Next Article in Journal
A Qualitative Study on the Consumer Behaviour Related to Food Waste: Romanian Perspectives through Word Cloud and Sentiment Analysis
Previous Article in Journal
Assessing Traffic Characteristics for Safe Pedestrian Crossings: Developing Warrants for Sustainable Urban Safety
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 16
Issue 10
10.3390/su16104191
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Unveiling Peru’s Agricultural Diversity: Navigating Historical and Future Trends in a Post-COVID-19 Context

by
Segundo G. Chavez
1,
Erick Arellanos
2,
Jaris Veneros
1,
Nilton B. Rojas-Briceño
3,
Manuel Oliva-Cruz
1,
Carlos Bolaños-Carriel
4 and
Ligia García
1,5,*
1
Instituto de Investigación para el Desarrollo Sustentable de Ceja de Selva (INDES-CES), Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, 342 Higos Urco, Chachapoyas 01001, Peru
2
Instituto de Investigación en Ingeniería Ambiental (INAM), Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, 342 Higos Urco, Chachapoyas 01001, Peru
3
Grupo de Investigación en Ciencia de la Información Geoespacial, Escuela Profesional de Ingeniería Ambiental, Facultad de Ingeniería y Arquitectura, Universidad Nacional de Moquegua, Pacocha 18610, Peru
4
Facultad de Ciencias Agrícolas, Universidad Central del Ecuador, Av. Universitaria, Quito 170129, Ecuador
5
Facultad de Ingeniería Zootecnista, Agronegocios y Biotecnología, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, 342 Higos Urco, Chachapoyas 01001, Peru
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(10), 4191; https://doi.org/10.3390/su16104191
Submission received: 27 March 2024 / Revised: 13 May 2024 / Accepted: 14 May 2024 / Published: 16 May 2024
Browse Figures
Versions Notes

Abstract

:
Over a comprehensive 5-year assessment, and extrapolating it prospectively until 2025, a thorough examination was conducted of productive agrobiodiversity in nine rural agricultural districts across Peru. The present study involved in-depth interviews with 180 representative farmers of the Coast, Highlands, and Jungle natural regions. Employing the Shannon–Weiner diversity index and the Margalef species richness index, the dynamics within years and across different zones were analyzed. Utilizing quadratic trend models, we assessed the frequency of each crop, aiming for the optimal fit concerning absolute deviation from the mean, mean squared deviation, and mean absolute percentage error. These findings revealed five distinct crop types—tuberous, fruits, cereals, legumes, and roots—distributed across 25 diverse families. Looking ahead to 2025, our projections indicated positive trends in 15 families and negative trends in 9 crop families. The nuanced mathematical distinctions observed in crop management decisions varied significantly depending on the specific area and year, underscoring the importance of localized considerations in agricultural planning.

1. Introduction

Factors such as global food demand, scarce cereal stocks, and climate change are emerging challenges to be confronted for the stability of food systems at the national and global levels [1,2,3]. Likewise, hunger has increased in Latin America and the Caribbean [4] and threatened a massive resurgence amid the coronavirus/COVID-19 pandemic [5,6,7]. Peru has a commodity-based economy in which agriculture plays an essential role in the nation’s development [8,9].
Various critical factors, including increased global food demand and the pervasive impacts of climate change, pose substantial threats to the stability of food systems both nationally and globally [2,3]. The escalation of hunger in Latin America and the Caribbean [4] has been exacerbated amid the challenges posed by the COVID-19 pandemic [7]. Peru, with its economy heavily reliant on commodities, underscores the pivotal role of agriculture in national development [9]. Species diversity, defined as the variability of living organisms in ecosystems [10,11,12], has long been acknowledged for its crucial role in ecosystem functioning [13,14]. Particularly in spaces dedicated to food cultivation, the correlation between biodiversity and nutritional security becomes evident [7,15]. The alarming decline in agricultural biodiversity on a global scale underscores the urgent need for the development of multifunctional and sustainable agriculture [4,16]. Recognizing the significance of productive agricultural biodiversity, it becomes imperative to make informed decisions for conservation and cultivation. Numerous indices have been proposed to measure species diversity and richness [17,18]. The Shannon Diversity Index was primarily used to identify species richness and diversity. In 1958, Margalef popularized the concept of species diversity among the scientific community [19]. Assessments of species uniformity, diversity, and richness have been instructive for future research in various forest ecosystems at spatial scales [20,21].
Agriculture is the second largest economic sector in Peru after mining [22] and faces numerous challenges. Now, it is known that Peru’s agricultural industry has experienced remarkable growth in recent decades due to consumer demand for healthier, fresher, and more convenient food products [9]. Nutrition, food systems, and food biodiversity in smallholder farmers are being transformed in the Andean countries, despite urgent concerns for food sovereignty, uneven geographic development, and climate change [7].
The role of Peruvian rural agriculture in supporting employment, ancillary businesses, and environmental services remains largely unknown. Understanding the prospective planting of specific crops is crucial for effective agricultural production planning, facilitating inventory control of agricultural commodities, and optimizing the allocation and conservation of natural resources. Regrettably, detailed information regarding the future planting of crops is generally scarce [23]. Therefore, this research aims to document the historical facets of Peru’s productive agricultural diversity, forecasting future trends, and elucidating their implications for food security. This study will provide insights that will enhance agricultural management in Peru. Consequently, the historical variability of agricultural crops across the three natural regions of Peru was identified, calculating indices of agricultural diversity and trends. Models are being presented for projecting crop types and families, offering valuable guidance for Peruvian farmers in optimizing their agricultural practices.

