to pine nuts [11]. Pine nut production, which exhibits a high inter-annual variability [12], is related to the number of cones and to the morphometric characters that define cone to pine nut yield (PY).

In the last decades, a severe reduction of cone to seed yield (from 17% to 5%) and PY (from 4% to 2%, or even less) has been reported in Europe [13], with apparently healthy cones containing up to 50% of empty seeds. This fact is relevant, since companies buy cones by weight instead of pine nut content [14]. Thus, the importance of PY monitoring has increased due to the growing presence of empty and damaged seeds [15], which was attributed to biotic (Leptoglossus occidentalis, [16, 17]) and abiotic damage (droughts, [13, 17]). The insect L. occidentalis was detected in Chile in 2017 [18, 19] and in Argentina in 2019 [20]. Even though cone weight was found to be correlated to pine nut number and weight [21, 22], a non-significant correlation was found between cone weight and PY in Chile [23].

The production of bigger cones has been related to an improved fruit quality [21]. Therefore, management practices such as fertilization and irrigation could be implemented to boost production of heavy cones, thereby improving PY. The objective of this study was to compare the number of damaged seeds and PY among different stone pine cone weight categories across a wide range of spatial-temporal variability in Chile. Our working hypothesis was that heavy cones would have a higher PY and a lower percentage of damaged seeds than light cones. Knowledge of PY is necessary to boost stone pine cropping in the local industry. Comparing harvest traits with values reported for the species in other parts of the world is of interest to the international industry and associated organizations.

Material and methods

Material

Cones were sampled in seven adult stone pine plantations located in an area extending between Valparaiso and Araucanía regions in Chile. The location of the plantations is presented in Table 1, along with general climatic characteristics (average climatic values during the 2010–2020 period). Table 2 presents a description of stands, including dendrometric variables.

thumbnail
Table 1. Characterization of the studied stone pine plantations.

https://doi.org/10.1371/journal.pone.0300008.t001

thumbnail
Table 2. Characterization of stone pine plantations (2020).

https://doi.org/10.1371/journal.pone.0300008.t002

All plantations were longitudinally sampled to obtain 10 cones per stand in winter during the 2010–2020 period, except for 2016. A hierarchical random sampling was used to select 10 trees and one healthy 3-year-old cone per tree. Therefore, 10 cones were randomly harvested per plantation each year. In some years the number of collected cones was lower than 10 due to harvesting complexities. Fresh weight of cones was immediately recorded, as previously indicated [24, 25].

Each year, the harvested cones were processed at INFOR’s laboratory to extract seeds (in-shell pine nuts) and pine nuts (kernels); in total, 560 cones were harvested throughout the study period. Seed number per cone was counted and seed and pine nuts were weighed and measured in the laboratory using the procedures indicted in Table 3. Cone to seed yield, seed to pine nut and PY were calculated using the formulas presented in Table 3. Empty and damaged seeds were also quantified to monitor cone health. We measured all in-shell seeds and shelled pine nuts per cone; for the 2010–2015 period, we only have aggregated data. Between 2017 and 2020, we counted and weighed all the in-shell and shelled pine nuts per cone, and measured a random sample of 20 in-shell and shelled pine nuts per cone for all traits [26].

Statistical analyses

First, cone weight categories were defined as the extreme tertiles of cone weight probabilistic distribution using all harvested cones across plantations and years (n = 560). Then, mean differences in morphometric traits, percentage of damaged seeds and PY were compared between cone weight categories (light and heavy cones) using a multi-environment ANOVA mixed model (α = 0.05), including cone weight category and plantation as fixed effects, and year as well as the corresponding interactions as random effects [27]. The ANOVA mixed model was fitted with homogeneous and heterogeneous variances for each cone weight category. The Akaike information criteria (AIC) was used to select the best model (the heteroscedastic model).

In addition, regression tree (RT) analysis [28] was used to explain PY variability in traits of cones, seeds and pine nuts. Box plots were used to describe the distribution of the main variables affecting PY for each plantation, according to the literature [27]. Statistical analyses were performed using the software InfoStat [29] and its interface with R (www.r-project.org).

