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Research article · 2025 · Full text

International Journal of Geography Geology and Environment

Rural-urban migration dynamics and impact on agricultural land conversion in the Nile Valley

Elena ParedesAlejandro GutiérrezGabriela Morales

Volume 7(2) · Pages 108–110 · DOI: 10.22271/27067483.2025.v7.i2b.575 ↗

Research areasLand Use and Ecosystem ServicesRegional Socio-Economic Development TrendsAgriculture, Land Use, Rural Development

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Abstract

Author-written

Rural-urban migration—the movement of people from agricultural hinterlands to cities in search of employment, education, or improved living standards—reshapes landscapes as dramatically as it reshapes societies. This research examined how out-migration from six Upper Egypt governorates between 2000 and 2020 influenced the rate and pattern of agricultural land conversion along the Nile Valley. Census records, household survey data from 1,240 farming families, and multi-temporal Landsat imagery formed the analytical foundation. Net out-migration rates ranged from 19.3 per thousand in Luxor to 33.2 per thousand in Minya. Agricultural land conversion to built-up area accelerated from a mean annual rate of 1.8% during 2000–2005 to 5.3% during 2016–2020 across the six governorates. Regression modelling showed that migration rate explained 67% of the variance in land conversion rate (R² = 0.67, p < 0.001), after controlling for population growth and proximity to urban centres. Household surveys revealed that 58.4% of families with at least one migrant member had reduced their cultivated area, compared to 21.7% of non-migrant households. The research identified a feedback loop: migration reduces the agricultural labour force, which lowers farm productivity, which then encourages further migration and land sale for construction. These patterns suggest that agricultural preservation policies in the Nile Valley must address both push factors driving migration and the land market dynamics that follow.

Summary

Source-checked

Plain-language reading of the full paper, prepared with AI and checked against the text. Each point links to the authors' own words.

  1. Why it matters

    • The Nile Valley is a critical area where a narrow strip of fertile soil, typically under 15 km wide, is under pressure from competing agricultural and urban needs. View evidence ↗

      Why it matters

      The Nile Valley is a critical area where a narrow strip of fertile soil, typically under 15 km wide, is under pressure from competing agricultural and urban needs.

      In the paper's own words

      “Egypt’s Nile Valley presents an acute case: a narrow ribbon of fertile alluvial soil, rarely more than 15 km wide, supports both the agricultural economy and rapidly growing cities that compete for the same limited land”

      Page 1

      Found in: Introduction

      Read it in the full textOpen PDF at page 1 ↗
    • Egypt experienced significant urbanization between 2000 and 2020, with the urban population increasing from 42% to 57%, largely driven by migration from Upper Egypt. View evidence ↗

      Why it matters

      Egypt experienced significant urbanization between 2000 and 2020, with the urban population increasing from 42% to 57%, largely driven by migration from Upper Egypt.

      In the paper's own words

      “Between 2000 and 2020, Egypt’s urban population grew from 42% to 57% of the national total, with much of that growth fed by rural-to-urban flows from the Upper Egypt governorates”

      Page 1

      Found in: Introduction

      Read it in the full textOpen PDF at page 1 ↗
    • The departure of young adults from farming communities can lead to higher production costs and a reduction in crop diversity and cultivated land. View evidence ↗

      Why it matters

      The departure of young adults from farming communities can lead to higher production costs and a reduction in crop diversity and cultivated land.

      In the paper's own words

      “Labour shortages raise production costs, reduce crop diversification, and push marginal plots out of cultivation”

      Page 1

      Found in: Introduction

      Read it in the full textOpen PDF at page 1 ↗
  2. The question

    • The study aimed to determine how rural-to-urban migration in six Upper Egypt governorates affected the conversion of agricultural land between 2000 and 2020. View evidence ↗

      The question

      The study aimed to determine how rural-to-urban migration in six Upper Egypt governorates affected the conversion of agricultural land between 2000 and 2020.

      In the paper's own words

      “This research examined how out-migration from six Upper Egypt governorates between 2000 and 2020 influenced the rate and pattern of agricultural land conversion along the Nile Valley.”

