Gridded 5-arcmin, simultaneously farm-size- and crop-specific harvested area for 56 countries

Su, Han; Willaarts, Bárbara; Luna-Gonzalez, Diana; Krol, Maarten S.; Hogeboom, Rick J.

doi:https://doi.org/10.5194/essd-2022-72

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https://doi.org/10.5194/essd-2022-72

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07 Mar 2022

Status: a revised version of this preprint is currently under review for the journal ESSD.

Gridded 5-arcmin, simultaneously farm-size- and crop-specific harvested area for 56 countries

Han Su^1,2, Bárbara Willaarts², Diana Luna-Gonzalez², Maarten S. Krol¹, and Rick J. Hogeboom^1,3 Han Su et al.

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¹Multidisciplinary Water Management group, Faculty of Engineering Technology, University of Twente, Enschede, 7500AE, the Netherlands
²Water Security group, International Institute for Applied Systems Analysis (IIASA), Laxenburg, 2361, Austria
³Water Footprint Network, Enschede, 7522NB, the Netherlands

Received: 20 Feb 2022 – Discussion started: 07 Mar 2022

Abstract. Farms are not homogeneous. Smaller farms generally have different planted crops, yields, agricultural input, and irrigations compared to larger farms. Mapping farm size could facilitate studies to quantify how water availability and climate change affect small and large farms respectively. Given the lack of gridded farm-size specific data, this study aims to develop a global gridded simultaneously farm-size- and crop-specific dataset of harvested area. We achieved it by downscaling a best-available dataset, which collected direct measurements on crop and farm size, using crop maps, cropland extent, and dominant field size distributions for 2010. Uncertainties in crop maps were explicitly considered by using two crop maps separately during downscaling. Due to data availability, our downscaled maps cover 56 countries accounting for half of the global cropland. Based on the two different crop maps, we have one 5-arcmin gridded, simultaneously farm-size- and crop-specific dataset of harvested area for 11 farm sizes, 27 crops, and 2 farming systems and the other one for 11 farm sizes, 42 crops, and 4 farming systems. The downscaled maps show major planted crops and irrigation change along with farm sizes which support previous findings. Validations show well consistencies with observations on farm-size specific oil palm from satellite images, farm-size specific irrigation from household surveys, and previous studies that map farm size but are not crop-specific. We observed some uncertainties at the grid cell level and found conclusions at the country-level are robust to these uncertainties including the uncertainties from the crop maps. Our downscaled maps will help to explicitly include farm size into global agriculture modeling. The source data, code, and downscaled maps are open-access and free available at https://doi.org/10.5281/zenodo.5747616 (Su et al., 2022).

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Han Su et al.

Status: final response (author comments only)

RC1:
'Comment on essd-2022-72', Anonymous Referee #1, 22 Mar 2022

The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2022-72/essd-2022-72-RC1-supplement.pdf

Citation: https://doi.org/10.5194/essd-2022-72-RC1
- AC1:
  'Reply on RC1', Han Su, 31 Mar 2022
  
  We would like to thank you for your comments. We appreciate the time and effort you spent on reviewing. Please find our responses in the supplement.
  
  Citation: https://doi.org/10.5194/essd-2022-72-AC1
  - RC2: 'Reply on AC1', Anonymous Referee #1, 01 Apr 2022
    
    In relation to the author’s response to [Comment] 2, I’m very much impressed by the figure (Fig. The distribution of irrigated, low- and high-input rainfed, and subsistence rainfed farming systems within each farm size according to the SPAM based downscaled map) that the portion of the subsistence rainfed and low-input rainfed farming systems account for more in the smaller farm sizes than in the larger farm sizes. The figure also shows that the portion of the irrigated farming system is more in the smaller farm sizes. Why is the irrigation-equipped area share relatively high in small size farmers? I would be appreciate it if the authors could explain this point. I suspect that this is due to the large number of small size farmers in Asia (in particular India) where water resources are abundant thanks to monsoon rainfalls.
    