2. Materials and Methods

2.1. Location of the Study

Peru is a South American country located on the Pacific coast (0°02′, 18°20′ south and 68°30′, 81°25′ west) and has an area of 1,285,215 km2 [24]; due to the variability of climate change, each zone of the country has very specific and different characteristics [24].
A total of 180 rural farmers located in the different districts of the three natural regions of Peru were randomly identified: from the coast (Sapillica, Guadalupe, and Atico), the highlands (Lucanas, Lonyachico, and Sapillica), and the jungle (Villa Rica, Jepelacio, and Santa Rosa). Each site was georeferenced according to map 1 and represents the productive agricultural diversity of Peru (Figure 1).

2.2. Historical Variability of Agricultural Crops

Data were collected through semi-structured interviews with 180 farmers. We recorded the values of crops planted each year, and then we registered the scientific name in the different databases according to procedures already established in Excel [25].

2.3. Index of Agricultural Diversity and Trends for 9 Districts of the Three Natural Regions of Peru

The present study identified the concentration of species among the productive agricultural diversity in nine districts of the three natural regions of Peru. Five-year data (2018–2022) was collected for two indices of ecological indicators and their trends were plotted. The Shannon–Weiner index (H’) (to assess species diversity) and the Margalef index (SR) (to assess species richness) were evaluated [10].
The Shannon–Weiner index (H’) was determined with the following formula [26]:
H / = ∑ i = 1 S p i l n p i
where
  • H/ = Shannon–Weiner index
  • pi = Proportion of individuals belonging to species i
  • ln = natural logarithm
The Margalef index (SR) was determined with the formula [27]:
S R = S − 1 ln ⁡ ( N )
where
  • SR = Margalef Species Richness Index
  • S = Number of species
  • N = Total number of individuals

2.4. Current Models for Future Trends in Agricultural Species, According to Crop Type and Crop Families

To generate the graphs for annual trend values, as well as the model formulas, the quadratic model was chosen in all cases because it has the greatest fit in the absolute deviation of the mean (MAD), the mean square deviation (MSD), and the mean absolute percentage error (MAPE) [28].

3. Results

The presence of 47 crops was recorded for 5 years (2018–2022) in the nine districts of the three natural regions of Peru. When we divided the crops according to type, 13.51% were cereals, 13.51% were legumes, and fruits corresponded to 51.35% of the total types of crops, registering as the highest percentage. As for the roots, they were registered as 24.32% of the total, the tuberous with 2.70% and the vegetables correspond to 21.62% with respect to the type of crop (Table 1).
The crops (with common names and their respective scientific names) were matched in 25 plant families that showed the diversity of Peruvian foods. The case of corn and coffee leads in frequency with respect to their presence in all locations and years recorded. The frequencies with which the farmers registered their crops the most in 2018 were corn (61 farmers) and coffee (60 farmers). For 2019, they were also corn and coffee (with 62 farmers), followed by beans (33 farmers). For 2020, the frequency of corn was 61 and coffee was 55, while for 2021 these crops reached frequencies of 65 and 61 farmers, respectively (Table 1).
The trend in the average number of species grown by farmers in the districts is represented (Table 2). In the coastal region, and for all years (2018–2022), the highest values were presented in the district of Paccho, with up to 3.9 crops (2018); however, this district presents a variability of −12.82%. In Atico, the trend is increasing (24.53%), as well as in Guadeloupe 6.45%, while in the highland region, the highest values for average number of crops per farmer are in Lonya Chico with up to 4.75 average crops in 2018. However, this district shows a reduction in crop variability (−5.26%), as well as in Sapillica with up to a −27.66% reduction. On the contrary, in Lucanas, there was greater variability with 7.25%. For the jungle region, in Jepelacio, there was the highest average for the number of crops per farmer (3 in 2018), despite the fact that the variability trend is reduced by −11.67%. In Santa Rosa, the value of 1.65 crops was maintained and in Villa Rica, there was a trend of a 4.76% increase.
If analyzed from various approaches (context according to natural regions, according to districts, even according to the post-COVID-19 pandemic impact), Table 2 shows the great dynamics existing in the indicators of agrobiodiversity, which can be exploited by farmers, and the great need to manage the conservation of current crops. Furthermore, the results reinforce the need to join efforts to avoid the reduction in the average number of crops per farmer, which currently varies by −2.87% at a general level.

3.1. Indices of Agricultural Diversity and Trends for 9 Districts of the Three Natural Regions of Peru

From the Shannon index, the maximum number of species was recorded along with their uniform distribution; on the coast, it was the district of Guadalupe (H/ =2,94) in 2018 and Paccho in the years 2019–2022 (H/ =2.94 and H/ = 2.95, H/ = 2.96, respectively). In the highland region, the highest Shannon index was presented in Lonya Chico for the year 2018; Lucanas in the years 2019, 2021, and 2022 (H/ = 2.92, H/ = 2.09, H/ = 2.95, and H/ = 2.93, in the given order); and Sapillica in 2020 (H/ = 2.90). Likewise, from the Shannon index, the maximum number of species was recorded along with their uniform distribution for the jungle region, and the indices with the highest values were in the district of Villa Rica (H/ = 2.98) in 2018, 2019 (H/ = 2.99), and 2020 (H/ = 2.99). This analysis resulted in lower values for diversity indices for the district of Santa Rosa in all years (2018–2022) due to the consideration of both the number of species recorded and their relative abundance in the forest (Table 3).
Regarding the value of the Margalef species richness index, it is directly related to the number of species present in each district. Thus, for the coastal region, the highest values were recorded in Guadalupe in the 5 years (SR 2018= 5.53, SR 2019= 5.48, SR 2020 = 5.43, SR 2021 = 5.43, and SR 2022 = 5.43). In the highland region, the highest SR was in all years for Sapillica (SR 2018 = 4.93, SR 2019 = 5.02, SR 2020 = 5.30, SR 2021 = 5.30, and SR 2022 = 5.38); and in the jungle, for the 5-year study, the highest index values were presented in Villarica (SR 2018 = 6.24, SR 2019 = 6.34, SR 2020 = 6.34, SR 2021 = 6.24, and SR 2022 = 6.14). The lowest value with respect to the total corresponded to Lonya Chico (highland region, 2022) with an SR index of 4.

