Using remote sensing to calculate plant available nitrogen from industrial hog CAFOs in North Carolina at the sprayfield and sub-watershed scales

Duplin county, NC has the highest hog-population density of any county in the United States. Liquid manure from industrial-sized hog concentrated animal feeding operations (CAFOs) is stored in open-air lagoons and sprayed onto sprayfields as fertilizer. Hog CAFOs are regulated by the NC Department of Environment and Natural Resources (DENR) based on their ability to have nutrient management plans (NMPs) that have balanced plant available nitrogen (PAN) so that the estimated portion of nitrogen that remains available for crops to use after irrigation is absorbed. Objectives of this research are to quantify the difference in permitted PAN between CAFO point and sprayfield area locations at two sub-watershed scales in Duplin county by conducting a review of all 485 active CAFOs and creating a sprayfield spatial database. Second, a new method incorporating remote sensing data identifies annual PAN for crops on sprayfields and two sub-watershed scales in Duplin county between 2010-2014

Assessment of methods of acquiring analyzing, and reporting crop production statistics, volume 4

There are no author-identified significant results in this report

Program on stimulating operational private sector use of Earth observation satellite information

Ideas for new businesses specializing in using remote sensing and computerized spatial data systems were developd. Each such business serves as an 'information middleman', buying raw satellite or aircraft imagery, processing these data, combining them in a computer system with customer-specific information, and marketing the resulting information products. Examples of the businesses the project designed are: (1) an agricultural facility site evaluation firm; (2) a mass media grocery price and supply analyst and forecaster; (3) a management service for privately held woodlots; (4) a brokerage for insulation and roofing contractors, based on infrared imagery; (5) an expanded real estate information service. In addition, more than twenty-five other commercially attractive ideas in agribusiness, forestry, mining, real estate, urban planning and redevelopment, and consumer information were created. The commercial feasibility of the five business was assessed. This assessment included market surveys, revenue projections, cost analyses, and profitability studies. The results show that there are large and enthusiastic markets willing to pay for the services these businesses offer, and that the businesses could operate profitably

Advanced Soil Moisture Predictive Methodology in the Maize Cultivation Region using Hybrid Machine Learning Algorithms

The moisture level in the soil in which maize is grown is crucial to the plant's health and production. And over 60% of India's maize cultivation comes from the states of South India. Therefore, forecasting the soil moisture of maize will emerge as a crucial factor for regulating the cultivation of maize crops with optimal irrigation. In light of this, this research provides a unique Improved Hybridized Machine Learning (IHML) model, which combines and optimizes several ML models (base learners-BL). The convergence rate of all the considered BL approaches and the preciseness of the proposed approach significantly enhances the process of determining the appropriate parameters to attain the desirable outcome. Consequently, IHML contributes to an improvement in the accuracy of the overall forecast. This research collects data from districts in South India that are primarily committed to maize agriculture to develop a model. The correlation evaluations served as the basis for the model's framework and the parameter selection. This research compares the outcomes of BL models to the IHML model in depth to ensure the model's accuracy. Results reveal that the IHML performs exceptionally well in forecasting soil moisture, comprising Correlation Coefficient (R2) of 0.9762, Root Mean Square Error (RMSE) of 0.293, and Mean Absolute Error (MAE) of 0.731 at a depth of 10 cm. Conceptual IHML models could be used to make smart farming and precise irrigation much better

SPATIAL-TEMPORAL VARIATION OF THE DENSITY AND DISTRIBUTION OF STINK BUGS (HETEROPTERA: PENTATOMIDAE) IN COTTON, GOSSYPIUM HIRSUTUM (L.), AS PART OF A DIVERSE AGROECOSYSTEM

Studies were conducted during 2007 and 2008 to investigate the distribution and density of phytophagous stink bugs and boll injury in cotton as part of a variable farmscape. The goals of this research were to: (1) compare and contrast sampling techniques and correlate the density of stink bugs and associated internal boll injury with measurements of crop phenology, (2) establish the spatial and temporal distributions of stink bugs and boll injury on a whole-field scale, and (3) determine the density of stink bugs and boll injury along field margins as influenced by adjacent habitats and crops. The ground cloth was the most efficient method to directly sample stink bugs. Monitoring bolls for internal injury was the more sensitive method to detect the presence of stink bugs than the ground cloth or sweep net. The density of adult stink bugs was positively correlated to plant height and normalized difference vegetative index (NDVI). The density of bugs and boll injury were significantly greater in grids located along the periphery of fields than in grids located near the center of the fields. Along field margins, the densities of stink bugs were greatest on the first row and decreased as the distance towards the interior of the cotton field increased. Also, density of stink bugs and boll injury were greatest in cotton adjacent to soybean and peanut fields. These results demonstrate that spatial and temporal variation exists in populations of stink bugs and boll injury along field margins and within fields, and can vary significantly based on the adjacent crop

Spatiotemporal analysis of gapfilled high spatial resolution time series for crop monitoring.

