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#cropland — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #cropland, aggregated by home.social.

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  1. GMIA-NEXT - Next-Generation Global Map of Irrigated Areas |
    --
    doi.org/10.21203/rs.3.rs-10085 <-- shared paper
    --
    zenodo.org/records/17627111 <-- shared open data
    --
    H/T @kyle Davis
    “Irrigation plays a critical role in global food production and climate adaptation and exercises profound influence over humanity's water use. Yet despite its critical importance, there is a persistent lack of understanding of fine-scale irrigation patterns across the planet, knowledge which is essential for informing global food security and sustainability targets. Utilizing either statistical downscaling or remote sensing approaches, existing global irrigation datasets are constrained by coarse spatial resolutions, a lack of timeliness, or varying robustness and reliability. To address this gap, here [they] integrate[d] multi-source Earth observation and environmental datasets and use[d] machine learning to develop a medium-resolution (30 metre) global irrigated area dataset for the 2023/24 growing season. Within existing cropland extent, we leverage a newly compiled set of georeferenced irrigated (N=230,683) and non-irrigated (N=153,194) ground-truth points and integrate seasonal vegetation metrics derived from Landsat 8/9 imagery with agroecological-zone information and hydroclimatic and topographic variables. [They] subsequently develop and evaluate two machine-learning frameworks, a continental Agro-Ecological Zone (AEZ) tile-based framework and a continental-scale framework, and apply the best-performing approach for each continent. Evaluation using held-out test samples yielded a global accuracy of 80.5 ± 2.1%. The resulting maps were also validated against independent global and national irrigation datasets and statistics, demonstrating broad agreement in the spatial distribution of irrigated areas. This approach is robust and reliable because it is built on a harmonized global ground-truth database, incorporates multiple predictors, and is rigorously validated using independent datasets. All code, ground-truth, and data products are freely and publicly available [link above] and can serve as a robust, scale-neutral, and fully reproducible framework for fine-resolution irrigation mapping. These advances provide the critical and long-needed foundation for near-real-time monitoring and early warning systems, and fine-scale land and water resource management…”
    #IrrigatedAreas #Mapping #GIS #spatial #mapping #spatialanalysis #spatiotemporal #global #irrigation #water #hydrology #hydrography #waterresources #farming #agriculture #opendata #remotesensing #earthobservation #geomorphometry #AI #machinelearning #LLM #model #modeling #WaterManagement #opendata #AgroEcologicalZone #AEZ #cropland #irrigatedareas #foodproduction #wateruse #humanimpacts #EarthObservation #remotesensing #earlywarning #monitoring #FoodandAgricultureOrganizationFAO #FAO
    @FAO - Food and Agriculture Organization

  2. GMIA-NEXT - Next-Generation Global Map of Irrigated Areas |
    --
    doi.org/10.21203/rs.3.rs-10085 <-- shared paper
    --
    zenodo.org/records/17627111 <-- shared open data
    --
    H/T @kyle Davis
    “Irrigation plays a critical role in global food production and climate adaptation and exercises profound influence over humanity's water use. Yet despite its critical importance, there is a persistent lack of understanding of fine-scale irrigation patterns across the planet, knowledge which is essential for informing global food security and sustainability targets. Utilizing either statistical downscaling or remote sensing approaches, existing global irrigation datasets are constrained by coarse spatial resolutions, a lack of timeliness, or varying robustness and reliability. To address this gap, here [they] integrate[d] multi-source Earth observation and environmental datasets and use[d] machine learning to develop a medium-resolution (30 metre) global irrigated area dataset for the 2023/24 growing season. Within existing cropland extent, we leverage a newly compiled set of georeferenced irrigated (N=230,683) and non-irrigated (N=153,194) ground-truth points and integrate seasonal vegetation metrics derived from Landsat 8/9 imagery with agroecological-zone information and hydroclimatic and topographic variables. [They] subsequently develop and evaluate two machine-learning frameworks, a continental Agro-Ecological Zone (AEZ) tile-based framework and a continental-scale framework, and apply the best-performing approach for each continent. Evaluation using held-out test samples yielded a global accuracy of 80.5 ± 2.1%. The resulting maps were also validated against independent global and national irrigation datasets and statistics, demonstrating broad agreement in the spatial distribution of irrigated areas. This approach is robust and reliable because it is built on a harmonized global ground-truth database, incorporates multiple predictors, and is rigorously validated using independent datasets. All code, ground-truth, and data products are freely and publicly available [link above] and can serve as a robust, scale-neutral, and fully reproducible framework for fine-resolution irrigation mapping. These advances provide the critical and long-needed foundation for near-real-time monitoring and early warning systems, and fine-scale land and water resource management…”