3.2. Models of Historical and Future Trends for Productive Agricultural Diversity at the Level of Crop Type and Family

The annual frequency (2018–2022) on average was represented in trend models for the different types of crops. In all cases, the best fit (according to MAPE, MAD, and MSD) was presented using the quadratic trend model. MAD values showed values of 0.27 in tuberoses, 0.07 in fruits, 0.09 in cereals, 0.29 in legumes, and 0.30 in roots. Since tuberous are the lowest value, it means that they had the best fit for crop types. For all crop types, the model forecasts a positive upward trend (Figure 2). Regarding the ASM for crop type, the forecast for roots is wrong by 6.80%, this being the highest value of error identified. The rest of the ASM values for crop types are between 2.12% (legumes) and 0.46% (cereals). For the MSD measure, the accuracy of the adjusted values of the time series ranges from 0.07 in fruits (highest fit) to 0.27 in tuberoses, representing the lowest fit (Figure 2).
The historical trend and projection models to 2025 for productive agricultural diversity, according to crop family, are presented in Figure 3 and Figure 4. A total of 25 plant families were identified in the three natural regions, corresponding to nine districts of Peru. In this sense, the MAPE, MAD, and MSD values had the best fit for the quadratic trend model in all cases. For the year 2025, the projections in a positive trend correspond to the families of the poaceae, rutaceae, rosaceae, solanaceae, tropaeolaceae, malvaceae, lythraceae, moraceae, lauraceae, fabaceae, euphorbiaceae, convolvulaceae, brassicaceae, asteraceae, and amarillydaceae. On the other hand, the projections in a negative trend correspond to the families of passifloraceae, rubiaceae, olaceae, oxalidaceae, musaceae, cucurbitaceae, basellaceae, apiaceae, and amarantaceae.

4. Discussion

Agrobiodiversity is underutilized in national food systems; although this is critical for healthy agro-ecosystems [29]. The present study focused on investigating the historical variability of agricultural crops in the three natural regions of Peru; from these crops, the indices of agricultural diversity and trends were obtained, to finally present models for the projection of crop types and crop families for the Peruvian farmer. The diversity of crops is very dynamic between years and areas. Genetic diversity is not only necessary to maintain among species [30] but it is also responsible for the diversity of food, medicines, and fibers available to humans in ecosystems. Therefore, it is essential to adopt differentiated approaches to the conservation and promotion of agrobiodiversity in local contexts. Natural and modified ecosystems provide a multitude of functions and services that contribute to human well-being [2]. It has long been recognized that biodiversity plays an important role in the functioning of ecosystems [31,32]. It is proposed to use crop-specific planting frequency data as indicators to provide indirect information on the planting of future crops [23]. While later studies suggest that a few dominant species can provide most ecosystem services [26], the case of productive agricultural diversity requires wealth and abundance. Therefore, they are dependent on many complementary species to provide ecosystem services. Changes in the response of ecosystem services to biodiversity can operate in combination [33]. Depending on the type of crop, farmers assess the yield of marketable crops on acreage and also on the basis of the weight of the fruits or seeds [9].
The Peruvian economy has grown at a dizzying pace in recent decades. Peru’s GDP has more than tripled, from $60 billion in 1990 to $215 billion in 2019 [34]. In line with economic growth, Peru is facing a higher consumption of food, so the data from this research show an alert for conservation and the search for new sources to strengthen food security in the countryside and the city [9]. Smallholder farmers are the most important custodians of plant genetic resources for in situ conservation. Despite this, the complexity of rural agricultural Peru incorporates the conditions of poverty and development in a geographical context, which are combined with the transformation of food systems and climate change [7]. The retrospective diversity values in this study will allow us to relate to the availability of food in each area. Likewise, prospective trends will allow us to look for strategies to anticipate this lack of future food in terms of quantity and availability for local consumption. It is, therefore, reaffirmed that the emerging capacities of agrobiodiversity actively provide a partial degree of food sovereignty [35].
This dynamic of productive agricultural diversity can be attributed to various factors such as the adoption of agricultural practices and sustainable agricultural developments. The literature reports that biologically diverse communities are also more likely to contain species that confer resilience to that ecosystem because, as a community accumulates species, there is a greater likelihood that any of them will have traits that allow them to adapt to a changing environment [36,37]. Such species could buffer the system against the loss of other species [38]. These findings highlight the importance of promoting agrobiodiversity conservation and management strategies to ensure food security and sustainable development in Peru.
Studies have documented the effects of COVID-19 on public health, as measures to contain the disease pose significant risks to food and nutrition security due to declines in food production, distribution, and access [39,40,41]. This phenomenon primarily affected low-income families in poorer areas of Lima and the main cities. Faced with the situation, they had to migrate back to their centers of origin in the hope of finding better conditions [42,43,44]. Reverse migration can have both positive and negative effects on agrobiodiversity [45,46]. On the one hand, it can promote the revitalization of traditional agricultural practices and the use of local varieties, which contributes to the conservation of agrobiodiversity. Also, the pandemic offers opportunities to rethink the whole aspect of migration, and, using the innate or acquired skills of returning migrants, outstanding problems in the rural sector can be tried to solve [47,48,49]. On the other hand, reverse migration has wide-ranging direct and indirect effects on biodiversity loss and ecosystem health. Due to financial, cultural, and many other factors, people engage in activities that promote deforestation and wildlife trade to support their livelihoods. Certain policy actions, such as subsidies to extractive, agricultural, and development industries, can generate rapid economic growth, but they can also exacerbate land use changes, biodiversity loss, greenhouse gas emissions, and unsustainable agricultural intensification, all of which can create conditions for future emerging diseases [50]. Therefore, it is also necessary to implement appropriate management strategies and policies that promote the sustainability of agrobiodiversity in the context of reverse migration.
The diversity indices of 47 crops recorded in rural areas of Peru are grouped into types of crops that are part of the country’s food security [51], as well as crops of economic importance such as coffee and cacao. This research will allow for decisions to be made to prioritize and zone territories with aptitudes for these crops, as has been performed for potatoes [52], coffee [53], and cocoa [54]; there have even been studies of the potential distribution of crop species of medicinal importance in the country [25] and of the floral resources for bees in rural areas [55].