[ES] La obtención de mapas fiables de clasificación de cultivos es importante para muchas aplicaciones agrícolas, como el monitoreo de los campos y la seguridad alimentaria. Hoy en día existen distintas bases de datos de cobertura terrestre con diferentes escalas espaciales y temporales cubriendo diferentes regiones terrestres (por ejemplo, Corine Land cover (CORINE) en Europa o Cropland Data Layer (CDL) en Estados Unidos (EE.UU.)). Sin embargo, estas bases de datos son mapas históricos y por lo tanto no reflejan los estados fenológicos actuales de los cultivos. Normalmente estos mapas requieren un tiempo específico (anual) para generarse basándose en las diferentes fenologías de cada cultivo. Los objetivos de este trabajo son dos: 1- analizar la distribución espacial de los cultivos a diferentes regiones espaciales para identificar las áreas más representativas. 2- analizar el rango temporal utilizado para acelerar la generación de mapas de clasificación. El análisis se realiza sobre el contiguo de Estados Unidos (CONUS, de sus siglas en inglés) en 2019. Para abordar estos objetivos, se utilizan diferentes fuentes de datos. La capa CDL, una base de datos robusta y completa de mapas de cultivo en el CONUS, que proporciona datos anuales de cobertura terrestre rasterizados y georeferenciados. Así como, datos multiespectrales a 30 metros de resolución espacial, preprocesados para rellenar los posibles huecos debido a la presencia de nubes y aerosoles en los datos. Este conjunto de datos ha sido generado por la fusión de sensores Landsat y Moderate Resolution Imaging Spectroradiometer (MODIS). Para procesar tal elevada cantidad de datos se utilizará Google Earth Engine (GEE), que es una aplicación que procesa la información en la nube y está especializada en el procesamiento geoespacial. GEE se puede utilizar para obtener mapas de cultivos a nivel mundial, pero requiere algoritmos eficientes. En este estudio se analizarán diferentes algoritmos de aprendizaje de máquina (machine learning) para analizar la posible aceleración de la obtención de los mapas de clasificación de cultivo. En GEE hay diferentes tipos de algoritmos de clasificación disponibles, desde simples árboles de decisión (decision trees) hasta algoritmos más complejos como máquinas de vectores soporte (SVM) o redes neuronales (neural networks). Este estudio presentará los primeros resultados para la generación de mapas de clasificación de cultivos utilizando la menor cantidad posible de información, a nivel temporal, con una resolución espacial de 30 metros.[EN] Reliable crop classification maps are important for many agricultural applications, such as field monitoring and food security. Nowadays there are already several crop cover databases at different scales and temporal resolutions for different parts of the world (e. g. Corine Land cover in Europe (CORINE) or Cropland Data Layer (CDL) in the United States (US)). However, these databases are historical crop cover maps and hence do not reflect the actual crops on the ground. Usually, these maps require a specific time (annually) to be generated based on the diversity of the different crop phenologies. The aims of this work are two: 1- analyzing the multi-scale spatial crop distribution to identify the most representative areas. 2- analyzing the temporal range used to generate crop cover maps to build maps promptly. The analysis is done over the contiguous US (CONUS) in 2019. To address these objectives, different types of data are used. The CDL, a robust and complete cropland mapping in the CONUS, which provides annual land cover data raster geo-referenced. And, multispectral high-resolution gap-filled data at 30 meter spatial resolution used to avoid the presence of clouds and aerosols in the data. This dataset has been generated by the fusion of Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS). To process this large amount of data will be used Google Earth Engine (GEE) which is a cloud-based application specialized in geospatial processing. GEE can be used to map crops globally, but it requires efficient algorithms. In this study, different machine learning algorithms will be analyzed to generate the promptest classification crop maps. Several options are available in GEE from simple decision trees to more complex algorithms like support vector machines or neural networks. This study will present the first results and the potential to generate crop classification maps using as less possible temporal range information at 30 meters spatial resolution.Rajadel Lambistos, C. (2020). Análisis espaciotemporal de series temporales sin huecos de alta resolución espacial. Universitat Politècnica de València. http://hdl.handle.net/10251/155879TFG