    Citation: https://doi.org/10.5194/essd-2022-72-RC2
    
    AC2: 'Reply on RC2', Han Su, 06 Apr 2022
    
    Thank you for your comment. Indeed, a higher portion of irrigated farming system in smaller farms is shown in the figure you refer to, as well as in Fig. 3 in our manuscript this is supported by previous evidence (FAO, 2021; Ricciardi et al., 2020). The inclusion of the 56 countries and exclusion of other countries affect this estimation, but for the 56 countries, the overall higher portion of irrigated area in smaller farms correlates with the level of water scarcity: Fig. 3 in the manuscript indicates that higher portions of smaller farms are located in water-scarce regions as compared to larger farms. In the water-scarce regions, the percentage of the irrigated area could reach on average 40% for small farms. For India, the water scarcity map of Mekonnen and Hoekstra (2016) indicates a large part of India is under water scarcity from January to June, and thus under water scarcity on an annual average. The India agriculture input survey (DAFW, 2022) indicates 47.8% of the crop area belonging to farm size 0-2 ha was irrigated in India during 2011-2012. Thus, water scarcity may partly contribute to the high portion of irrigated areas in Indian small farms. Asian smaller farms also contribute to the higher irrigation portion in another way. In Asian countries including India, previous studies show that independent of regional water scarcity, on average the percentage of irrigated area in small farms is high: over 50% when water is scarce and over 20% when water is not scarce (Ricciardi et al., 2020). This percentage is much higher than that in Europe, Central Asia, Latin America, and Sub-Saharan Africa (Ricciardi et al., 2020). Since a large number of small farms are from Asia, the overall portion of irrigated areas in small farms is high. We will add the above analysis in the next revision.
    
    Reference
    DAFW (2022) Agriculture Census, Input Survey Dashboard, National Tables, All India tables, 2011-2012, Table 2A & 2B. Department of Agriculture & Farmers Welfare, Government of India
    FAO (2021) RuLIS - Rural Livelihoods Information System.
    Mekonnen MM, Hoekstra AY (2016) Four billion people facing severe water scarcity. Science Advances 2. doi:10.1126/sciadv.1500323
    Ricciardi V, Wane A, Sidhu BS, Godde C, Solomon D, McCullough E, Diekmann F, Porciello J, Jain M, Randall N (2020) A scoping review of research funding for small-scale farmers in water scarce regions. Nature Sustainability 3:836-844.
    
    Citation: https://doi.org/10.5194/essd-2022-72-AC2
    
    RC3: 'Reply on AC2', Anonymous Referee #1, 06 Apr 2022
    
    Thank you very much for your clarifications that is convincing. I look forward to see a revised manuscript.
    
    Citation: https://doi.org/10.5194/essd-2022-72-RC3
RC4:
'Comment on essd-2022-72', Anonymous Referee #2, 08 Apr 2022

This study tries to map the global distribution of farm size using data harmonization approach. This is an interesting topic, but there are a few major issues that need to be solved. First, there is a large gap in China, causing an unpleasant blank area in the entire East Asia. I believe China's data can be easily obtained from the annual yearbook or other statistical records, and I would suggest the authors fill this gap. Second, I have concerns about the validation in Lines 220-224. The comparisons are actually a compromise of data inconsistency. What if a different threshold value was used? Do the conclusions change if a different threshold was used? A sensitivity analysis maybe helpful here. Third, language editing is also needed.

Other minor suggestions:

1. Line 119, an extra "and"?

2. The claims in Lines 263-264 were actually not supported by the figure. There is a large drop in the >1000 category in Fig. 3a for the orange and red lines. Please also explain.

3. In line273, I don't know why the author made this claim: "This means the spatial distributions of oil palm production in our downscaled maps and Descals et al. (2020) are similar." The comparisons were about the harvested area, and why and how did the production involved here?

4. Line 328, separately?

Citation: https://doi.org/10.5194/essd-2022-72-RC4
- AC3:
  'Reply on RC4', Han Su, 22 Apr 2022
  
  We would like to thank you for your comments. We appreciate the time and effort you spent on reviewing. Please find our responses in the supplement.
  
