Land productivity dynamics in and around protected areas globally from 1999 to 2013

Fuente, Begoña de la; Weynants, Mélanie; Bertzky, Bastian; Delli, Giacomo; Mandrici, Andrea; Bendito, Eduardo Garcia; Dubois, Grégoire

doi:10.1371/journal.pone.0224958

Cited by 15 publications

(7 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With greater intensity of resource usage, the carrying capacity of the land will decline [53]. Along with changes in environmental factors, the level of land production will also be dynamic [54].…”

Section: Figure 1 Results Of Causal Loop Modeling and Dynamic Modelin...mentioning

confidence: 99%

Analysis of productivity levels of saline coastal land for crop cultivation activities

Badrudin,

Ghulamahdi,

Purwoko

et al. 2023

BIO Web Conf.

View full text Add to dashboard Cite

Saline land is an area zone that is widely developed for agribusiness activities with high economic value. The purpose of this study is to determine the parameters that affect the level of productivity of saline land in coastal areas for agricultural agribusiness activities. The research methods used were descriptive quantitative and descriptive qualitative methods. The indicators observed were soil and water parameters on saline land. The results showed that soil parameters that are not following the quality standards of agricultural cultivation are N 0.12-0.14%, P2O5 14-57 mg/L, K2O 473-506 mg/L, Ca 0.47-1.27%, Mg 0.51-5.14%, Al 8.51-243%, and Na 0-0.37%. Water parameters that do not comply with agricultural cultivation quality standards are Ca 54.02-320.60 mg/L, Mg 78.22-424.94 mg/L, Na 582.31-3208.68 mg/L, PO4 0.11-0.72 mg/L. Y= 18.021+0.80X1+0.262X2+0.332X3+0.081X4+0.105X5+0.177X6+0.794X7 Is formula of soil regression test resulted. Meaning that parameters that do not meet soil quality standards will simultaneously affect the land productivity. The water regression model formula by Y= 7890+0.001X1+0.002X2+0.003X3+0.004X4, means that water quality parameters that do not comply with quality standards do not affect the level of land productivity. The results of the dynamic analysis estimate that the level of productivity and carrying capacity of land in saline coastal areas will decrease along with the increase in crop yields. Based on the study of modelling analysis, it can be concluded that there are several soil parameters such as N, P2O5, K2O, Ca, Mg, Al, and Na that affect the productivity level of saline land in coastal areas.

show abstract

Section: Figure 1 Results Of Causal Loop Modeling and Dynamic Modelin...mentioning

confidence: 99%

Analysis of productivity levels of saline coastal land for crop cultivation activities

Badrudin,

Ghulamahdi,

Purwoko

et al. 2023

BIO Web Conf.

View full text Add to dashboard Cite

show abstract

“…Protected areas are less prone to degradation due to their protected status. [50,51] (1b) Socio-Economic Drivers…”

Section: Identifying Archetypes Of Land-degradation Drivers Using Self-organizing Mapsmentioning

confidence: 99%

Social-Ecological Archetypes of Land Degradation in the Nigerian Guinea Savannah: Insights for Sustainable Land Management

Adenle

Speranza

2020

Remote Sensing

View full text Add to dashboard Cite

The Nigerian Guinea Savannah is the most extensive ecoregion in Nigeria, a major food production area, and contains many biodiversity protection areas. However, there is limited understanding of the social-ecological features of its degraded lands and potential insights for sustainable land management and governance. To fill this gap, the self-organizing map method was applied to identify the archetypes of both proximal and underlying drivers of land degradation in this region. Using 12 freely available spatial datasets of drivers of land degradation—4 environmental; 3 socio-economic; and 5 land-use management practices, the identified archetypes were intersected with the Moderate-Resolution Imaging Spectroradiometer (MODIS)-derived land-degradation status of the region, and the state administrative boundaries. Nine archetypes were identified. Archetypes are dominated by: (1) protected areas; (2) very high-density population; (3) moderately high information/knowledge access; (4) low literacy levels and moderate–high poverty levels; (5) rural remoteness; (6) remoteness from a major road; (7) very high livestock density; (8) moderate poverty level and nearly level terrain; and (9) very rugged terrain and remote from a major road. Four archetypes characterized by very high-density population, moderate–high information/knowledge access, and moderate–high poverty level, as well as remoteness from a major town, were associated with 61.3% large-area degradation; and the other five archetypes, covering 38.7% of the area, were responsible for small-area degradation. While different combinations of archetypes exist in all the states, the five states of Niger (40.5%), Oyo (29.6%), Kwara (24.4%), Nassarawa (18.6%), and Ekiti (17.6%), have the largest shares of the archetypes. To deal with these archetypical features, policies and practices that address increasing population in combination with poverty reduction; and that create awareness about land degradation and promote sustainable practices and various forms of land restoration, such as tree planting, are necessary for progressing towards land-degradation neutrality in the Nigerian Guinea Savannah.

show abstract

“…However, before now, various studies (Alawode et al, 2020;Amanullah et al, 2020;Dokic et al, 2022;de la Fuente et al, 2020;Jianxu et al, 2020) had assessed the productivity and profitability of other agricultural crops such as (cassava, rice, wheat, sorghum, maize, yam, etc. ) in Nigeria and other countries of the world, while the few studies on cucumber (Abdulkadir et al, 2020;Bernard and Japhet 2021;Igwe et al, 2020) were explored using minor descriptive statistics such as mean, percentages, etc.…”

Section: Introductionmentioning

confidence: 99%

Does cucumber production affect land productivity and profitability? New evidence from Imo State, Nigeria

Munonye,

Osuji,

Nwokeji

et al. 2023

Int. j. adv. appl. sci.

View full text Add to dashboard Cite

The overall objective of this study is to examine the productivity and profitability of cucumber production in Imo State, Nigeria. A multistage sampling technique was used to select 216 cucumber farmers who were administered a questionnaire. Data were collected from primary sources and analyzed using descriptive statistics, budgeting principles, total and partial factor productivity model, and ordinary least square multiple regression approach. The mean productivity ratio of 0.0049 was used to isolate the land productivity levels of cucumber farmers into high and low degrees. Owerri zone had total factor productivity (TFP) and partial factor productivity (PFP) of 2.11 and 22.9, Okigwe had TFP and PFP of 2.69 and 31.0, and Orlu zone had 3.43 and 36.5 respectively. Age, gender, family size, farm size, education, farming experience, cooperative membership, and extension contacts were important and significant factors affecting net returns and land productivity of farmers in the state. Previous studies reported non-significance of age, education, and farming experience, which is completely different from the results of this study. Cucumber production and marketing were mainly constrained by low capital (100%), high labor costs (92.1%), and high input costs (94.4%). Cucumber production in the state was lucrative, profitable, and economically viable as evidenced by a net return of ₦53547.06.

show abstract

Land productivity dynamics in and around protected areas globally from 1999 to 2013

Cited by 15 publications

References 27 publications

Analysis of productivity levels of saline coastal land for crop cultivation activities

Analysis of productivity levels of saline coastal land for crop cultivation activities

Social-Ecological Archetypes of Land Degradation in the Nigerian Guinea Savannah: Insights for Sustainable Land Management

Does cucumber production affect land productivity and profitability? New evidence from Imo State, Nigeria

Contact Info

Product

Resources

About