Geology and Wine in Missouri: Spatial Analysis of Terroir Using a Geographic Information System and Remote Sensing

The concept of terroir, based on the French word meaning \u27sense of place,\u27 suggests that flavor and quality of wine is associated with certain physical characteristics of the earth as well as viticulture and viniculture practices. The physical characteristics of terroir include soil, geology, topography, and climate. Designated American Viticultural Areas (AVAs) are discrete appellations in the United States that have well-established and historic viticultural practices, but the relationship between terroir and AVA appellations is complex. Wineries are a growing industry in the United States and the use of a GIS and remote sensing has proved useful in many other studies on terroir and in consulting vineyard owners on management practices. This study examines the relationships between terroir and viticultural areas in Missouri. USDA National Agriculture Imagery Program (NAIP) aerial photographs and soil maps, USGS digital elevation models, and Missouri Division of Geology and Land Survey geologic maps are used to develop a Missouri vineyard geodatabases for four study areas. These data are used to create suitability maps for viticulture regions, describe the physical characteristics of vineyards in these four Missouri wine regions, and finally describe the terroir of each region and propose a new AVA appellation be created in the Ste. Genevieve wine-region of Missouri

Understanding and Predicting Nematode Damage on Soybean using Spatially Weighted Analysis

Aerial imagery offers great potential as a predictive scouting method and could allow growers to better understand crop performance over time. Evidence suggests that the seed treatments fluopyram and abamectin result in decreased reproduction and root galling by Meloidogyne incognita, but yield protection in fields with higher or different nematode pressure is unclear. The objective of this work was to determine the efficacy of these seed treatments compared to 1,3-dichloropropene (1,3-D) applied site-specifically and then predict where these might best be applied to other fields. In a soybean field infested with M. incognita, apparent electrical conductivity was highly correlated with sand content, and treatments were applied the total length of the field, across two soil textural zones. Fluopyram and abamectin seed treatments were compared to seeds without a nematicide seed treatment (control) and seeds without a nematicide seed treatment but planted within 1,3-D treated areas. Historical satellite images, normalized difference vegetation index (NDVI) and near infrared (NIR), from Sentinel-2 at 10-meter resolutions were compared to yield to determine if correlations with crop performance were evident over time. In 2016, treatment yields were not significant by zone, but yield was greater in the 1,3-D strips than all other treatments, while fluopyram and abamectin were not different than strips lacking nematicide (P=0.001). In 2017, 1,3-D strips had higher yield than all other treatments in both zones except for residual 1,3-D treatments that were applied in 2016 in Zone 2 (P=0.01). Fluopyram, abamectin, and the control treatments were not significantly different in Zone 1. Treatment effects for all treatments differed between the two textural zones (P=0.01). The distribution of M. incognita at harvest was uniformly distributed by treatments (P=0.08), suggesting that 1,3-D could be used as a two-year control and would be economically beneficial as a whole-field application when a susceptible soybean is planted. In 2017, NDVI and NIR observations were clustered, meaning that data are significantly positive for local spatial autocorrelation (P\u3c0.05). Seven surrounding fields were further observed and 100% of analyses were clustered using NDVI and NIR images from multiple snapshots throughout the 2017 growing season. Initial analyses indicated correlations with yield, suggesting opportunities for prediction and that site-specific application of 1,3-D in these fields might be beneficial when susceptible soybean varieties are planted

The economic value of remote sensing of earth resources from space: An ERTS overview and the value of continuity of service. Volume 3: Intensive use of living resources: Agriculture. Part 1: Overview

Potential economic benefits obtainable from a state-of-the-art ERS system in the resource area of intensive use of living resources, agriculture, are studied. A spectrum of equal capability (cost saving), increased capability, and new capability benefits are quantified. These benefits are estimated via ECON developed models of the agricultural marketplace and include benefits of improved production and distribution of agricultural crops. It is shown that increased capability benefits and new capability benefits result from a reduction of losses due to disease and insect infestation given ERS's capability to distinguish crop vigor and from the improvement in world trade negotiations given ERS's worldwide surveying capability