Results

Across plantations and years, cone weight was on average 470 g, with an average PY of 3.91%. No significant interaction between cone weight category and plantation (p>0.05) was found for any trait. Weight of light and heavy cones was below 393 g and above 503 g, respectively. Compared with the light cone category, heavy cones had a 40.0% higher number of seeds per cone (125 vs 89 seeds per cone, respectively), 26.3% higher seed weight, larger seeds (13.3% and 9.5% greater length and width, respectively) and 5.8% higher cone to seed yield. The heavy cone weight category also had a 48.4% higher number of pine nuts per cone (112.9 vs 76.1 pine nuts per cone, respectively), 22.2% higher pine nut weight, 13.0% longer pine nuts, 4.1% wider pine nuts, 11.9% higher PY (4.05 vs 3.62%) and a lower number of damaged seeds (9.0% vs 15.9% damaged seeds) (Table 4).

thumbnail
Table 4. Stone pine fruit traits by cone weight category.

https://doi.org/10.1371/journal.pone.0300008.t004

Regression tree analysis showed that PY was influenced by pine nut number per cone. PY decreased by 35.6% when cones contained fewer than 78.5 units; PY averages were 4.04% and 2.60% below and above that threshold, respectively. Cones with fewer than 45.5 units showed a further yield reduction of 39.4%. Cones containing 78.5 pine nuts or more, and that had a seed to pine nut yield above 21.0% had a higher PY (4.54% vs 3.64%) than cones that had a seed to pine nut yield below 21.0%. Moreover, in cones containing 78.5 pine nuts or more, and seed to pine nut yield above 21.0%, PY further increased when cone to seed yield was above 20.8% (5.10% vs 4.35%) (Fig 1).

thumbnail
Fig 1. Fruit variables that best explained cone to pine nut yield in Chile (light and heavy cone weight categories).

PY data were first split into two subsets based on the predictor variable (PN) and its threshold (78.5). Each subset, or node, was then analyzed independently using the same procedure. Variables forming top nodes are the most important to explain PY. Average PY values for each node are reported in the embedded table. PN: pine nuts per cone; SPY: seed to pine nut yield; SY: cone to seed yield.

https://doi.org/10.1371/journal.pone.0300008.g001

The distribution of variables related to PY for all sites is presented at the seed or pine nut level in Fig 2 for the 2017–2020 period.

thumbnail
Fig 2. Distribution of cone and pine nut weight, and number of healthy pine nuts and damaged seeds per cone for each sample site in the 2017–2020 period.

https://doi.org/10.1371/journal.pone.0300008.g002

Discussion

Size is an important quality attribute in most fruit crops [30]. In stone pine, the relationship between cone quality and cone weight was analyzed; size and cone weight were found to be statistically correlated and cone weight was also correlated with seed and pine nut weight [23, 31]. Cone weight has shown to be affected by climatic conditions in the spring of the last year of cone maturation, especially by rainfall [32, 33], which partially explains the high inter-annual variability previously reported in the species’ native habitat [34]. In Chile [9], annual rainfall [35] and average temperature were also positively correlated with cone weight.

There is a known dependence of PY on number of pine nuts inside cones [20]; the relationship of PY and cone weight is becoming a cause of concern due to the effect of pests and diseases on seeds. In this study, cone weight was on average 470 g, which is higher than in the species’ native range [34, 3640]. In the 2010–2020 period in Chile, seed to pine nut yield was on average 20.3% across sites and years; these results are in line with values reported for the species’ native range [36, 4143]. Similarly, average PY value (3.9%) is also within the range of historical values reported for Italy (3.6% [40, 44] and Spain (2.7–4.4% [45]). However, the average PY value is higher than values reported for Europe after the arrival of L. occidentalis, which caused a drastic decrease in Spain (1.1–2.1% [31, 45]) and Portugal (1.7% [46]; 3.0% [44]). In Chile, a trend towards PY decrease was also reported [23, 47].