    @FAO - Food and Agriculture Organization

  3. Optical, Radar, And Hybrid Indices To Detect Farming Practices In Europe
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    “HIGHLIGHTS:
    • [they] compare[d] Sentinel-1 and Sentinel-2 time series to detect farming practices.
    • HyBRIS index is introduced, temporally weighting BSI and VH/VV into a daily index.
    • Time-series minima and maxima are used to predict sowing, harvest, and tillage.
    • Validation is performed across several years, crop types, and European locations.
    • Phenology detection is improved compared to HRL-Cropland.
    ABSTRACT: Arable farming practices dictate both crop cycles and soil dynamics, and are central to agriculture's environmental impact and its mitigation. Sowing and harvesting mark the beginning and end of the growing season, while tillage modifies soil structure during the dormant period. Although well-established methods exist for delineating the growing season using phenology and optical data, the detection of farming practices, particularly tillage, remains underexplored. This study investigates the strengths of radar and optical data to retrieve sowing, harvest, and tillage dates at the field level, and proposes a novel Hybrid Bare Soil Radar Index (HyBRIS). Based on Sentinel-1 and Sentinel-2, HyBRIS merges optical and radar data into a single index using a temporally weighted mean. Local minima and maxima of the time series are used to detect farming practices across European sites. Validation is carried out against a reference dataset comprising 238 fields in 11 EU countries, including 462 sowing, 374 harvest, and 388 tillage events covering more than 40 crop types over 8 years. Compared to the Copernicus High Resolution Layer Croplands product (HRL-Cropland), the proposed method based on HyBRIS time series improved sowing and harvest dates detection (MAE 26 and 23 days, respectively). Additionally, this method enabled tillage dates estimation during dormant periods (MAE = 28 days), but tended to overestimate the number of tillage events (producer's accuracy = 97%, user's accuracy = 70%). Incorporating soil moisture data is advised for reducing false positives. The results highlight the potential of optical, radar, and hybrid indices for monitoring agricultural management and supporting environmental stewardship…”
    #Sowing #Harvest #tillage #tillagedetection #cropland #CroplandManagement #remotesensing #earthobservation #sentinel #Copernicus #cropland #satellite #optical #radar #sensor #landuse #landcover #landsurface #phenology #agricultural #monitoring #GIS #spatial #mapping #spatialanalysis #spatiotemporal #arable #farming #agriculture #soil #substrate #environment #sustainability #environmentalstewardship #growingseason #Europe #region #model #modeling

  4. Optical, Radar, And Hybrid Indices To Detect Farming Practices In Europe
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    “HIGHLIGHTS:
    • [they] compare[d] Sentinel-1 and Sentinel-2 time series to detect farming practices.
    • HyBRIS index is introduced, temporally weighting BSI and VH/VV into a daily index.
    • Time-series minima and maxima are used to predict sowing, harvest, and tillage.
    • Validation is performed across several years, crop types, and European locations.
    • Phenology detection is improved compared to HRL-Cropland.
    ABSTRACT: Arable farming practices dictate both crop cycles and soil dynamics, and are central to agriculture's environmental impact and its mitigation. Sowing and harvesting mark the beginning and end of the growing season, while tillage modifies soil structure during the dormant period. Although well-established methods exist for delineating the growing season using phenology and optical data, the detection of farming practices, particularly tillage, remains underexplored. This study investigates the strengths of radar and optical data to retrieve sowing, harvest, and tillage dates at the field level, and proposes a novel Hybrid Bare Soil Radar Index (HyBRIS). Based on Sentinel-1 and Sentinel-2, HyBRIS merges optical and radar data into a single index using a temporally weighted mean. Local minima and maxima of the time series are used to detect farming practices across European sites. Validation is carried out against a reference dataset comprising 238 fields in 11 EU countries, including 462 sowing, 374 harvest, and 388 tillage events covering more than 40 crop types over 8 years. Compared to the Copernicus High Resolution Layer Croplands product (HRL-Cropland), the proposed method based on HyBRIS time series improved sowing and harvest dates detection (MAE 26 and 23 days, respectively). Additionally, this method enabled tillage dates estimation during dormant periods (MAE = 28 days), but tended to overestimate the number of tillage events (producer's accuracy = 97%, user's accuracy = 70%). Incorporating soil moisture data is advised for reducing false positives. The results highlight the potential of optical, radar, and hybrid indices for monitoring agricultural management and supporting environmental stewardship…”