  Citation: https://doi.org/10.5194/essd-2022-72-AC3
  - AC4: 'Reply on AC3', Han Su, 22 Apr 2022
    
    Sorry. There seem to be some problems with supplement uploading. Please find our responses below.
    [Comment] This study tries to map the global distribution of farm size using data harmonization approach. This is an interesting topic, but there are a few major issues that need to be solved.
    [Response] Thank you for your comments. These comments enable us to improve our manuscript. We appreciate the time and effort you spent on reviewing. Below are our responses and how we will address them in the next revision.
    [Comment] First, there is a large gap in China, causing an unpleasant blank area in the entire East Asia. I believe China's data can be easily obtained from the annual yearbook or other statistical records, and I would suggest the authors fill this gap.
    [Response] The inclusion of China is our ambition since designing the research, however, data access remains unsolved so far. To include any extra country or region, we need farm-size specific and crop-specific data at the regional level from statistical records. This information for China is not publicly available, which is confirmed by the Statistics Information Service from the National Bureau of Statistics of China after consulting. According to our best knowledge, two databases may provide such data: the microdata of the Third National Agricultural Census in China (NBS, 2022) and the China Rural Household Panel Survey (CRHPS) (SSECZU, 2019). We submitted our data request and discussed with the database manager of the two databases in August 2021 and February 2022 respectively, however, we could not be granted access according to the corresponding current data policy. The data policy might change in the future, and we are prepared to include more countries including China once additional data is available. We would also like to invite scholars, users, and policymakers to update our database together in the future.
    [Comment] Second, I have concerns about the validation in Lines 220-224. The comparisons are actually a compromise of data inconsistency. What if a different threshold value was used? Do the conclusions change if a different threshold was used? A sensitivity analysis maybe helpful here.
    [Response] We agree that a sensitivity analysis would be helpful to understand the comparison here. Besides the current threshold of 25 ha, we also tried 10 ha and 50 ha as thresholds and conducted the same comparison with observations from satellite images. We found the conclusions in Section 3.3 are not sensitive to the choice of threshold. We will add the sensitivity analysis in the next revision.
    [Comment] Third, language editing is also needed.
    [Response] The next revision will receive proofreading from a native speaker.
    
    Other minor suggestions:
    
    [Comment] 1. Line 119, an extra "and"?
    [Response] Yes, this word is redundant and will be removed in the next revision.
    [Comment] 2. The claims in Lines 263-264 were actually not supported by the figure. There is a large drop in the >1000 category in Fig. 3a for the orange and red lines. Please also explain.
    [Response] Thanks for pointing it out; we agree that a more precise formulation is due. The more appropriate claim will be that large farms irrigate to a larger extent than small farms when water is scarce.
    The reason for the drop is that the water scarce area of the >1000 ha farm size is mainly contributed by limited crops from a few regions, at least in our dataset. In this case, the characteristics of these crops and regions have more impact on the overall relationship between water scarcity and irrigation. For example, one of the main contributors to the significant and severe water scarce area of >1000 ha farm size (the orange and red lines) is sugarcane from São Paulo in Brazil. Brazil is the world's largest sugarcane producer and São Paulo account for around 60% of sugarcane production in Brazil (Bordonal et al., 2018; Palludeto et al., 2018). Sugarcane in this area is dominated by >1000 ha farm size (Ricciardi et al., 2018), mainly rainfed (OECD-FAO, 2015; Yu et al., 2020), and under water scarcity (Mekonnen and Hoekstra, 2016). However, water scarcity is not present all year round. The level of water scarcity is low from January to June, which is the tillering phase for sugarcane. During the dry season, sugarcane is usually harvested, during which moisture in sugarcane is relatively low and the sugar is highly concentrated (Kavats et al., 2020). This may help to explain why the large farms in this area are rainfed even though under a certain level of water scarcity.
    In Fig. 3, we do not aim to draw conclusions on irrigation levels for specific farm sizes in absence of further investigation on influencing factors and uncertainties. The reason we have Fig. 3 is to compare it with previous observations. Ricciardi et al. (2020) show that large farms irrigate to a larger extent than small farms when water is scarce. In their study, farms are divided into either small or large farms without further classification, and the status of water scarcity is only classified as the water is scarce (moderate, significant, and severe) or not (low). Plausible thresholds to differentiate small and large farms could be country specific, and range from 1-42 ha for most countries (FAO, 2017, 2019; Khalil et al., 2017). With any threshold within this range, our dataset supports previous observations given that the farm size >1000 ha only contributes to less than 4.5% of water scarce area of large farms, so specific observations for the largest farm size may be spurious and are not emphasized in the paper.
    In the next revision, we will improve the claim, clarify the intention of this analysis, and explain Fig. 3 with more details based on the above response.
    [Comment] 3. In line273, I don't know why the author made this claim: "This means the spatial distributions of oil palm production in our downscaled maps and Descals et al. (2020) are similar." The comparisons were about the harvested area, and why and how did the production involved here?
    [Response] Thanks for pointing it out. The statement indeed is about the harvested area instead of production. We will formulate it unambiguously in the next revision.
    [Comment] 4. Line 328, separately?
    [Response] Yes, this word will be corrected in the next revision.
    