Regarding cone weight, our results showed that the PY value of the heavy cone category was 11.9% higher than that of the light category across sites and years, in agreement with [36]. This result disagrees with findings that showed no effect of cone weight on PY in the species’ native habitat [32]. This PY increase in the heavy cone category is explained by the increase in the number of healthy pine nuts per cone (48.4%) and pine nut weight (22.2%), the most influential variables in determining PY [23]. The measured values of pine nuts per cone (cone filling) are higher than those reported for Turkey [48], Italy [49] and Portugal [22], leading to an increased production. The fact that PY is higher in heavier fruits has been reported for other crops [5052], with composed fruits, formed from one flower and containing several seeds, as occurs in stone pine.

In agreement with previous findings [53], the number of damaged seeds was significantly lower in the heavy cone than in the light cone weight category, which favors PY. The average proportion of damaged seeds (12.8%) is similar to that reported for Croatia [54] and lower than those reported for Tunisia (19.3% [55]), Spain (50% [56], 60% [57]) and Lebanon (60% [58]). However, the average number of damaged seeds was twice as high as the value reported for Chile in 2018 [59]. This difference could be due to damage by L. occidentalis and/or to the severe uninterrupted mega-drought that has affected the country since 2010, with rainfall deficits of up to 40% [60]. The distribution of PY-related variables for Quilvo, Cáhuil and Rosario–geographically close locations with a similar rainfall regime–is not enough to attribute the observed differences to the irrigation provided in Quilvo.

Cone weight is highly variable within plantations; therefore, the differences in PY between the heaviest and the lightest cones are not trivial. However, our results indicate that cone weight may be used as an indicator of stone pine cone quality, in agreement with [36, 61]. In fact, our study showed that bigger cones contain a higher number of seeds (unshelled pine nuts), higher yield and bigger pine nuts (shelled white pine nuts, the edible component), as previously reported [36].

This longitudinal multi-environment study showed the dependence of PY on cone weight; hence, management practices, such as fertilization and irrigation, could be used to boost production of heavy cones. In fact, previous studies reported that fertilization increased PY [61] and cone weight [62, 63]. The benefits of fertilization with both micronutrients [64, 65] and macronutrients [21, 38] have been studied in adult stone pine trees. On the other hand, irrigation has also been found to improve cone weight in stone pine [61, 66, 67]. Therefore, further studies targeting these and other management practices to increase cone weight should explore tools to boost PY, with the consequent economic benefits.

Conclusions

In Chile, stone pine cones were found to be heavier than in the species’ native habitat. PY depends on seed and pine nut morphometry and seed health. Heavy cones contained a higher (48.4%) number of healthy pine nuts, and higher cone to seed and seed to pine nut yields, and consequently 11.9% higher PY than light cones. Management practices that increase cone weight, such as fertilization and irrigation, are recommended to increase PY.

Acknowledgments

We thank Aldo Salinas and Mauricio Navarrete for the assistance with cone harvesting, and the plantation owners for allowing access to the material.