  5. 🌱 #EvoLand C5 candidate prototype develops high-resolution Gross Primary Productivity data (10-daily, 10 m) for #grassland and #cropland across Europe.

    🌡️ A key improvement is the refined drought and temperature stress component, which better captures the impact of extreme weather on vegetation productivity.

    🌐 Explore our website and see how to connect with the Results Portal and uncover more about C5 evo-land.eu/results-portal/

    @EU_HaDEA @EU_ENV @DLR @cnes

  6. "Fertilizers that shed #microplastics are increasingly spreading on America’s #cropland, research shows, raising new worry about the soil contamination and safety of the US food supply.

    There are many types of slow release fertilizers, including those that are encapsulated with biodegradable materials, Salehi said. However, the plastic versions work well so the industry for now seems to be sticking with them, she added."

    theguardian.com/us-news/2025/m

  7. "Fertilizers that shed #microplastics are increasingly spreading on America’s #cropland, research shows, raising new worry about the soil contamination and safety of the US food supply.

    There are many types of slow release fertilizers, including those that are encapsulated with biodegradable materials, Salehi said. However, the plastic versions work well so the industry for now seems to be sticking with them, she added."

    theguardian.com/us-news/2025/m

  8. #Agriculture is a key driver of #landuse change and terrestrial #carbon & biodiversity loss. But its environment #footprint can be reduced by sustained #productivity growth: Globally, historic crop improvement (1961-2015) resulted net in less #cropland expansion, lower GHG #emissions, and more plant & animal species being saved from extinction (plus the higher #yields generally lowered commodity prices of staple crops): doi.org/10.1073/pnas.240483912 #biodiversity #foodsecurity

  9. The #genocide committed by #Israel in #Gaza continues.

    Here's what "Deliberately inflicting on the group conditions of life calculated to bring about its physical destruction in whole or in part" means, there:

    • 1.9 million people displaced, 90% of the population

    • 84% of Gaza now under evacuation orders

    • 745,000 people facing emergency levels of food insecurity, including 495,000 facing an extreme lack (#famine).

    • 995,000 people suffering from acute #respiratory #infections, 577,000 with acute watery diarrhea

    • 20 out of 36 #hospitals out of service

    • 21 more military strikes against schools that serve as shelters, since July 1st; as of today, 85% of all school buildings directly hit or damaged

    • 485 #health workers killed, with a further 283 #aid workers

    • 65% of the road network damaged

    • In August, one third of all humanitarian #missions applied for from Israel have been declined, so far

    • 66% of all #cropland and 33% of the #greenhouse area are damaged, 60-70% of all #livestock has been killed or prematurely slaughtered

    This is what you get when a single nation like the #US has the power to protect the openly genocidal government of #Israel under #Netanyahu from any consequences.

    You might consider donating to #UNICEF, to save toddlers and kids in #Gaza, rather than seeing your hard-earned money burned for video clips in support of #KamalaHarris - who nodded through another $20 billion weapons package for Israel, only days ago.

    help.unicef.org/ob/donate-to-c

    ochaopt.org/content/reported-i
    ochaopt.org/content/humanitari

    wikiless.org/wiki/Genocide_Con

  10. The #genocide committed by #Israel in #Gaza seems to be "unreal" and abstract for many.

    Here's what "Deliberately inflicting on the group conditions of life calculated to bring about its physical destruction in whole or in part" means, there:

    • 1.9 million people displaced, 90% of the population

    • 745,000 people facing emergency levels of food insecurity, including 495,000 facing an extreme lack (#famine).