      Page 1

      Found in: Introduction

      Read it in the full textOpen PDF at page 1 ↗
  3. What they did

    • Researchers surveyed 1,240 farming families across Asyut, Sohag, Qena, Luxor, Aswan, and Minya, stratifying them by their distance to urban centers. View evidence ↗

      What they did

      Researchers surveyed 1,240 farming families across Asyut, Sohag, Qena, Luxor, Aswan, and Minya, stratifying them by their distance to urban centers.

      In the paper's own words

      “A structured household survey was administered to 1,240 farming families across the six governorates (Asyut, Sohag, Qena, Luxor, Aswan, and Minya), stratified by distance from the nearest urban centre.”

      Page 2

      Found in: Methods

      Read it in the full textOpen PDF at page 2 ↗
    • The study used 30 m resolution Landsat 7 and Landsat 8 satellite imagery to track land use changes from 2000 to 2020. View evidence ↗

      What they did

      The study used 30 m resolution Landsat 7 and Landsat 8 satellite imagery to track land use changes from 2000 to 2020.

      In the paper's own words

      “Satellite imagery included Landsat 7 ETM+ (2000–2012) and Landsat 8 OLI (2013–2020) at 30 m resolution”

      Page 2

      Found in: Limitations

      Read it in the full textOpen PDF at page 2 ↗
    • A multiple linear regression model was used to analyze how migration rate, population growth, farm size, and urban proximity influenced land conversion. View evidence ↗

      What they did

      A multiple linear regression model was used to analyze how migration rate, population growth, farm size, and urban proximity influenced land conversion.

      In the paper's own words

      “Multiple linear regression modelled land conversion rate as a function of migration rate, population growth rate, distance to nearest urban centre, and farm size.”

      Page 2

      Found in: Methods

      Read it in the full textOpen PDF at page 2 ↗
  4. What they found

    • Net out-migration rates varied by region, with the lowest rate in Luxor (19.3 per thousand) and the highest in Minya (33.2 per thousand). View evidence ↗

      What they found

      Net out-migration rates varied by region, with the lowest rate in Luxor (19.3 per thousand) and the highest in Minya (33.2 per thousand).

      In the paper's own words

      “Net out-migration rates ranged from 19.3 per thousand in Luxor to 33.2 per thousand in Minya.”

      Page 1

      Found in: Comprehensive Interpretation

      Read it in the full textOpen PDF at page 1 ↗
    • The average annual rate of agricultural land conversion to built-up areas increased from 1.8% (2000–2005) to 5.3% (2016–2020). View evidence ↗

      What they found

      The average annual rate of agricultural land conversion to built-up areas increased from 1.8% (2000–2005) to 5.3% (2016–2020).

      In the paper's own words

      “Agricultural land conversion to built-up area accelerated from a mean annual rate of 1.8% during 2000–2005 to 5.3% during 2016–2020 across the six governorates.”

      Page 1

      Found in: Methods

      Read it in the full textOpen PDF at page 1 ↗
    • Migration rate was a strong predictor of land conversion, explaining 67% of the variance (R² = 0.67, p < 0.001) when controlling for other factors. View evidence ↗

      What they found

      Migration rate was a strong predictor of land conversion, explaining 67% of the variance (R² = 0.67, p < 0.001) when controlling for other factors.

      In the paper's own words

      “Regression modelling showed that migration rate explained 67% of the variance in land conversion rate (R² = 0.67, p < 0.001), after controlling for population growth and proximity to urban centres.”

      Page 1

      Found in: Methods

      Read it in the full textOpen PDF at page 1 ↗
    • Families with at least one migrant member were more likely to reduce their cultivated area (58.4%) compared to non-migrant households (21.7%). View evidence ↗

      What they found

      Families with at least one migrant member were more likely to reduce their cultivated area (58.4%) compared to non-migrant households (21.7%).

      In the paper's own words

      “Household surveys revealed that 58.4% of families with at least one migrant member had reduced their cultivated area, compared to 21.7% of non-migrant households.”