    Reference
    Bordonal RO, Carvalho JLN, Lal R, de Figueiredo EB, de Oliveira BG, La Scala N (2018) Sustainability of sugarcane production in Brazil. A review. Agronomy for Sustainable Development 38. doi:10.1007/s13593-018-0490-x
    FAO (2017) Small family farms data portrait. Basic information document. Methodology and data description. Food and Agriculture Organization of the United Nations, Rome
    FAO (2019) Methodology for computing and monitoring the Sustainable Development Goal indicators 2.3.1 and 2.3.2. FAO Statistics Working Paper Series 18-14. Food and Agriculture Organization of the United Nations, Rome
    Kavats O, Khramov D, Sergieieva K, Vasyliev V (2020) Monitoring of sugarcane harvest in Brazil based on optical and SAR data. Remote Sensing 12:1-26. doi:10.3390/rs12244080
    Khalil CA, Conforti P, Ergin I, Gennari P (2017) Defining small scale food producers to monitor target 2.3 of the 2030 Agenda for Sustainable Development. FAO, Rome
    Mekonnen MM, Hoekstra AY (2016) Four billion people facing severe water scarcity. Science Advances 2. doi:10.1126/sciadv.1500323
    NBS (2022) Micro data, National Bureau of Statistics. https://microdata.stats.gov.cn/ (in Chinese) Accessed 20-April-2022
    OECD-FAO (2015) OECD-FAO Agricultural Outlook 2015-2024. Organisation for Economic Co-operation Development, Food and Agriculture Organization of the United Nations
    Palludeto AWA, Telles TS, Souza RF, de Moura FR (2018) Sugarcane expansion and farmland prices in São Paulo State, Brazil. Agriculture and Food Security 7. doi:10.1186/s40066-017-0141-5
    Ricciardi V, Ramankutty N, Mehrabi Z, Jarvis L, Chookolingo B (2018) An open-access dataset of crop production by farm size from agricultural censuses and surveys. Data Brief 19:1970-1988. doi:10.1016/j.dib.2018.06.057
    Ricciardi V, Wane A, Sidhu BS, Godde C, Solomon D, McCullough E, Diekmann F, Porciello J, Jain M, Randall N (2020) A scoping review of research funding for small-scale farmers in water scarce regions. Nature Sustainability 3:836-844.
    SSECZU (2019) Data access policy of the Chinese Family Database from Zhejiang University. http://ssec.zju.edu.cn/sites/main/template/news.aspx?id=51027 (in Chinese) Accessed 20-April-2022
    Yu Q, You L, Wood-Sichra U, Ru Y, Joglekar AKB, Fritz S, Xiong W, Lu M, Wu W, Yang P (2020) A cultivated planet in 2010 – Part 2: The global gridded agricultural-production maps. Earth System Science Data 12:3545-3572. doi:10.5194/essd-12-3545-2020
    
    Citation: https://doi.org/10.5194/essd-2022-72-AC4
    
    RC5: 'Reply on AC4', Anonymous Referee #2, 27 Apr 2022
    
    Thanks for the explanations. I would like to see the revised manuscript. I also look forward to seeing the updated version of the dataset with more countries included.
    
    Citation: https://doi.org/10.5194/essd-2022-72-RC5

Han Su et al.

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https://doi.org/10.5194/essd-2022-72-supplement

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Gridded 5-arcmin, simultaneously farm-size- and crop-specific harvested area for 56 countries Han Su, Bárbara Willaarts, Diana Luna Gonzalez, Maarten S. Krol, Rick J. Hogeboom https://doi.org/10.5281/zenodo.5747616

Han Su et al.

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Short summary

There are over 608 million farms around the world but they are not the same. We developed high spatial resolution maps telling where small and large farms were located and which crops were planted for 56 countries. We checked the reliability and have the confidence to use them for the country-level and global studies. Our maps will help more studies to easily measure how agriculture policies, water availabilities, and climate change affect small and large farms respectively.


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