    • 995,000 people suffering from acute #respiratory #infections, 577,000 with acute watery diarrhea

    • 20 out of 36 #hospitals out of service

    • 485 #health workers killed, with a further 283 #aid workers

    • 65% of the road network damaged

    • In July, 30% of all humanitarian #missions applied for from Israel have been declined, or blocked or delayed on the ground

    • 66% of all #cropland and 33% of the #greenhouse area are damaged, 60-70% of all #livestock has been killed or prematurely slaughtered

    This is what you get when a single nation like the #US has the power to protect the openly genocidal government of #Israel under #Netanyahu from any consequences.

    You might consider donating to #UNICEF, to save toddlers and kids in #Gaza, rather than seeing your hard-earned money burned for video clips in support of #KamalaHarris.

    help.unicef.org/ob/donate-to-c

    ochaopt.org/content/reported-i

    wikiless.org/wiki/Genocide_Con

  11. While feral domesticated cats are a big problem in a lot of places (including NYC) and need vigorous #TNVR / #TNR or relocation, #WildFelines need to be protected!

    In #Brazil, #conservationists try to save one of the world’s most #endangered #cats

    by Sarah Brown on 13 June 2024

    via @mongabay

    "- #Muñoa’s #Pampas cat, a small wild feline, is endemic to the Pampas grasslands that sprawl over southern Brazil, #Uruguay and northeastern #Argentina.

    "- With fewer than 100 individuals left in the wild, experts call Muñoa’s pampas cat one of the most endangered felines in the world and warn it go extinct within 10 years as its natural habitat is cleared for #cropland.

    - #Conservation plans to save the species include switching from #monocultures to extensive ranching that preserves the natural #grasslands, creating a #captivebreeding program, and developing a trinational conservation agreement.

    "- Recent #floods in the Brazilian state of #RioGrandeDoSul, where many Muñoa’s Pampas cat sightings have been recorded, have currently halted all local #conservation efforts, putting the future of this feline at risk."

    Read more:
    news.mongabay.com/2024/06/in-b

    #EndangeredSpecies #SouthAmerica #ConservationEfforts #Biodiversity

  12. While feral domesticated cats are a big problem in a lot of places (including NYC) and need vigorous #TNVR / #TNR or relocation, #WildFelines need to be protected!

    In #Brazil, #conservationists try to save one of the world’s most #endangered #cats

    by Sarah Brown on 13 June 2024

    via @mongabay

    "- #Muñoa’s #Pampas cat, a small wild feline, is endemic to the Pampas grasslands that sprawl over southern Brazil, #Uruguay and northeastern #Argentina.

    "- With fewer than 100 individuals left in the wild, experts call Muñoa’s pampas cat one of the most endangered felines in the world and warn it go extinct within 10 years as its natural habitat is cleared for #cropland.

    - #Conservation plans to save the species include switching from #monocultures to extensive ranching that preserves the natural #grasslands, creating a #captivebreeding program, and developing a trinational conservation agreement.

    "- Recent #floods in the Brazilian state of #RioGrandeDoSul, where many Muñoa’s Pampas cat sightings have been recorded, have currently halted all local #conservation efforts, putting the future of this feline at risk."

    Read more:
    news.mongabay.com/2024/06/in-b

    #EndangeredSpecies #SouthAmerica #ConservationEfforts #Biodiversity

  13. Global evaluation of human-bird coexistence challenges: 1. impact of birds on crop production, 2. effect of interventions on #crop losses, 3. perceptions of #birds by stakeholders, producing a map of #cropland areas with win-win potential #PLOSBiology plos.io/3JMeQqt

  14. The Landsat-Derived Global Rainfed and Irrigated-Cropland Product (LGRIP) provides high resolution, #global #cropland data to assist and address #food and #water #security issues of the twenty-first century.

    As an extension of the Global Food Security-support Analysis Data ( #GFSAD ) project, LGRIP maps the world’s #agricultural lands by dividing them into #irrigated and #rainfed croplands, and calculates irrigated and rainfed areas for every country in the world.

    LGRIP data are produced using Landsat 8 time-series satellite sensor data for the 2014-2017 time period to create a nominal 2015 product.

    Each LGRIP 30 meter resolution GeoTIFF file contains a contains a layer that identifies areas of rainfed cropland (cropland areas that are purely dependent on direct precipitation), irrigated cropland (cropland that had at least one irrigation during the crop growing period), non-cropland, and water bodies over a 10° by 10° area, as well as an accuracy assessment of the product. A low-resolution browse image is also available.

    #DAAC - #LGRIP30

    lpdaac.usgs.gov/products/lgrip