5. Conclusions

In conclusion, this study reveals significant changes in the diversity of agricultural species cultivated in different districts of Peru during the period from 2018 to 2022, influenced by the phenomenon of reverse migration caused by the COVID-19 pandemic.
These results highlight the importance of adopting differentiated approaches for the conservation and promotion of agrobiodiversity in local contexts, as well as implementing appropriate management strategies and policies to ensure the sustainability of agrobiodiversity in the context of reverse migration. Future research could deepen the analysis of the drivers of these changes and assess the impact of agrobiodiversity conservation policies and programs in the context of reverse migration.

Author Contributions

Conceptualization, S.G.C.; methodology, E.A.; software, J.V.; validation, N.B.R.-B.; formal analysis, L.G.; investigation, C.B.-C.; resources, M.O.-C.; data curation, N.B.R.-B.; writing—original draft preparation, S.G.C. and L.G.; writing—review and editing, J.V.; visualization, N.B.R.-B.; supervision, M.O.-C.; project administration, L.G.; funding acquisition, L.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by PROCIENCIA, grant number CONTRATO N° 075-2021-PROCIENCIA, and the APC was funded by Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original contributions presented in the study are included in the article.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Koning, N.B.J.; Van Ittersum, M.K.; Becx, G.A.; Van Boekel, M.A.J.S.; Brandenburg, W.A.; Van den Broek, J.A.; Goudriaan, J.; Van Hofwegen, G.; Jongeneel, R.A.; Schiere, J.B.; et al. Long-Term Global Availability of Food: Continued Abundance or New Scarcity. NJAS Wagening. J. Life Sci. 2008, 55, 229–292. [Google Scholar] [CrossRef]
  2. Charles, H.; Godfray, J.; Beddington, J.R.; Crute, I.R.; Haddad, L.; Lawrence, D.; Muir, J.F.; Pretty, J.; Robinson, S.; Thomas, S.M.; et al. Food Security: The Challenge of Feeding 9 Billion People. Science 2010, 327, 812–818. [Google Scholar]
  3. Wheeler, T.; Von Braun, J. Climate Change Impacts on Global Food Security. Science 2013, 341, 508–513. [Google Scholar] [CrossRef]
  4. FAO; IFAD; UNICEF; WFP; WHO. The State and Food Security and Nutrition in the World 2019. Safeguarding against Economic Slowdowns and Downturns; FAO: Rome, Italy, 2019; ISBN 9789251315705. [Google Scholar]
  5. Siche, R. What Is the Impact of COVID-19 Disease on Agriculture? Sci. Agropecu. 2020, 11, 3–9. [Google Scholar] [CrossRef]
  6. García, L.; Veneros, J.; Tineo, D. Severe Acute Respiratory Syndrome (SARS-CoV-2): A National Public Health Emergency and Its Impact on Food Security in Peru. Sci. Agropecu. 2020, 11, 241–245. [Google Scholar] [CrossRef]
  7. Zimmerer, K.S.; de Haan, S.; Jones, A.D.; Creed-Kanashiro, H.; Tello, M.; Amaya, F.P.; Carrasco, M.; Meza, K.; Tubbeh, R.M.; Nguyen, K.T.; et al. Indigenous Smallholder Struggles in Peru: Nutrition Security, Agrobiodiversity, and Food Sovereignty amid Transforming Global Systems and Climate Change. J. Lat. Am. Geogr. 2020, 19, 74–111. [Google Scholar] [CrossRef]
  8. Larson, D.F.; Varangis, P.; Yabuki, N.; Larson, Y. Commodity Risk Management and Development; SSRN 597214; Elsevier: Amsterdam, The Netherlands, 1998. [Google Scholar]
  9. Ramirez-Hernandez, A.; Galagarza, O.A.; Álvarez Rodriguez, M.V.; Pachari Vera, E.; Valdez Ortiz, M.d.C.; Deering, A.J.; Oliver, H.F. Food Safety in Peru: A Review of Fresh Produce Production and Challenges in the Public Health System. Compr. Rev. Food Sci. Food Saf. 2020, 19, 3323–3342. [Google Scholar] [CrossRef] [PubMed]
  10. Kumar, P.; Dobriyal, M.; Kale, A.; Pandey, A.K.; Tomar, R.S.; Thounaojam, E. Calculating Forest Species Diversity with Information-Theory Based Indices Using Sentinel-2A Sensor’s of Mahavir Swami Wildlife Sanctuary. PLoS ONE 2022, 17, e0268018. [Google Scholar] [CrossRef] [PubMed]
  11. Pimentel, D.; Stachow, U.; Takacs, D.A.; Brubaker, H.W.; Amy, R.; Meaney, J.J.; Neil, J.A.S.O.; Onsi, D.E.; Corzilius, D.B.; Dumas, A.R.; et al. Conserving Biological Diversity in Most Biological Diversity Exists in Agricultural/Forestry Systems. Bioscience 1992, 42, 354–362. [Google Scholar] [CrossRef]