      Page 1

      Found in: Comprehensive Interpretation

      Read it in the full textOpen PDF at page 1 ↗
    • Migrant households sold farmland at a significantly higher rate (34.6%) than non-migrant households (8.3%). View evidence ↗

      What they found

      Migrant households sold farmland at a significantly higher rate (34.6%) than non-migrant households (8.3%).

      In the paper's own words

      “Sold farmland (%) | 34.6 | 8.3 | < 0.001”

      Page 2

      Found in: Comprehensive Interpretation

      Read it in the full textOpen PDF at page 2 ↗
    • Land conversion was 2.4 times higher in peri-urban villages than in remote settlements. View evidence ↗

      What they found

      Land conversion was 2.4 times higher in peri-urban villages than in remote settlements.

      In the paper's own words

      “peri-urban villages showing conversion rates 2.4 times higher than remote settlements”

      Page 3

      Found in: Discussion

      Read it in the full textOpen PDF at page 3 ↗
  5. What it means

    • The authors concluded that a self-reinforcing feedback loop exists where migration leads to labor shortages and lower productivity, which in turn drives more migration and land sales. View evidence ↗

      What it means

      The authors concluded that a self-reinforcing feedback loop exists where migration leads to labor shortages and lower productivity, which in turn drives more migration and land sales.

      In the paper's own words

      “The research identified a feedback loop: migration reduces the agricultural labour force, which lowers farm productivity, which then encourages further migration and land sale for construction.”

      Page 1

      Found in: Discussion

      Read it in the full textOpen PDF at page 1 ↗
    • The authors recommend policies that simultaneously improve rural livelihoods to reduce migration and strengthen regulations to protect prime agricultural land. View evidence ↗

      What it means

      The authors recommend policies that simultaneously improve rural livelihoods to reduce migration and strengthen regulations to protect prime agricultural land.

      In the paper's own words

      “Policy responses should address both sides: improving rural livelihood options to reduce push-driven migration, and strengthening land use regulations to prevent conversion of prime agricultural land”

      Page 3

      Found in: Conclusion

      Read it in the full textOpen PDF at page 3 ↗
    • The 30 m resolution of the satellite imagery may have missed small-scale land conversions, such as individual houses built on farms. View evidence ↗

      What it means

      The 30 m resolution of the satellite imagery may have missed small-scale land conversions, such as individual houses built on farms.

      In the paper's own words

      “Satellite classification at 30 m resolution may undercount small-scale conversions (individual houses on farms) that fall below the detection threshold.”

      Page 3

      Found in: Limitations

      Read it in the full textOpen PDF at page 3 ↗

Visual summary

3 visuals

Illustrations explain the paper; charts are replotted from the paper's own table values.

AI-generated illustration
AI-generated conceptual illustration (Nile Valley agricultural landscape and urban settlements). It explains the idea and does not show study data.↗ Enlarge
The Migration-Land Conversion Feedback Loop
AI-generated conceptual illustration (Nile Valley agricultural landscape and urban settlements). It explains the idea and does not show study data.

AI-generated conceptual illustration (Nile Valley agricultural landscape and urban settlements). It explains the idea and does not show study data.

Open full-size image ↗

The Migration-Land Conversion Feedback Loop

AI-generated conceptual illustration (Nile Valley agricultural landscape and urban settlements). It explains the idea and does not show study data.

View source evidence ↗
Replotted from source data
Replotted from Table 1 (page 2) of the original paper. Every bar is the paper's own table value.↗ Enlarge
Migration Rate: Asyut vs Sohag vs Qena vs Luxor vs Aswan
Replotted from Table 1 (page 2) of the original paper. Every bar is the paper's own table value.

Replotted from Table 1 (page 2) of the original paper. Every bar is the paper's own table value.

Open full-size image ↗

Migration Rate: Asyut vs Sohag vs Qena vs Luxor vs Aswan

Replotted from Table 1 (page 2) of the original paper. Every bar is the paper's own table value.

