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electricsheepafrica
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nigerian_agriculture_seasonal_rainfall_patterns

Tasks:
Time Series Forecasting
Modalities:
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Size:
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Tags:
nigeria
agriculture
food-systems
Synthetic
weather-and-climate
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state
stringclasses
37 values
year
int64
2.02k
2.03k
onset_day_of_year
int64
90
149
cessation_day_of_year
int64
250
309
total_rainfall_mm
float64
301
2.5k
dry_spell_days
int64
1
19
Kano
2,024
101
266
896.9
7
Cross River
2,022
129
256
2,435.7
10
Osun
2,023
102
261
2,224.5
8
Ondo
2,023
123
283
2,132.1
7
Abia
2,025
119
301
2,361.5
7
Rivers
2,023
95
255
2,404.2
5
Borno
2,022
147
275
408
11
Osun
2,024
146
264
1,915.6
4
Gombe
2,025
98
291
1,376.3
5
Akwa Ibom
2,022
128
300
1,643.4
13
Delta
2,022
138
303
2,030.5
11
Zamfara
2,022
91
279
724.2
10
Abia
2,022
145
251
1,587.5
14
Kogi
2,024
143
301
1,079.3
13
Abia
2,022
142
257
1,918.4
9
Yobe
2,023
119
287
459.9
5
Katsina
2,024
134
253
766.6
12
Kano
2,023
114
299
883.7
12
Kogi
2,022
104
293
1,365.7
7
Kebbi
2,025
132
309
685.6
7
Lagos
2,024
101
262
1,587.6
8
Benue
2,023
148
303
1,244.5
9
Kano
2,022
119
302
675.3
13
Niger
2,022
136
258
1,193.3
8
Kano
2,022
134
271
953.5
10
Ogun
2,025
93
258
2,293.9
6
Zamfara
2,022
120
288
638.5
8
Sokoto
2,022
99
289
755.9
9
Adamawa
2,025
105
279
1,397.5
11
Abia
2,022
149
280
1,771.3
5
Adamawa
2,024
108
256
1,044.7
8
Kaduna
2,022
92
259
1,312
9
Katsina
2,022
125
261
906.5
11
Enugu
2,022
140
290
1,806.4
8
Lagos
2,025
140
289
2,427.2
9
Adamawa
2,025
116
298
1,141
5
Bauchi
2,024
148
306
1,237.4
11
Plateau
2,023
106
291
1,061.8
7
Abia
2,022
109
300
2,121.5
12
Rivers
2,023
104
297
2,179.5
6
Benue
2,025
146
281
1,302.3
7
Ebonyi
2,023
106
305
2,265.5
9
Kaduna
2,022
125
290
1,042.2
11
Plateau
2,022
140
304
1,028.5
8
Ondo
2,025
133
283
1,748.5
9
Borno
2,024
123
286
502.9
7
Benue
2,024
119
254
1,446
15
Adamawa
2,025
145
275
1,313.4
7
Oyo
2,023
119
262
1,812.6
5
Oyo
2,025
149
306
1,857.9
9
Edo
2,022
120
278
2,452.9
9
Jigawa
2,022
136
253
500.1
7
Jigawa
2,025
94
268
583.4
10
Anambra
2,022
90
250
2,361.2
4
Nasarawa
2,024
127
285
1,063.3
6
FCT
2,022
130
279
1,412.5
11
Kano
2,023
137
268
787.1
7
Borno
2,023
116
289
452
4
Cross River
2,024
97
263
1,619.1
10
Lagos
2,024
146
256
2,066.7
6
Imo
2,025
128
280
1,606.6
9
Ondo
2,022
137
275
1,548.6
3
Borno
2,024
99
272
442.2
5
Imo
2,023
111
290
2,184.6
6
Abia
2,025
100
258
1,936.7
7
Rivers
2,023
129
304
2,087.4
10
Anambra
2,024
149
257
1,758.1
7
Ogun
2,023
138
272
2,221.4
11
Katsina
2,022
96
266
805.6
6
Kebbi
2,023
97
281
860.5
6
Sokoto
2,023
120
269
985.6
9
Ondo
2,022
97
301
2,396.9
10
Lagos
2,022
138
256
2,396.8
7
Imo
2,025
148
292
1,509
11
Kano
2,023
94
274
689.1
4
Benue
2,024
133
266
1,117.6
7
Ekiti
2,023
119
267
1,563
9
Lagos
2,022
134
254
1,665.4
6
Enugu
2,023
131
256
2,190.8
9
Ondo
2,025
124
262
1,707.6
8
Gombe
2,023
99
279
1,244.5
7
Zamfara
2,023
103
276
736.4
6
Kogi
2,023
144
258
1,166.7
7
Kwara
2,023
107
266
1,373.8
7
Anambra
2,025
147
254
2,437.3
7
Jigawa
2,024
100
294
301
5
Kogi
2,023
107
268
1,215.1
5
Taraba
2,024
125
276
1,126.1
9
Imo
2,025
110
275
2,100.9
12
Bauchi
2,022
117
289
1,132.4
9
Taraba
2,022
138
307
1,257.6
8
Gombe
2,023
100
274
1,035.7
10
Ogun
2,025
142
264
2,053.3
5
FCT
2,024
111
284
1,133.8
9
Gombe
2,025
141
282
1,179.5
7
Rivers
2,023
119
305
2,364.5
12
Niger
2,024
147
267
1,002.9
4
Cross River
2,023
95
276
1,968.8
10
Benue
2,022
116
265
1,182.2
9
Kebbi
2,025
93
266
858.7
7
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Nigeria Agriculture – Seasonal Rainfall Patterns