Annual variation of bare arable soil areas on a global scale

Wydział Nauk Geograficznych i GeologicznychArable land around the world has a 12% share of the global land area. This thesis was created as a part of a project aimed at estimation of shortwave radiation reflected from those surfaces according to various scenarios based on the farming methods. This thesis was created as a part of a project aimed at estimation of shortwave radiation reflected from those surfaces according to various scenarios based on the farming methods. Its key element was the estimation of the bare soil area, defined for its spectral properties, as the area of arable land not covered by vegetation on more than 15% on its surface. In conventional agriculture, during the period immediately following the planting of crops, the soil stays bare until the newly planted crops reach defined above share of surface cover. This work focuses on estimating the periods of bare soil that occur after the planting of 13 major crops at the global scale; those selected crops are wheat, maize, barley, sorghum, soybeans, millet, cotton, rapeseed, groundnuts, potato, cassava, rye, and sugar beet. The supplementary objective of the study was to determine which soil groupings, and in what proportions, were bare during those periods. Arable land, divided into extensive agricultural regions located on six continents, was analyzed. The estimation of bare soil acreage was performed based on publicly available spatial datasets including the distribution of arable land in the world, crop calendars containing planting dates and the geographic distribution of crops. The arable land in the world was first divided into agricultural regions inspired by the division proposed by United States Department of Agriculture. For each region, average daily temperatures were used to predict plant growth stages. For each crop within a region, the planting date was used as the beginning of the bare soil period, which ended when it reached a stage where at least 15% of the surface was covered by vegetation. The aggregated periods concerning every crop within any given region resulted in an annual variation of bare soil area. The acreages of soil grouping used in agriculture for any region were then extracted based on the location of arable land and the region’s boundaries.The global annual variation of bare soil area shows that the maximum level occurs around the 140th day of the year (DOY) (middle of May), influenced primarily by the planting of crops occurring in the northern hemisphere. Up to 1.5 million km2of soil surface stays bare at that time. Centered on that maximum is a period of bare soil lasting for almost four months, between the 92nd DOY and the 200th DOY (early April and end of July), when two lesser maxima were observed, of around 900,000 and 700,000 km2, respectively. The equivalent of that period, resulting from planting in the southern hemisphere, starts around the 330th DOY (middle of November) and lasts for about a month, reaching almost 400,000 km2. The other distinguishable episode of bare soil in the southern hemisphere was noted between the 15th and the 25th DOY (second half of January) when its area reached 100,000 km2. Asia is the super region with by far the largest area of arable land and consequently, it sports the highest acreage of bare soil. During the aforementioned maximum in the northern hemisphere occurring around the 140th DOY, the Asian super region contributes around 700,000 km2 of bare soil, which is almost half of the bare soil area for the whole northern hemisphere at that time, with Lithosols, Cambisols, and Gleysols being the major soil groupings that stay bare. In Europe, two distinct periods of bare soil were found; during the first, starting around the 40th DOY (middle of February) and lasting until the 150th DOY (end of May), the steady increase of the bare soil area lasts until the 140th DOY (middle of May) when it reaches almost 500,000 km2, after which a rapid decline was observed. The second, manifesting two and a half months later, lasts between around the 230th and the 290th DOY (middle of August to middle of October), and exceeds 100,000 km2. Chernozems, Cambisols, and Luvisols are dominant soil groupings on arable land in Europe. Similar trends, related to the European bare soil areas, were found in the North American super region, where a period of maximum bare soil area occurs in late spring, and a second period, characterized by a much smaller area, follows the main one three months later. The maxima coincide with the aforementioned ones in Asia and Europe, reaching 300,000 km2 of bare soil around the 140th DOY. Similar to