View source evidence ↗
Replotted from source data
Replotted from Table 1 (page 2) of the original paper. Every bar is the paper's own table value.↗ Enlarge
Conv. 00-05 (%) and Conv. 16- 20 (%): Asyut vs Sohag vs Qena vs Luxor vs Aswan
Replotted from Table 1 (page 2) of the original paper. Every bar is the paper's own table value.

Replotted from Table 1 (page 2) of the original paper. Every bar is the paper's own table value.

Open full-size image ↗

Conv. 00-05 (%) and Conv. 16- 20 (%): Asyut vs Sohag vs Qena vs Luxor vs Aswan

Replotted from Table 1 (page 2) of the original paper. Every bar is the paper's own table value.

View source evidence ↗

Figures & tables

From the original paper
Migration rates and land conversion by governorateTable 1Page 2Household survey results: land management by migrationTable 2Page 2Distribution of net out-migration rates (per 1,000) across sixFigure 1Page 2Annual agricultural land conversion rate (%) by five-yearFigure 2Page 2
FULL TEXT Original article · reflowed for reading

Introduction

Rural-urban migration, defined as the permanent or semi-permanent relocation of individuals from rural settlements to urban areas, operates as one of the most powerful forces shaping land use in developing countries [1] . Egypt's Nile Valley presents an acute case: a narrow ribbon of fertile alluvial soil, rarely more than 15 km wide, supports both the agricultural economy and rapidly growing cities that compete for the same limited land [2] . Between 2000 and 2020, Egypt's urban population grew from 42% to 57% of the national total, with much of that growth fed by rural-to-urban flows from the Upper Egypt governorates [3] . When young adults leave farming communities, the effects extend beyond simple population decline. Labour shortages raise production costs, reduce crop diversification, and push marginal plots out of cultivation [4] . Families receiving remittances sometimes invest in housing construction on former farmland rather than in agricultural improvement [5] . And as cultivated land fragments, its value as real estate rises relative to its agricultural returns, creating market incentives for conversion [6] . Earlier research documented land conversion rates in the Nile Delta [7] and tracked migration streams from Upper Egypt to Cairo [8] , but few linked these two phenomena at the governorate level. This research connected migration dynamics to agricultural land conversion across six governorates, combining demographic records with remote sensing to measure both the human flows and the landscape changes they set in motion.

Theoretical Framework

This research draws on the New Economics of Labour Migration (NELM) framework, which treats migration as a household-level strategy to manage risk and diversify income rather than as an individual response to wage differentials [9] . Under NELM, remittances sent by migrants alter household investment decisions-including whether to maintain, expand, or sell agricultural land [10] . The research also applies the land rent theory of von Thünen, which predicts that proximity to urban centres raises the opportunity cost of agricultural land use, accelerating conversion at the rural-urban fringe [6] . Combining these two frameworks allows the research to model migration as both a demographic process (people moving) and an economic trigger (land values shifting).

Materials and Methods

Materials and Methods

Materials

Materials

Methods

Net out-migration rates were calculated from inter-censal population change adjusted for natural increase (births minus deaths). Land use classification used a supervised maximum likelihood algorithm in ERDAS Imagine 2020, distinguishing agricultural land, built-up area, bare soil, and water. Classification accuracy reached 89.1% overall (kappa = 0.86). Annual land conversion rates were computed as the percentage of agricultural land reclassified to built-up between consecutive image dates. Multiple linear regression modelled land conversion rate as a function of migration rate, population growth rate, distance to nearest urban centre, and farm size. Household survey data were analysed using chi-square tests for categorical variables and independent t-tests for continuous measures. All statistics used SPSS 27.0 with significance at p < 0.05.

Results

Results

Comprehensive Interpretation

Migration rates varied from 19.3 per thousand in Luxor to 33.2 in Minya (Table 1, Figure 1). Land conversion accelerated sharply, roughly tripling between the first and last five-year periods (Figure 2). Regression confirmed migration rate as the strongest predictor of conversion (R² = 0.67, p < 0.001). Migrant households were 2.7 times more likely to have reduced their cultivated area and 4.2 times more likely to have sold farmland than non-migrant households (Table 2). Minya and Sohag, with the highest migration and remittance rates, also recorded the largest absolute land losses.

Figure 2. Annual agricultural land conversion rate (%) by five-year
Annual agricultural land conversion rate (%) by five-year↗ Enlarge
Figure 2
Annual agricultural land conversion rate (%) by five-year

Annual agricultural land conversion rate (%) by five-year

Open full-size image ↗
Original paper figureSource PDF · page 2 ↗
Figure 1. Distribution of net out-migration rates (per 1,000) across six
Distribution of net out-migration rates (per 1,000) across six↗ Enlarge
Figure 1
Distribution of net out-migration rates (per 1,000) across six

Distribution of net out-migration rates (per 1,000) across six

Open full-size image ↗
Original paper figureSource PDF · page 2 ↗
Table 2. Household survey results: land management by migration
IndicatorMigrant HH (n=682)Non-migrant (n=558)p-value
Reduced cultivated area (%)58.421.7< 0.001
Sold farmland (%)34.68.3< 0.001
Mean farm size (feddan)2.13.40.003
Hired labour cost (EGP/yr)18,40012,6000.011

Table text extracted from the original PDF; superscript letters are shown inline. The original table image is below.

View original table image
Household survey results: land management by migration↗ Enlarge
Table 2
Household survey results: land management by migration

Household survey results: land management by migration

Open full-size image ↗
Original paper tableSource PDF · page 2 ↗
Table 1. Migration rates and land conversion by governorate
Migration rates and land conversion by governorate↗ Enlarge
Table 1
Migration rates and land conversion by governorate

Migration rates and land conversion by governorate

Open full-size image ↗
Original paper figureSource PDF · page 2 ↗

Discussion

The strong migration-conversion link supports the NELM prediction that remittances reshape land use at the household level [9,10] . When migrants send money home, families often invest in housing-which in the Nile Valley means building on agricultural land because flat, irrigated plots are the most accessible construction sites [5] . The conversion acceleration from 1.8% to 5.3% per year over two decades is alarming given that Egypt already imports over 40% of its wheat [11] . The feedback loop identified-migration reducing labour, lowering productivity, encouraging more migration and land sale-resembles patterns documented in China's interior provinces [12] and Mexico's southern highlands [13] . But the Nile Valley case is more severe because the cultivable strip is so narrow: unlike regions where farmland extends hundreds of kilometres, here every hectare lost to construction permanently removes irreplaceable alluvial soil.

Distance to urban centres moderated the relationship, with peri-urban villages showing conversion rates 2.4 times higher than remote settlements, consistent with von Thünen's predictions about land rent gradients [6] .

Limitations

This research relied on two census points (2006 and 2017) to estimate migration rates, which may miss short-term fluctuations driven by economic shocks or policy changes.

The household survey captured self-reported behaviour that could be subject to recall bias, particularly regarding land transactions from the early 2000s. Satellite classification at 30 m resolution may undercount small-scale conversions (individual houses on farms) that fall below the detection threshold. Seasonal variation in agricultural activity could affect the accuracy of land use classification if cloud-free images were not available during peak cropping periods.

Conclusion

This research showed that rural-urban migration from Upper Egypt governorates is strongly linked to agricultural land conversion in the Nile Valley, with migration rate explaining 67% of conversion variance. Migrant households were far more likely to reduce or sell farmland than nonmigrant families. The conversion rate tripled over 20 years, and a self-reinforcing feedback loop connecting labour loss, productivity decline, and land sale appears to be accelerating the process. Policy responses should address both sides: improving rural livelihood options to reduce push-driven migration, and strengthening land use regulations to prevent conversion of prime agricultural land even when market incentives favour construction. Without both, the Nile Valley's already narrow cultivable strip will continue to shrink.

Acknowledgement

Acknowledgements Funding SourcesSupported by institutional research funds for human geography and land use research. Institutional SupportMérida College of Earth and Mineral Sciences provided GIS facilities and survey logistics support. Contributions Not Qualifying for AuthorshipThanks to local survey enumerators across the six governorates.

References

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