References

  1. 1. Evaristo I, Batista D, Correia I, Correia P, Costa R. Chemical profiling of Portuguese Pinus pinea L. nuts and comparative analysis with Pinus koraiensis Sieb. & Zucc. commercial kernels. Opt. Médit. 2013;105: 99.
  2. 2. INC. Statistical Review: Pine Nuts. Nutfruit. 2020;79: 82–82.
  3. 3. Cortés-Sánchez M, Morales-Muñiz A, Simón-Vallejo MD, Lozano-Francisco MC, Vera-Pelaez JL, Finlayson C, et al. Earliest Known Use of Marine Resources by Neanderthals. Lalueza-Fox C, editor. PLoS One. 2011;6: e24026. pmid:21935371
  4. 4. Guàrdia M, Teixidó A, Sanchez-Bragado R, Aletà N. An Agronomic Approach to Pine Nut Production by Grafting Stone Pine on Two Rootstocks. Agriculture. 2021;11: 1034.
  5. 5. Moricca C, Nigro L, Masci L, Pasta S, Cappella F, Spagnoli F, et al. Cultural landscape and plant use at the Phoenician site of Motya (Western Sicily, Italy) inferred from a disposal pit. Veg Hist Archaeobot. 2021;30: 815–829.
  6. 6. Zuleta A, Weisstaub A, Giacomino S, Dyner L, Loewe V, Del Río R, et al. An ancient crop revisited: Chemical composition of Mediterranean pine nuts grown in six countries. Ital J Food Sci. 2018;30: 170–183.
  7. 7. Holman G. Pine nut production in Australia. NSW Southern Tablelands, Australia; 2013. pp. 1–4. Available: https://nutindustry.org.au/LiteratureRetrieve.aspx?ID=168689
  8. 8. Vanhanen L, Savage G. Mineral analysis of Pine nuts (Pinus spp.) grown in New Zealand. Foods. 2013;2: 143–150. pmid:28239104
  9. 9. Loewe V, Balzarini M, Álvarez A, Delard C, Navarro-Cerrillo R. Fruit productivity of Stone pine (Pinus pinea L.) along a climatic gradient in Chile. Agric For Meteorol. 2016;223: 203–216.
  10. 10. Loewe-Muñoz V, Del Río R, Delard C, Balzarini M. Enhancing Pinus pinea cone production by grafting in a non-native habitat. New For. 2022;53: 37–55.
  11. 11. Montero G, Calama R, Ruiz R. Selvicoltura de Pinus pinea L. In: Montero G, Serrada R, Reque J, editors. Compendio de selvicoltura de especies. Madrid, España: INIA-Fundación Conde del Valle de Salazar; 2008. pp. 431–470.
  12. 12. Calama R, Mutke S, Tomé J, Gordo J, Montero G, Tomé M. Modelling spatial and temporal variability in a zero-inflated variable: the case of stone pine (Pinus pinea L.) cone production. Ecol Modell. 2011;222: 606–618.
  13. 13. Mutke S, Martínez J, Gordo J, Nicolas JL, Herrero N, Pastor A, et al. Severe seed yield loss in Mediterranean Stone pine cones. 5th International Conference on Mediterranean Pines (Medpine5) 22–26 September 2014. Solsona, Spain; 2014.
  14. 14. Nunes A, Pereira H, Tomé M, Silva J, Fontes L. Tomography as a method to study umbrella pine (Pinus pinea) cones and nuts. For Syst. 2016;25: 1–5.
  15. 15. Mutke S, Calama R, Montero G, Gordo J. Pine nut production from forests and agroforestry systems around the Mediterranean—a short overview. European Non-Wood Forest Products 3rd Workshop 18–20 February 2015. Zagreb, Croatia; 2015.
  16. 16. Sousa E, Ferreira C, Pimpão M, Naves P, Valdiviesso T. Sanidade dos povoamentos de pinheiro manso em Portugal. Seminario “Valorizaçao da Fileira da Pihna/Pinhao” September 18, 2012. Alcácer do Sal, Portugal: UNAC; 2012.
  17. 17. Özden S, Okan T, Buğday SE, Köse C. Perspectives of Farmers on the Decline in Pinus pinea Nut Yield and the Sustainability of the Production: A Case Study in Kozak Basin in Western Turkey. Agriculture. 2022;12: 1070.