Europe, the second period sports a much smaller bare soil area, short of 30,000 km2. The dominant soil groupings in agricultural use in North America are Kastanozems, Luvisols, and Chernozems. Africa is a super region whose area is divided between both northern and southern hemispheres, which shows in the annual variation of its bare soil area. Three distinct periods were found there, the major one around the middle of a year lasted for about two and a half months, between the 167th and the 230th DOY (middle of June to middle of August) with the bare soil area being up to almost 400,000 km2. The other peak occurs about a month and a half earlier, between the 95th and the 115th DOY (roughly the month of April) and is characterized by a bare soil area exceeding 120,000 km2. The last notable episode of bare soil in Africa manifests itself between the 317th DOY and the 10th day of the following year (middle of November to the middle of January), with the area of soil uncovered by vegetation reaching almost 100,000 km2. Luvisols together with Arenosols, followed by Vertisols, are the most extensively farmed soil groupings in Africa. The majority of arable land in the southern hemisphere is found in the South American super region, which is reflected in the annual variation of bare soil area, which is similar to that of the whole southern hemisphere. The maximum lasts for around two weeks, between the 330th and the 345th DOY (end of November to the middle of December), when almost 500,000 km2 of arable soil is bare. A secondary peak was observed between the 15th and the 30th DOY (second half of January), sporting around 100,000 km2 of bare soil area. Ferrasols is the most commonly farmed soil grouping in the region, followed by Phaozems and Luvisols. In Oceania, the maximum area of bare soil slightly exceeds 25,000 km2 for about two weeks in the first half of June, followed by a rapid decline. A secondary period is characterized by a longer duration but the smaller area, lasting between the 313th and the 14th DOY (middle of November to middle of January) with about 5,000 km2 of arable land which is not covered by vegetation at that time. Luvisols are the dominant soil grouping under cultivation in Oceania, followed by Planosols, Solonetz, and Vertisols. The obtained variations of bare soil areas together with the corresponding share of soil groupings for all regions were used in other work in order to estimate the amount of shortwave radiation reflected from those surfaces according to various scenarios based on the farming methods.Grunty orne stanowią około 12% powierzchni lądów na całym świecie. Niniejsza praca powstała w ramach projektu dążącego do oszacowania ilości promieniowania krótkofalowego odbijanego od tych powierzchni. Kluczowym jej elementem było oszacowanie areału odkrytej gleby, definiowanej ze względu na jej właściwości spektralne, jako powierzchni gruntów ornych niepokrytych roślinnością w stopniu większym niż 15%. W przypadku rolnictwa konwencjonalnego, w okresie bezpośrednio po sianiu lub sadzeniu roślin gleba pozostaje odkryta, dopóki nowo zasiane lub zasadzone rośliny nie osiągną fazy wzrostu powodującej pokrycie powierzchni w wyżej zdefiniowanym stopniu. Praca ta koncentruje się na oszacowaniu okresów kiedy gleba pozostaje odkryta, które występują po sianiu lub sadzeniu 13 głównych upraw w skali globalnej; te wybrane uprawy to pszenica, kukurydza, jęczmień, sorgo, soja, proso, bawełna, rzepak, orzeszki ziemne, ziemniaki, maniok, żyto i burak cukrowy. Celem badania było ustalenie, które główne grupy glebowe (major soil groupings wg definicji FAO–UNESCO) oraz w jakich areałach pozostają odkryte. Przeanalizowane zostały grunty orne podzielone na regiony rolnicze położone na sześciu kontynentach. Oszacowanie areału odkrytej gleby przeprowadzono przy użyciu publicznie dostępnych zbiorów danych przestrzennych, w tym rozmieszczenia gruntów ornych na świecie, geograficznego rozmieszczenia upraw oraz kalendarzy upraw zawierających daty sadzenia. Używane zbiory danych zostały w pierwszej kolejności podzielone na regiony rolnicze zainspirowane podziałem zaproponowanym przez Departament Rolnictwa Stanów Zjednoczonych. Dla każdego z tych regionów zastosowano średnie dzienne temperatury w celu oszacowania etapów wzrostu roślin. Dla każdej uprawy w regionie data sadzenia została wykorzystana jako początek okresu występowania odkrytej gleby, który kończy się, gdy osiągnie etap, w którym gleba zostaje pokryte roślinnością. Zagregowane okresy dotyczące każdej uprawy w danym regionie posłużyły do ustalenia rocznej zmienności powierzchni odkrytej gleby. Areały głównych grup glebowych wykorzystywanych w rolnictwie dla każdego z