  12. Tomar, V.; Kumar, P.; Gupta, G. A Satellite-Based Biodiversity Dynamics Capability in Tropical Forest. Electron. J. Geotech. Eng. 2013, 18, 1171–1180. [Google Scholar]
  13. Hooper, D.U.; Chapin, F.S.; Ewel, J.J.; Hector, A.; Inchausti, P.; Lavorel, S.; Lawton, J.H.; Lodge, D.M.; Loreau, M.; Naeem, S.; et al. ESA Report Effects of Biodiversity on Ecosystem Functioning: A Consensus of Current Knowledge. Ecol. Monogr. 2005, 75, 3–35. [Google Scholar] [CrossRef]
  14. Wester, P.; Mishra, A.; Mukherji, A.; Shrestha, A.B. The Hindu Kush Himalaya Assessment; Springer Nature: Berlin/Heidelberg, Germany, 2019. [Google Scholar]
  15. Ulian, T.; Diazgranados, M.; Pironon, S.; Padulosi, S.; Liu, U.; Davies, L.; Howes, M.J.R.; Borrell, J.S.; Ondo, I.; Pérez-Escobar, O.A.; et al. Unlocking Plant Resources to Support Food Security and Promote Sustainable Agriculture. Plants People Planet 2020, 2, 421–445. [Google Scholar] [CrossRef]
  16. Ghosh, S.; Kumari, A.; Chuleui, P.; Satpal, J.; Bisht, S. Emerging Solutions in Sustainable Food and Nutrition Security; Springer: Cham, Switzerland, 2023. [Google Scholar]
  17. Keylock, C.J. Simpson Diversity and the Shannon-Wiener Index as Special Cases of a Generalized Entropy. Oikos 2005, 109, 203–207. [Google Scholar] [CrossRef]
  18. Roswell, M.; Dushoff, J.; Winfree, R. A Conceptual Guide to Measuring Species Diversity. Oikos 2021, 130, 321–338. [Google Scholar] [CrossRef]
  19. Margalef, R. Dynamic Aspects of Diversity. J. Veg. Sci. 1994, 5, 451–456. [Google Scholar] [CrossRef]
  20. Koricho, H.H.; Seboka, A.D.; Song, S. Assessment of the Structure, Diversity, and Composition of Woody Species of Urban Forests of Adama City, Central Ethiopia. Arboric. J. 2020, 1–12. [Google Scholar] [CrossRef]
  21. Wang, C.T.; Long, R.J.; Wang, Q.J.; Ding, L.M.; Wang, M.P. Effects of Altitude on Plant-Species Diversity and Productivity in an Alpine Meadow, Qinghai-Tibetan Plateau. Aust. J. Bot. 2007, 55, 110–117. [Google Scholar] [CrossRef]
  22. Sistema Integrado de Estadística Agraria Actividades Estadísticas. Available online: https://opsaa.iica.int/resource-149-siea:-sistema-integrado-de-estadisticas-agrarias-en-peru (accessed on 15 January 2024).
  23. Boryan, C.G.; Yang, Z.; Willis, P. US Geospatial Crop Frequency Data Layers. In Proceedings of the Third International Conference on Agro-Geoinformatics, Beijing, China, 11–14 August 2014; IEEE: New York, NY, USA, 2014. [Google Scholar]
  24. Anríquez, G.; Toledo, G. De-Climatizing Food Security: Lessons from Climate Change Micro-Simulations in Peru. PLoS ONE 2019, 14, e0222483. [Google Scholar] [CrossRef] [PubMed]
  25. García, L.; Veneros, J.; Chavez, S.G.; Oliva, M.; Rojas-Briceño, N.B. World Historical Mapping and Potential Distribution of Cinchona Spp. in Peru as a Contribution for Its Restoration and Conservation. J. Nat. Conserv. 2022, 70, 126290. [Google Scholar] [CrossRef]
  26. Omoro, L.M.A.; Pellikka, P.K.E.; Rogers, P.C. Tree Species Diversity, Richness, and Similarity between Exotic and Indigenous Forests in the Cloud Forests of Eastern Arc Mountains, Taita Hills, Kenya. J. For. Res. 2010, 21, 255–264. [Google Scholar] [CrossRef]
  27. Ulanowicz, R.E. Information Theory in Ecology. Comput. Chem. 2001, 25, 393–399. [Google Scholar] [CrossRef] [PubMed]
  28. Fauziyah; Haryanti, Y. Prediksi Profitabilitas Bank Umum Konvensional Pada Masa Pandemi Covid-19. J. Stat. 2022, 15, 245–250. [Google Scholar]
  29. Jones, S.K.; Estrada-Carmona, N.; Juventia, S.D.; Dulloo, M.E.; Laporte, M.A.; Villani, C.; Remans, R. Agrobiodiversity Index Scores Show Agrobiodiversity Is Underutilized in National Food Systems. Nat. Food 2021, 2, 712–723. [Google Scholar] [CrossRef]
  30. Lankau, R.A.; Strauss, S.Y. Mutual Feedbacks Maintain Both Genetic and Species Diversity in a Plant Community. Science 2007, 317, 1561–1563. [Google Scholar] [CrossRef] [PubMed]
  31. Scherer-Lorenzen, M.; Gessner, M.O.; Beisner, B.E.; Messier, C.; Paquette, A.; Petermann, J.S.; Soininen, J.; Nock, C.A. Pathways for Cross-Boundary Effects of Biodiversity on Ecosystem Functioning. Trends Ecol. Evol. 2022, 37, 454–467. [Google Scholar] [CrossRef]
  32. Heino, J.; Alahuhta, J.; Bini, L.M.; Cai, Y.; Heiskanen, A.S.; Hellsten, S.; Kortelainen, P.; Kotamäki, N.; Tolonen, K.T.; Vihervaara, P.; et al. Lakes in the Era of Global Change: Moving beyond Single-Lake Thinking in Maintaining Biodiversity and Ecosystem Services. Biol. Rev. 2021, 96, 89–106. [Google Scholar] [CrossRef] [PubMed]