  18. 18. Calama R, Mutke S. Impact of Leptoglossus occidentalis on commercial pine nut kernel per cone output In: Incredible Innovation Network for Cork, Resin & Edibles, Factsheet 20770 [Internet]. 2020 [cited 16 Aug 2021]. Available: https://oppla.eu/casestudy/20770
  19. 19. Faúndez E, Rocca J, Villablanca J. Detection of the invasive western conifer seed bug Leptoglossus occidentalis Heidemann, 1910 (Heteroptera: Coreidae: Coreinae) in Chile. Arq Entomolóxicos. 2017;17: 317–320.
  20. 20. Kun M, Masciocchi M. First detection of the cosmopolitan invader Leptoglossus occidentalis Heidemann (Heteroptera: Coreidae) in Argentina. An Acad Bras Ciencias. 2019;91: e20180493.
  21. 21. Calama R, Madrigal G, Candela J, Montero G. Effects of fertilization on the production of an edible forest fruit: stone pine (Pinus pinea L.) nuts in the south-west of Andalusia. For Syst. 2007;16: 241–252.
  22. 22. Afonso A, Gonçalves AC, Pereira DG. Pinus pinea (L.) nut and kernel productivity in relation to cone, tree and stand characteristics. Agrofor Syst. 2020;94: 2065–2079.
  23. 23. Loewe-Muñoz V, Balzarini M, Delard C, Álvarez A. Variability of stone pine (Pinus pinea L.) fruit traits impacting pine nut yield. Ann For Sci. 2019;76: 37.
  24. 24. Calama R, Montero G. Cone and seed production from Stone pine (Pinus pinea L.) stands in Central Range (Spain). Eur J For Res. 2007;126: 23–35.
  25. 25. Mutke S, Calama R, Gordo J, Nicolas J, Herrero N, Roques A. Pérdida del rendimiento en piñón blanco de Pinus pinea en fábrica, Leptoglossus y la seca de la piña. III Reunión Científica de Sanidad Forestal. Madrid, Spain; 2015.
  26. 26. Verónica L-M, Rodrigo del R, Mónica B. Pinus pinea cone, seed and pine nut morphometry and health. 2023 [cited 23 Aug 2023].
  27. 27. West B, Welch K, Galecki A. Linear mixed models: a practical guide using statistical software. 2nd ed. New York, US: CRC Press; 2014.
  28. 28. Breiman L. Classification and Regression Trees. Chapman and Hall/CRC; 1998. Available: https://books.google.cl/books?id=r0pwxgEACAAJ
  29. 29. Di Rienzo J, Casanoves F, Balzarini M, Gonzalez L, Tablada M, Robledo C. InfoStat. 2023. Available: http://www.infostat.com.ar
  30. 30. Hussain Q, Shi J, Scheben A, Zhan J, Wang X, Liu G, et al. Genetic and signaling pathways of dry fruit size: targets for genome editing‐based crop improvement. Plant Biotechnol J. 2020;18: 1124–1140. pmid:31850661
  31. 31. Calama R, Gordo J, Conde M, Madrigal G, Mutke S, Pardos M, et al. Rendimiento de piñón en piña de Pinus pinea en Portugal: caracterización y comparación con otras regiones. Seminario UNAC “Avanços no conhecimento na fileira do Pinheiro manso” Marzo 2015. Alcácer do Sal, Portugal: UNAC; 2015.
  32. 32. Santos S. Pine cone weight interannual variation. In: Incredible Innovation Network for Cork, Resin & Edibles, Factsheet 20826 [Internet]. 2020 [cited 16 Aug 2021]. Available: https://repository.incredibleforest.net/oppla-factsheet/20826
  33. 33. Gonçalves A, Afonso A, Pereira D, Pinheiro A. Influence of umbrella pine (Pinus pinea L.) stand type and tree characteristics on cone production. Agrofor Syst. 2017;91: 1019–1030.
  34. 34. Schneider R, Calama R, Martin-Ducup O. Understanding Tree-to-Tree Variations in Stone Pine (Pinus pinea L.) Cone Production Using Terrestrial Laser Scanner. Remote Sens. 2020;12: 173.
  35. 35. Balekoglu S, Caliskan S, Dirik H. Effects of geoclimatic factors on the variability in Pinus pinea cone, seed, and seedling traits in Turkey native habitats. Ecol Process. 2020;9: 55.
  36. 36. Gordo J, Calama R, Pardos M, Montero G, Bravo F. La regeneración natural de los pinares en los arenales de la Meseta Castellana. Gordo J, Calama R, Pardos M, Bravo F, Montero G, editors. Valladolid, España: Instituto Universitario de Investigación en Gestión Forestal Sostenible, Universidad de Valladolid, INIA; 2012. Available: http://www.pfcyl.es/sites/default/files/biblioteca/regeneracion_pinares.pdf
  37. 37. Jaouadi W, Alsubeie M, Mechergui K, Naghmouchi S. Silviculture of Pinus Pinea L. in North Africa and The Mediterranean Areas: Current Potentiality and Economic Value. J Sustain For. 2021;40: 656–674.
  38. 38. Kilci M, Akbin G, Sayman M, Özçankaya M. Determination of effect of fertilizing on cone yield of stone pine (Pinus pinea L.) in Kozak province. 2013. Report No.: 52.
  39. 39. Gonçalves AC, Pommerening A. Spatial dynamics of cone production in Mediterranean climates: A case study of Pinus pinea L. in Portugal. For Ecol Manage. 2012;266: 83–93.
  40. 40. Peruzzi A, Cherubini P, Gorreri L, Cavalli S. Le pinete e la produzione dei pinoli dal passato ai giorni nostri, nel territorio del parco di Migliarino, S. Rossore, Massaciuccoli. Pisa: Ento Parco Regionale Migliarino, San Rossore, Massaciuccoli; 1998.
  41. 41. Silveira P. Pinha ou Pinhão negro, rentabilidade e resultados. Seminario “Valorizaçao da Fileira da Pihna/Pinhao” September 18, 2012. Alcácer do Sal, Portugal: UNAC; 2012.
  42. 42. Cabannes B. Le pin pignon, une opportunité pour la forêt provençale. Forêt Méditerranéenne. 2015;36: 37–48.
  43. 43. Vacas De Carvalho A. Algumas considerações sobre o pinheiro manso, na regüio de Alcácer do Sal. Actas de la Reunión sobre selvicultura, mejora y producción de Pinus pinea. Madrid, Spain: I.N.I.A. & Comisión; 1989.
  44. 44. Santos C. Innovation Annual press release on pine cone quality. In: Incredible Innovation Network for Cork, Resin & Edibles, Factsheet 20844 [Internet]. 2020 [cited 20 Oct 2022]. Available: https://repository.incredibleforest.net/oppla-factsheet/20844
  45. 45. Calama R, Gordo J, Conde M, Madrigal G, Mutke S, Pardos M, et al. Pérdidas de rendimiento de piña y piñón en las masas de Pinus pinea. Jornada Presentación Proyecto PROPINEA Pedrajas de San Esteban 21 Noviembre 2014. España; 2014.
  46. 46. Evaristo I, Tenreiro R, Costa R. Caracterização de Parâmetros Biométricos e de Ácidos Gordos em Pinhões de Populações Portuguesas de Pinus pinea L. Silva Lusit. 2008;16: 1–19. Available: http://www.scielo.mec.pt/pdf/slu/v16n1/v16n1a01.pdf
  47. 47. Loewe-Muñoz V, del Río R, Delard C, Balzarini M. Effect of fertilization on Pinus pinea cone to seed and kernel yields. For Ecol Manage. 2023;545: 121249.
  48. 48. Bilir N. Cone, seed and nut characters in Pinus pinea. International Convention Center “Seed orchards and the link to long-term tree breeding in response to climate change” 8-11/9/2009. Jeju, Korea: IUFRO; 2009.
  49. 49. Ciancio O, Cutini A, Mercurio R, Veracini A. Un modèle sylvicole pour la conservation et l’amélioration de la pinède de Pin pignon d’Alberese (Toscane-Italie). Forêt Méditerranéenne. 1990;12: 131–142.
  50. 50. Otegui M, Nicolini M, Ruiz R, Dodds P. Sowing date effects on grain yield components for different maize genotypes. Agron J. 1995;87: 29–33.
  51. 51. García A, Dorado M, Pérez I, Montilla E. Efecto del déficit hídrico sobre la distribución de fotoasimilados en plantas de arroz (Orysa sativa L.). Interciencia. 2010;35.
  52. 52. Phakamas N, Patanothai A, Jogloy S, Pannangpetch K, Hoogenboom G. Physiological Determinants for Pod Yield of Peanut Lines. Crop Sci. 2008;48: 2351–2360.
  53. 53. Calama R, Gordo J, Mutke S, Conde M, Madrigal G, Garriga E, et al. Decline in commercial pine nut and kernel yield in Mediterranean stone pine (Pinus pinea L.) in Spain. iForest—Biogeosciences For. 2020;13: 251–260.
  54. 54. Jakovljevic T, Gredecki-Postenjak M, Radojcic IR. Stone pine seeds (Pinus pinea L.), forest reproductive material and food. Hrvat Sumar Inst. 2009;44: 29–36.
  55. 55. Boutheina A, Hedi El Aouni M, Balandier P. Influence of stand and tree attributes and silviculture on cone and seed productions in forests of Pinus pinea L., in northern Tunisia. Options Medit. 2013;105: 9–14.
  56. 56. Mutke S, Roque A, Calama R. Impact of the dry cone syndrome on kernel yield from Stone pine cones. In: Mutke, S.; Correia, A.C.; Vila Verde C, editor. AgroPine2016 2nd International Meeting on Mediterranean Stone Pine for Agroforestry 2016/05/18-20. Oeiras, Portugal: INIAV; 2016.
  57. 57. Calama R, Gordo J, Mutke S, Madrigal G, Conde M, Raposo R, et al. Variabilidad espacio-temporal en el daño asociado a Leptoglossus occidentalis en pinares de Pinus pinea de la provincia de Valladolid. 7° Spanish Forest Congress 26–30 June 2017. Plasencia, Spain: Sociedad Española de Ciencias Forestales; 2017. p. 8.
  58. 58. El Khoury Y, Noujeim E, Bubici G, Tarasco E, Al Khoury C, Nemer N. Potential Factors behind the Decline of Pinus pinea Nut Production in Mediterranean Pine Forests. Forests. 2021;12: 1167.
  59. 59. Loewe V, Álvarez A, Navarro-Cerrillo R. Morphometric and chemical fruit variability of selected stone pine trees (Pinus pinea L.) grown in non-native environments. Plant Biosyst. 2018;152: 547–555.
  60. 60. Garreaud RD, Boisier JP, Rondanelli R, Montecinos A, Sepúlveda HH, Veloso-Aguila D. The Central Chile Mega Drought (2010–2018): A climate dynamics perspective. Int J Climatol. 2020;40: 421–439.
  61. 61. Freire J, Rodrigues G, Tomé M. Climate Change Impacts on Pinus pinea L. Silvicultural System for Cone Production and Ways to Contour Those Impacts: A review Complemented with Data from Permanent Plots. Forest. 2019;10: 169.
  62. 62. Kilci M, Akbin G, Sayman M, Özçankaya M. Determination of effect of fertilizing on cone yield rate of stone pine (Pinus pinea L.) in Kozak province, Turkey. In: Mutke S, Correia A, Vila Verde C, editors. 2nd International Meeting on Mediterranean Stone Pine for Agroforestry 18–20 May. Oeiras, Portugal; 2016. p. 75.
  63. 63. Loewe-Muñoz V, Delard C, Del Río R, Balzarini M. Long-term effect of fertilization on stone pine growth and cone production. Ann For Sci. 2020;77: 69.
  64. 64. Bento J, Coutinho J. Boron deficiency in Stone pine. Agropine 2011 International Meeting on Mediterranean Stone pine for Agroforestry 2011/11/17-19. Valladolid, Spain; 2011. p. 25.
  65. 65. Malchi T, Shenker M. Iron Deficiency of Pinus pinea: Evaluation of Iron Uptake Mechanisms and Comparison of Different Genetic Lines. Jerusalem, Israel; 2011.
  66. 66. Lobell D, Ortiz-Monasterio J, Asner G, Naylor R, Falcon W. Combining field surveys, remote sensing and regression trees to understand yield variations in an irrigated wheat landscape. Agron J. 2005;97: 241–249.
  67. 67. Bono D, Aleta N. Cone yield evaluation of a grafted Pinus pinea L. trial. International Meeting on Mediterranean Stone pine for Agroforestry 2011/11/17-19. Valladolid, Spain: AgroPine 2011; 2011. p. 16.