Dataset Description

Annual rainfall onset, cessation, total, dry spells by state.

Category: Weather & Climate
Rows: 500
Format: CSV, Parquet
License: MIT
Synthetic: Yes (generated using reference data from FAO, NBS, NiMet, FMARD)

Dataset Structure

Schema

  • state: string
  • year: integer
  • onset_day_of_year: integer
  • cessation_day_of_year: integer
  • total_rainfall_mm: float
  • dry_spell_days: integer

Sample Data 

| state       |   year |   onset_day_of_year |   cessation_day_of_year |   total_rainfall_mm |   dry_spell_days |
|:------------|-------:|--------------------:|------------------------:|--------------------:|-----------------:|
| Kano        |   2024 |                 101 |                     266 |               896.9 |                7 |
| Cross River |   2022 |                 129 |                     256 |              2435.7 |               10 |
| Osun        |   2023 |                 102 |                     261 |              2224.5 |                8 |
| Ondo        |   2023 |                 123 |                     283 |              2132.1 |                7 |
| Abia        |   2025 |                 119 |                     301 |              2361.5 |                7 |

Data Generation Methodology

This dataset was synthetically generated using:

  1. Reference Sources:

    • FAO (Food and Agriculture Organization) - crop yields, production data
    • NBS (National Bureau of Statistics, Nigeria) - farm characteristics, surveys
    • NiMet (Nigerian Meteorological Agency) - weather patterns
    • FMARD (Federal Ministry of Agriculture and Rural Development) - extension guides
    • IITA (International Institute of Tropical Agriculture) - agronomic research

  2. Domain Constraints:

    • Crop calendars and phenology (planting/harvest windows)
    • Agro-ecological zone characteristics (Sahel, Sudan Savanna, Guinea Savanna, Rainforest)
    • Nigeria-specific realities (smallholder dominance, market dynamics, conflict zones)
    • Statistical distributions matching national agricultural patterns

  3. Quality Assurance:

    • Distribution testing (KS test, chi-square)
    • Correlation validation (rainfall-yield, fertilizer-yield, yield-price)
    • Causal consistency (DAG-based generation)
    • Multi-scale coherence (farm → state aggregations)
    • Ethical considerations (representative, unbiased)

See QUALITY_ASSURANCE.md in the repository for full methodology.

Use Cases

  • Machine Learning: Yield prediction, price forecasting, pest detection, supply chain optimization
  • Policy Analysis: Agricultural program evaluation, subsidy impact assessment, food security planning
  • Research: Climate-agriculture interactions, market dynamics, technology adoption patterns
  • Education: Teaching agricultural economics, data science applications in agriculture

Limitations

  • Synthetic data: While grounded in real distributions, individual records are not real observations
  • Simplified dynamics: Some complex interactions (e.g., multi-generational pest populations) are simplified
  • Temporal scope: Covers 2022-2025; may not reflect longer-term trends or future climate scenarios
  • Spatial resolution: State/LGA level; does not capture micro-level heterogeneity within localities

Citation

If you use this dataset, please cite:

@dataset{nigeria_agriculture_2025,
  title = {Nigeria Agriculture – Seasonal Rainfall Patterns},
  author = {Electric Sheep Africa},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/electricsheepafrica/nigerian_agriculture_seasonal_rainfall_patterns}
}

Related Datasets

This dataset is part of the Nigeria Agriculture & Food Systems collection:

Contact

For questions, feedback, or collaboration:

Changelog

Version 1.0.0 (October 2025)

  • Initial release
  • 500 synthetic records
  • Quality-assured using FAO/NBS/NiMet reference data
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Collection including electricsheepafrica/nigerian_agriculture_seasonal_rainfall_patterns