regionów zostały następnie obliczone na podstawie lokalizacji gruntów ornych i granic regionu. Analizując wszystkie grunty orne na świecie, maksymalny poziom odkrycia występuje około 140 dnia roku (day of year - DOY);(połowa maja), i jest spowodowany przede wszystkim przez sianie oraz sadzenie roślin uprawnych na półkuli północnej. W tym czasie do 1,5 mln km2 powierzchni gruntów ornych nie jest pokryta przez rośliny. Wyżej opisane maksimum występuje podczas okres odsłoniętej gleby trwającego przez prawie cztery miesiące, między 92 DOY a 200 DOY (początek kwietnia a koniec lipca), kiedy zaobserwowano dwa pomniejsze maksima, odpowiednio około 900 000 i 700 000 km2. Odpowiednik tego okresu, wynikający z siania oraz sadzenia na półkuli południowej, zaczyna się około 330 DOY (połowa listopada) i trwa około miesiąca, osiągając prawie 400 000 km2. Inny wyraźnie widoczny okres odkrytej gleby na półkuli południowej odnotowano między 15 a 25 DOY (druga połowa stycznia), kiedy jego powierzchnia osiągnęła 100 000 km2. Azja to kontynent o zdecydowanie największym areale odkrytej gleby wynikający ze zdecydowanie największej powierzchni gruntów ornych. Podczas wspomnianego maksimum na półkuli północnej, występującego około 140 DOY, azjatycki region odpowiada za około 700 000 km2 odkrytej gleby, a więc prawie połowę powierzchni odkrytej gleby dla całej półkuli północnej w tym czasie, z Lithosols, Cambisols i Gleysols jako głównymi grupami gleb, które pozostają odkryte. W Europie znaleziono dwa odrębne okresy odkrytej gleby; podczas pierwszego, rozpoczynającego się około 40 DOY (połowa lutego) i trwającego do 150 DOY (koniec maja), stały wzrost powierzchni odkrytej gleby trwa do 140 DOY (połowa maja), kiedy osiąga ona prawie 500 000 km2, po czym następuje gwałtowny spadek tego areału. Drugi, zaczynający się dwa i pół miesiąca później, trwa od około 230 do 290 DOY (od połowy sierpnia do połowy października) i przekracza 100 000 km2. Chernozems, Cambisols i Luvisols są dominującymi grupami glebowymi na gruntach ornych w Europie. Podobne tendencje jak w przypadku odsłoniętych gleb na kontynencie europejski zanotowano w Ameryce Północnej, w przypadku której okres największej powierzchni odkrytej gleby występuje późną wiosną, a drugi okres, obejmującym znacznie mniejszy areał, następuje trzy miesiące później. Maksymalne wartości występują w podobnym okresie jak w wyżej wymienionych Azji i Europie, osiągając 300 000 km2 odkrytej gleby około 140 DOY. Podobnie jak w Europie, drugi okres charakteryzuje się znacznie mniejszą powierzchnię odkrytej gleby, poniżej 30 000 km2. Dominującymi grupami glebowymi uprawianymi w Ameryki Północnej są Kastanozems, Luvisols i Chernozems. Afryka jest kontynentem zajmującym półkulą północną, jak i południową, co jest odzwierciedlone w rocznym przebiegu areału odkrytej gleby. Wyróżniono tam trzy osobne okresy, największy z nich występuje w połowie roku i trwa około dwóch i pół miesiąca, między 167 a 230 DOY (od połowy czerwca do połowy sierpnia), podczas którego powierzchnia odkrytej gleby osiąga prawie 400 000 km2. Drugi szczyt występuje około półtora miesiąca wcześniej, między 95 a 115 DOY (w kwietniu) i charakteryzuje się areałem odkrytej gleby przekraczającym 120 000 km2. Ostatni znaczący okresy odkrytej gleby w Afryce ustalono między 317 DOY a 10 dniem następnego roku (od połowy listopada do połowy stycznia), przy czym odkryty areał gleby sięga prawie 100 000 km2. Luvisols wraz z Arenosols oraz Vertisols, są najbardziej ekstensywnie uprawianymi grupami glebowymi w Afryce. Roczna zmienność powierzchni odsłoniętej gleby na kontynencie Ameryki Południowej ma podobny przebieg jak w przypadku całej półkuli południowej. Maksimum areału odsłoniętej gleby trwa przez około dwa tygodnie, między 330 a 345 DOY (koniec listopada do połowy grudnia), kiedy prawie 500 000 km2 gruntów ornych pozostaje odkrytych. Drugi szczyt zaobserwowano między 15 a 30 DOY (druga połowa stycznia), w którego trakcie około 100 000 km2 gruntów ornych jest odsłoniętych. Ferrasols są najczęściej uprawianą grupą glebową na kontynencie, a następnie Phaozems i Luvisols. W Oceanii maksymalny areał odkrytej gleby nieznacznie przekracza 25 000 km2 przez okres około dwóch tygodni w pierwszej połowie czerwca, po czym następuje jego gwałtowny spadek. Drugi okres charakteryzuje się dłuższym czasem trwania, ale mniejszym areałem, utrzymującym się od 313 do 14 DOY (od połowy listopada do połowy stycznia) z około 5000 km2 gruntów ornych, które nie są w tym czasie pokryte roślinnością. Luvisols są dominującą grupą glebową pod uprawą w Oceanii, a następnie Planosols, Solonetz i Vertisols.project 2014/13/B/ST10/02111, financed by the Polish National Science Cente