  33. Ifo, S.A.; Moutsambote, J.M.; Koubouana, F.; Yoka, J.; Ndzai, S.F.; Bouetou-Kadilamio, L.N.O.; Mampouya, H.; Jourdain, C.; Bocko, Y.; Mantota, A.B.; et al. Tree Species Diversity, Richness, and Similarity in Intact and Degraded Forest in the Tropical Rainforest of the Congo Basin: Case of the Forest of Likouala in the Republic of Congo. Int. J. For. Res. 2016, 2016, 7593681. [Google Scholar] [CrossRef]
  34. The World Bank. World Development Indicators—DataBank; World Bank Group: Washington, DC, USA, 2019. [Google Scholar]
  35. Hernández, C.; Perales, H.; Jaffee, D. “Without Food There Is No Resistance”: The Impact of the Zapatista Conflict on Agrobiodiversity and Seed Sovereignty in Chiapas, Mexico. Geoforum 2022, 128, 236–250. [Google Scholar] [CrossRef]
  36. Ghalambor, C.K.; McKay, J.K.; Carroll, S.P.; Reznick, D.N. Adaptive versus Non-Adaptive Phenotypic Plasticity and the Potential for Contemporary Adaptation in New Environments. Funct. Ecol. 2007, 21, 394–407. [Google Scholar] [CrossRef]
  37. Smit, B.; Wandel, J. Adaptation, Adaptive Capacity and Vulnerability. Glob. Environ. Chang. 2006, 16, 282–292. [Google Scholar] [CrossRef]
  38. Cleland, E.E. Trait Divergence and the Ecosystem Impacts of Invading Species. New Phytol. 2011, 189, 649–652. [Google Scholar] [CrossRef] [PubMed]
  39. Mardones, F.O.; Rich, K.M.; Boden, L.A.; Moreno-Switt, A.I.; Caipo, M.L.; Zimin-Veselkoff, N.; Alateeqi, A.M.; Baltenweck, I. The COVID-19 Pandemic and Global Food Security. Front. Vet. Sci. 2020, 7, 578508. [Google Scholar] [CrossRef] [PubMed]
  40. Picchioni, F.; Goulao, L.F.; Roberfroid, D. The Impact of COVID-19 on Diet Quality, Food Security and Nutrition in Low and Middle Income Countries: A Systematic Review of the Evidence. Clin. Nutr. 2022, 41, 2955–2964. [Google Scholar] [CrossRef] [PubMed]
  41. Amjath-Babu, T.S.; Krupnik, T.J.; Thilsted, S.H.; McDonald, A.J. Key Indicators for Monitoring Food System Disruptions Caused by the COVID-19 Pandemic: Insights from Bangladesh towards Effective Response. Food Secur. 2020, 12, 761–768. [Google Scholar] [CrossRef] [PubMed]
  42. Ruben, R.; Van Houte, M.; Davids, T. What Determines the Embeddedness of Forced-Return Migrants? Rethinking the Role of Pre- and Post-Return Assistance. Int. Migr. Rev. 2009, 43, 908–937. [Google Scholar] [CrossRef]
  43. Mencutek, Z.S. Voluntary and Forced Return Migration Under a Pandemic Crisis. In Migration and Pandemics: Spaces of Solidarity and Spaces of Exception; Research Series Migration and Pandemics; Springer: Berlin/Heidelberg, Germany, 2022. [Google Scholar]
  44. Fort, R.; Espinoza, M.; Espinoza, Á. COVID-19 y Las Migraciones de La Ciudad Al Campo En El Perú: Identificación de Amenazas y Oportunidades Para El Uso Sostenible Del Capital Natural; Nota Técnica N° IDB-TN-02234; BID-GRADE: Lima, Peru, 2021; p. 56. [Google Scholar]
  45. Sorensen, D.M. Western Agricultural Economics Association Reverse Migration and the Rural Community Development Problem. West. J. Agric. Econ. 1977, 1, 49–55. [Google Scholar]
  46. Recognising Silent Epidemics States and Revenue Protection Manufacturing Mobile Phones Gandhi and Self-Interrogation. Economic & Political Weekly, 9 May 2020.
  47. Pradhan, P.C. Redefining the Policies on Agriculture for Accommodating the Reverse Migrants During COVID-19 & Building Road Map for Development. In Social Sector in India: COVID-19 Issues and Challenges; Bharti Publications: New Delhi, India, 2021. [Google Scholar]
  48. Nath, A.; Jindal, T.O.P. Economic Implications of Reverse Migration in India. J. Migr. Aff. 2020, 3, 16–31. [Google Scholar] [CrossRef]
  49. Kaur, B.; Shubham, S. COVID-19 Crisis Through a Reverse Migration Lens. Rural Pulse 2021, 36, 1–4. [Google Scholar]
  50. Lawler, O.K.; Allan, H.L.; Baxter, P.W.J.; Castagnino, R.; Tor, M.C.; Dann, L.E.; Hungerford, J.; Karmacharya, D.; Lloyd, T.J.; López-Jara, M.J.; et al. The COVID-19 Pandemic Is Intricately Linked to Biodiversity Loss and Ecosystem Health. Lancet Planet. Health 2021, 5, e840–e850. [Google Scholar] [CrossRef]
  51. Pillaca-Medina, S.; Chavez-Dulanto, P.N. How Effective and Efficient Are Social Programs on Food and Nutritional Security? The Case of Peru: A Review. Food Energy Secur. 2017, 6, e00120. [Google Scholar] [CrossRef]
