<p>In case you cannot access official local data, I recommend the use of the IFPRI resource <a href="https://www.mapspam.info/" rel="nofollow noreferrer">MapSPAM2010</a>. 
Quoting the description of the dataset: </p>

<blockquote>
  <p>Using a variety of inputs, IFPRI's Spatial Production Allocation Model (SPAM) uses a cross-entropy approach to make plausible estimates of crop distribution within disaggregated units. Moving the data from coarser units such as countries and sub-national provinces, to finer units such as grid cells, reveals spatial patterns of crop performance, creating a global grid-scape at the confluence between geography and agricultural production systems. Improving spatial understanding of crop production systems allows policymakers and donors to better target agricultural and rural development policies and investments, increasing food security and growth with minimal environmental impacts.</p>
</blockquote>

<p>The dataset makes available data about agricultural variables for 42 crops at 10km resolution for the whole world. More info are available in the <a href="https://www.mapspam.info/data/" rel="nofollow noreferrer">MapSPAM website</a>, while the full dataset is available <a href="https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/PRFF8V" rel="nofollow noreferrer">here</a>. </p>

<p><strong>Reference</strong>: International Food Policy Research Institute, 2019, "Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 1.1", <a href="https://doi.org/10.7910/DVN/PRFF8V" rel="nofollow noreferrer">https://doi.org/10.7910/DVN/PRFF8V</a>, Harvard Dataverse, V3 </p>

<p>You can download the global rasters and then clip the area you are interested in and make your own estimates regarding specific crops and production technologies.</p>

<p>The information in this dataset, would help you to set up a relatively simple crop model, such as the <a href="http://www.fao.org/aquacrop/en/" rel="nofollow noreferrer">FAO AquaCrop</a>. This system, even with a approximative calibration, could help you to understand some of the impacts of the </p>

<blockquote>
  <p>meteorological factors on the agricultural yields.</p>
</blockquote>

CC BY-SA 4.0
2020-03-16T10:02:47.660Z
2020-03-16T13:50:26.753Z
2020-03-16T13:50:26.753Z
Nemesi
<p>You can definitely try to use <a href="http://www.earthstat.org/" rel="nofollow noreferrer">Earthstat.org</a> and try to recalculate these spatial values with data from <a href="http://www.fao.org/faostat/en/" rel="nofollow noreferrer">FAOSTAT</a>. Other possible data source could be <a href="http://www.fao.org/nr/gaez/en/" rel="nofollow noreferrer">Agroecological zones</a>. Hope that will help!</p>

CC BY-SA 4.0
2020-03-16T09:05:13.613Z
2020-03-16T09:05:13.613Z
Luckasino
<p>I am trying to understand the role of various meteorological factors on agricultural yield over India. Is there a dataset available directly or an indirect proxy of agricultural data, maybe satellite, if any product is there</p>

CC BY-SA 4.0
2019-10-18T09:59:16.947Z
India is a huge country, with a wide variety of climates and crops, covering everything from subtropical desert and temperate agricultural  land to tropical rainforest. It seems to me you are unlikely to find a data base that covers them all.
Michael Walsby
<meteorology><agriculture>
What data of agricultural yield to use for India?
Manmeet Singh
# What data of agricultural yield to use for India?

(Asked by: username_0 on 2019-10-18)

I am trying to understand the role of various meteorological factors on agricultural yield over India. Is there a dataset available directly or an indirect proxy of agricultural data, maybe satellite, if any product is there

**Tags:** meteorology, agriculture

> **Comments:**
> **username_1**: India is a huge country, with a wide variety of climates and crops, covering everything from subtropical desert and temperate agricultural  land to tropical rainforest. It seems to me you are unlikely to find a data base that covers them all.  

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## 2 answers

### Answer by username_2 on 2020-03-16. Score: 4

In case you cannot access official local data, I recommend the use of the IFPRI resource [MapSPAM2010](https://www.mapspam.info/).
Quoting the description of the dataset:

> Using a variety of inputs, IFPRI's Spatial Production Allocation Model (SPAM) uses a cross-entropy approach to make plausible estimates of crop distribution within disaggregated units. Moving the data from coarser units such as countries and sub-national provinces, to finer units such as grid cells, reveals spatial patterns of crop performance, creating a global grid-scape at the confluence between geography and agricultural production systems. Improving spatial understanding of crop production systems allows policymakers and donors to better target agricultural and rural development policies and investments, increasing food security and growth with minimal environmental impacts.

The dataset makes available data about agricultural variables for 42 crops at 10km resolution for the whole world. More info are available in the [MapSPAM website](https://www.mapspam.info/data/), while the full dataset is available [here](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/PRFF8V).

**Reference**: International Food Policy Research Institute, 2019, "Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 1.1", <https://doi.org/10.7910/DVN/PRFF8V>, Harvard Dataverse, V3

You can download the global rasters and then clip the area you are interested in and make your own estimates regarding specific crops and production technologies.

The information in this dataset, would help you to set up a relatively simple crop model, such as the [FAO AquaCrop](http://www.fao.org/aquacrop/en/). This system, even with a approximative calibration, could help you to understand some of the impacts of the

> meteorological factors on the agricultural yields.


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### Answer by username_3 on 2020-03-16. Score: 2

You can definitely try to use [Earthstat.org](http://www.earthstat.org/) and try to recalculate these spatial values with data from [FAOSTAT](http://www.fao.org/faostat/en/). Other possible data source could be [Agroecological zones](http://www.fao.org/nr/gaez/en/). Hope that will help!


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