#cropland — Public Fediverse posts

Identifying Agricultural Consumptive-Use Patterns To Support Adaptive Water Management In California’s Santa Clara Valley Via Remote Sensing And Machine Learning
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https://doi.org/10.1371/journal.pwat.0000416 <-- shared paper
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H/T @Guillaume Wright | Executive Editor, PLOS
“💧 With drought [and high temperatures] gripping many areas of the world right now... [the H/T] wanted to highlight a new paper in PLOS Water this week with a very timely focus on hydroclimatic stresses and what can be done to mitigate this through water management practices when it comes to agriculture.
[The authors] investigate[d] adaptive water management practices in California’s Santa Clara Valley via remote sensing and machine learning techniques. They [found] good evidence for use of customized agricultural water-management plans for irrigation monitoring, conservation planning, and adaptive water management in groundwater-dependent regions such as is found in California…”
#GIS #spatial #mapping #California #SantaClara #SantaClaraValley #custom #watermanagement #practices #waterresources #agriculture #remotesensing #spatialanalysis #machinelearning #earthobservation #AI #planning #wateruse #efficiency #water #hydrology #irrigation #conservation #adaptivewatermanagement #model #modeling #drought #extremeweather #hydroclimate #stress #crop #cropland #evapotranspiration #ET #NDVI #PRISM #precipitation #rainfall #watermanagementplan #groundwater

Can Himalayan Crops Help Secure The Future Of Food?
(Exploring how genomic diversity, traditional crops, and regional cooperation can strengthen climate resilience and food security across the Hindu Kush Himalaya [HKH])
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https://substack.com/home/post/p-207443153 <-- shared technical post
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https://openlibrary.substack.com/ <-- shared Substack, “Open Library on Green Economy”
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https://adaptationwithoutborders.org/knowledge-base/adaptation-without-borders/shifting-cooperation-in-the-hindu-kush-himalaya/ <-- shared technical article, “Shifting cooperation in the [HKH]”
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https://www.icimod.org/who-we-are/the-hindu-kush-himalaya/ <-- background on the HKH, International Centre for Integrated Mountain Development (ICIMOD)
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https://doi.org/10.48130/cas-0026-0003 <-- shared paper
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[not at all my technical area - but fascinating, including the spatial component #alldataisspatial]
H/T @jeevan Labh
“As we navigate the intensifying impacts of the 2026 El Niño, the vulnerability of our global food systems require careful evaluation. With this year’s erratic weather patterns driving severe, prolonged droughts in some regions and unseasonal, devastating floods in others, the climate crisis is proving that it is not a distant threat; it is happening right now, in our fields and on our plates.
This crisis is more visible in the Hindu Kush Himalaya (HKH) region. Often called the “water tower of Asia,” this vast mountain range sustains 240 million people across eight countries and provides essential ecosystem services to nearly 2 billion people downstream. When glaciers melt at accelerated rates and monsoons become unpredictable, the narrative often focuses solely on the mountain communities. But the truth is much broader: a disrupted harvest in the high hills of Nepal or Bhutan creates a domino effect. It leads to displaced populations, reduced agricultural output flowing into the Indus, Ganges, and Brahmaputra basins, and ultimately, skyrocketing food prices for families living in the sprawling downstream plains.
The climate crisis respects no borders and recognizes no difference between altitude and sea level. Because this problem affects us all, the solution must protect us all. Fortunately, the means to overcome this challenge are already in our hands, locked within the ancient seeds of the mountains…”
## #GreenEconomy #economy #monoculture #agriculture #farming #crop #cropland #HinduKushHimalaya #himalaya #mountain #HKH #spatial #mapping #elevation #climatechange #foodsecurity #food #extremeweather #weather #ElNiño #drought #rainfall #precipitation #snowmelt #climatecrisis #mountainrange #water #hydrology #ecosystem #glacier #moonsoons #community #harvest #Nepal #Bhutan #watersheds #watersecurity #risk #hazard #waterresources #genes #genomicdiversity #cooperation

GMIA-NEXT - Next-Generation Global Map of Irrigated Areas |
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https://doi.org/10.21203/rs.3.rs-10085674/v1 <-- shared paper
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https://zenodo.org/records/17627111 <-- shared open data
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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