  52. Trigoso, D.I.; Salas López, R.; Rojas Briceño, N.B.; Silva López, J.O.; Gómez Fernández, D.; Oliva, M.; Quiñones Huatangari, L.; Terrones Murga, R.E.; Castillo, E.B.; Ángel, M.; et al. Land Suitability Analysis for Potato Crop in the Jucusbamba and Tincas Microwatersheds (Amazonas, NW Peru): AHP and RS-GIS Approach. Agronomy 2020, 10, 1898. [Google Scholar] [CrossRef]
  53. Salas López, R.; Gómez Fernández, D.; Silva López, J.O.; Rojas Briceño, N.B.; Oliva, M.; Terrones Murga, R.E.; Trigoso, D.I.; Castillo, B.; Ángel, M.; Gurbillón, B. Geo-Information Land Suitability for Coffee (Coffea Arabica) Growing in Amazonas, Peru: Integrated Use of AHP, GIS and RS. ISPRS Int. J. Geo-Inf. 2020, 9, 673. [Google Scholar] [CrossRef]
  54. Rojas-Briceño, N.B.; García, L.; Cotrina-Sánchez, A.; Goñas, M.; Salas López, R.; Silva López, J.O.; Oliva-Cruz, M. Land Suitability for Cocoa Cultivation in Peru: AHP and MaxEnt Modeling in a GIS Environment. Agronomy 2022, 12, 2930. [Google Scholar] [CrossRef]
  55. Cotrina-Sanchez, A.; García, L.; Calle, C.; Sari, F.; Bandopadhyay, S.; Rojas-Briceño, N.B.; Meza-Mori, G.; Torres Guzmán, C.; Auquiñivín-Silva, E.; Arellanos, E.; et al. Multicriteria Analysis in Apiculture: A Sustainable Tool for Rural Development in Communities and Conservation Areas of Northwest Peru. Land 2023, 12, 1900. [Google Scholar] [CrossRef]
Sustainability 16 04191 g001
Figure 1. Geographic location of districts in Peru, where the surveys were conducted.
Figure 1. Geographic location of districts in Peru, where the surveys were conducted.
Sustainability 16 04191 g001
Sustainability 16 04191 g002
Figure 2. Historical trend model and projection to the year 2025 for productive agricultural diversity by crop type.
Figure 2. Historical trend model and projection to the year 2025 for productive agricultural diversity by crop type.
Sustainability 16 04191 g002
Sustainability 16 04191 g003
Figure 3. Historical trend model and projection to 2025 for productive agricultural diversity by crop family (Part A).
Figure 3. Historical trend model and projection to 2025 for productive agricultural diversity by crop family (Part A).
Sustainability 16 04191 g003
Sustainability 16 04191 g004
Figure 4. Historical trend model and projection to 2025 for productive agricultural diversity by crop family (Part B).
Figure 4. Historical trend model and projection to 2025 for productive agricultural diversity by crop family (Part B).
Sustainability 16 04191 g004
Table 1. Historical Variability of Agricultural Crops in the Three Natural Regions of Peru.
Table 1. Historical Variability of Agricultural Crops in the Three Natural Regions of Peru.
N°TypeCommon NameScientific NameFamilyF. 2018F. 2019F. 2020F. 2021F. 2022
1C.MaízZea mays L.Poaceae6162556365
2L.FréjolPhaseolus vulgaris L.Fabaceae3333292929
3F.CaféCoffea arabica L.Rubiaceae6062616161
4F.Caña de azúcarSaccharum officinarum L.Poaceae78898
5F.PlátanoMusa paradisiaca L.Musaceae2628282625
6R.YucaManihot esculenta C.Euphorbiaceae2622251823
7Le.ArverjaPisum sativum L.Fabaceae2520232021
8Le.HabasVicia faba L.Fabaceae1614171321
9C.TrigoTriticum aestivum L.Poaceae2827302324
10L.ManíArachis hypogaea L.Fabaceae21033
11F.CacaoTheobroma cacao L.Malvaceae11000
12V.PepinilloCucumis sativus L.Cucurbitaceae00100
13V.CaiguaCyclanthera pedata L.Cucurbitaceae00100
14V.LechugaLactuca sativa L.Asteraceae11122
15R.RabanitoRaphanus sativus L.Brassicaceae00001
16T.PapaSolanum tuberosum L.Solanaceae2928272627
17R.CamoteIpomoea batatas L.Convolvulaceae545411
18F.OlivoOlea europaea L.Oleaceae12141196
19F.PaltaPersea americana M.Lauraceae21459
20F.CiruelaPrunus domestica L.Rosaceae10010
21F.ManzanaMalus domestica B.Rosaceae21011
22F.membrilloCydonia oblonga M.Rosaceae10000
23R.CebollaAllium cepa L. Amaryllidaceae11000
24F.HigoFicus carica L.Moraceae11101
25F.PacaeInga feuilleei DCFabaceae10002
26F.NaranjaCitrus sinensis L.Rutaceae11102
27F.GranadaPunica granatum L.Lythraceae10023
28F.DuraznoPrunus persica L.Rosaceae67569
29V.AlfalfaMedicago sativa L.Fabaceae9101088
30F.LimónCitrus limon L.Rutaceae01111
31F.NísperoEriobotrya japonica T.Rosaceae00010
32C.CebadaHordeum vulgare L.Poaceae13871115
33R.OllucosUllucus tuberosus C.Basellaceae 11131
34R.MashuaTrapeolum tuberosum Ruiz & Pav.Tropaeolaceae21001
35R.OcaOxalis tuberosa M.Oxalidaceae56953
36C.QuinoaChenopodium quinoa W.Amarantaceae12431
37C.AvenaAvena sativaPoaceae10001
38F.GranadillaPassiflora ligularis J.Passifloraceae11121
39F.RocotoCapsicum pubescens Ruiz & PavSolanaceae00011
40V.AjíCapsicum annuum L.Solanaceae11111
41F.SandíaCitrullus lanatus T.Cucurbitaceae10000
42R.Bituca Colocasia esculenta L.Araceae11111
43R.Racacha Arracacia xanthorrhiza B.Apiaceae44443
44V.ZapalloCucurbita Maxima D.Cucurbitaceae10000
45V.RepolloBrassica oleracea L.Brassicaceae00002
46V.Zanahoria Daucus carota L.Apiaceae23233
47L.ChochoLupinus mutabilis S. Leguminosae11111
F/year = absolute frequency over 180 farmers interviewed. C = cereal, L = legume, F = fruit, R = root, T = tuberose, V = vegetable.
Table 2. Historical records of the average number of agricultural species cultivated per farmer in nine districts of the three natural regions of Peru.
Table 2. Historical records of the average number of agricultural species cultivated per farmer in nine districts of the three natural regions of Peru.
RegionDistrictAverage Number of Agricultural Species Cultivated
per Farmer		∆
2018–2022		Tendency
20182019202020212022
CoastAtico2.65 ± 1.502.1 ± 1.022.15 ± 0.882.25 ± 1.253.3 ± 1.4224.53%Sustainability 16 04191 i001
Guadalupe 1.55 ± 0.511.6 ± 0.601.65 ± 0.591.65 ± 0.591.65 ± 0.596.45%Sustainability 16 04191 i002
Paccho3.9 ± 1.173.8 ± 1.203.4 ± 0.943.3 ± 0.803.4 ± 0.82−12.82%Sustainability 16 04191 i003
HighlandsLucanas3.45 ± 1.282.9 ± 0.972.75 ± 1.123.25 ± 0.853.7 ± 1.347.25%Sustainability 16 04191 i004
Lonya Chico4.75 ± 1.374.4 ± 1.604.4 ± 1.604.4 ± 1.544.5 ± 1.50−5.26%Sustainability 16 04191 i005
Sapillica2.35 ± 1.092.2 ± 1.111.8 ± 0.771.8 ± 0.7001.7 ± 0.60−27.66%Sustainability 16 04191 i006
JungleSanta Rosa1.65 ± 1.091.65 ± 0.991.65 ± 0.991.65 ± 0.991.65 ± 0.990.00%Sustainability 16 04191 i007
Jepelacio3.00 ± 1.082.8 ± 1.012.9 ± 0.792.7 ± 0.982.65 ± 0.99−11.67%Sustainability 16 04191 i008
Villa Rica1.05 ± 0.221 ± 0.011 ± 0.011.05 ± 0.221.1 ± 0.454.76%Sustainability 16 04191 i009
Summary2.71 ± 1.572.49 ± 1.442.41 ± 1.362.45 ± 1.362.63 ± 1.49−2.87%Sustainability 16 04191 i010
Table 3. Retrospective values of the Agricultural Diversity Index, and trends for 9 districts of the three natural regions of Peru.
Table 3. Retrospective values of the Agricultural Diversity Index, and trends for 9 districts of the three natural regions of Peru.
Natural RegionDistrictIndex20182019202020212022Trend
Coast RegionAticoShannon_H2.842.882.912.862.9
Margalef4.795.085.054.994.53
PacchoShannon_H2.962.952.962.972.97Sustainability 16 04191 i011
Margalef4.364.394.54.534.5
GuadalupeShannon_H2.942.932.942.942.94
Margalef5.335.485.435.435.43
Highlands
Region		LucanasShannon_H2.922.942.912.962.93
Margalef4.494.684.744.554.41
LonyachicoShannon_H2.952.912.912.912.2
Margalef4.174.024.024.024Sustainability 16 04191 i012
SapillicaShannon_H2.892.882.912.922.93
Margalef4.945.025.305.305.39
Jungle
Region		Villa RicaShannon_H2.982.33.002.982.94Sustainability 16 04191 i013
Margalef6.246.346.346.246.14
JepelacioShannon_H2.932.932.962.942.94
Margalef4.644.724.684.764.79
Santa RosaShannon_H2.82.862.862.862.86
Margalef5.155.435.435.435.43
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.

Share and Cite

MDPI and ACS Style

Chavez, S.G.; Arellanos, E.; Veneros, J.; Rojas-Briceño, N.B.; Oliva-Cruz, M.; Bolaños-Carriel, C.; García, L. Unveiling Peru’s Agricultural Diversity: Navigating Historical and Future Trends in a Post-COVID-19 Context. Sustainability 2024, 16, 4191. https://doi.org/10.3390/su16104191

AMA Style

Chavez SG, Arellanos E, Veneros J, Rojas-Briceño NB, Oliva-Cruz M, Bolaños-Carriel C, García L. Unveiling Peru’s Agricultural Diversity: Navigating Historical and Future Trends in a Post-COVID-19 Context. Sustainability. 2024; 16(10):4191. https://doi.org/10.3390/su16104191

Chicago/Turabian Style

Chavez, Segundo G., Erick Arellanos, Jaris Veneros, Nilton B. Rojas-Briceño, Manuel Oliva-Cruz, Carlos Bolaños-Carriel, and Ligia García. 2024. "Unveiling Peru’s Agricultural Diversity: Navigating Historical and Future Trends in a Post-COVID-19 Context" Sustainability 16, no. 10: 4191. https://doi.org/10.3390/su16104191

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop