The OECD uses a wide range of agri-environmental indicators to monitor effects of the agricultural sector on the environment. However, the only indicator of farmland biodiversity is the farmland bird index, and this is not reported by all member states. Therefore, work is ongoing to design a more general indicator of farmland biodiversity that can be reported by all member states.

There are many challenges in creating an indicator that can be applied across all OECD countries. These countries have very diverse farming systems, land ownership, climate, biophysical conditions, and species pools. In addition, there are big differences in the type and amount of data available with which to calculate an indicator. Some countries already have monitoring programmes, tailored to their specific national needs and priorities. It may be challenging to harmonize reporting across countries, when data are collected in different ways and from different sources.

As a first step, the OECD published in 2023 guidelines for the development of an OECD farmland habitat biodiversity indicator (https://doi.org/10.1787/09d45d55-en). The aim is to calculate an indicator based on all agricultural habitats within a country, both those of high nature value, but also the ordinary and those that are currently of very low value.

In 2024, several countries are testing calculation of the indicator using national data. This presentation will describe the proposed indicator and share experiences from work to calculate the indicator for Norway.

How to cite: Fjellstad, W. and Bayr, U.: An indicator to monitor farmland biodiversity in OECD countries, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21456, https://doi.org/10.5194/egusphere-egu24-21456, 2024.

Agriculture plays a major role in the erosion of biodiversity, which represents one of the exceeded planetary boundaries. In the quest for solution to mitigate impacts of farming systems on biodiversity, it is essential to have tools to assess these impacts. Besides a plethora of indicators using field measurement of abundance or/and species richness of one or several taxa, predictive indicators offer a compromise between feasibility and integration of processes. Such indicators do not require in situ measurements and enable linking the response of biodiversity to drivers like agricultural practices.

Here we review three examples of predictive indicators representing a gradient of complexity regarding the number of input variables on field practices and landscape structure, the number of output variables on biodiversity components, and the model structure. The three indicators are NIVA-Biodiversity, BioSyScan and I-BIO.

NIVA-Biodiversity assesses biodiversity at the landscape and regional level, assessing biodiversity through a global score, without any precision on taxonomic or functional components, based on the percentage of semi-natural habitats (SNH), field size and crop diversity. BioSyScan is calculated at field level and assesses the impacts of field management (e.g. tillage, fertilization, pesticides spraying) and landscape variables (e.g field size and SNH) on soil-dependent species and mobile species. Last, I-BIO considers direct impacts of cropping systems on five taxonomic groups (microorganisms, plants, soil invertebrates, flying invertebrates and vertebrates) and indirect impacts through trophic chains.  It includes more precise variables on field and landscape management than the two other indicators. The three indicators are based on mixed models using linguistic rules “if-then”. While I-BIO is based on the DEXi tool and remains totally qualitative, NIVA-Biodiversity and BioSyScan were designed using the CONTRA aggregation method integrating fuzzy subsets in the decision rules, to mitigate threshold effects and increase transparency. We will highlight the potential use of each indicator using case studies, discuss the pros and cons of each indicator, and present the research needs to ensure their scientific validity.

How to cite: Bockstaller, C., Soulé, E., Dallaporta, B., and Sirami, C.: Assessing impacts of farming systems on biodiversity using predictive indicators: a gradient of complexity, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21429, https://doi.org/10.5194/egusphere-egu24-21429, 2024.

Abstract: Agrobiodiversity, a key principle of agroecology, encompasses crop diversification, offering resilience to climate variability (Ronnie Vernooy, 2022). Increasing crop species diversity within a region could improve agricultural sustainability, but knowledge of the spatiotemporal variation of crop species diversity and how this is related to climatic conditions is limited (Sjulgård, H., et al., 2022). Higher crop diversity may alleviate the effects of heat stress (Degani et al., 2019, Marini et al., 2020) and drought (Bowles et al., 2020, Marini et al., 2020) on crop yields. Therefore, crop diversity will play a crucial role in the functioning of agroecosystems under climate change (Sjulgård, H., et al., 2022). Hence, this study aims to investigate the relation between the spatiotemporal pattern of crop diversity and changing climatic conditions at the district level in India by building relationship between crop diversification and climatic variables. Crop species diversity was estimated using the Shannon Index. Advanced statistical analysis was used to understand the relationship between climatic variables and crop diversity. The outcome will also help the policymaker, researchers, and field practitioners in designing climate-resilient agricultural practices following the principles of agroecology.

Keywords:

Crop diversification, Shannon Index, Climate variables, Risk map, Agrobiodiversity, Agroecology, India.

Figure 1: Schematic representation of the proposed work

How to cite: Kumari, C., Shukla, R., and Gleixner, S.: Understanding spatio-temporal pattern of crop diversification for India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1070, https://doi.org/10.5194/egusphere-egu24-1070, 2024.

In order to fulfil the requirements of the Agriculture Act and the Ordinance on the Assessment of Sustainability, the Swiss Federal Research Centre Agroscope assesses the quantitative and qualitative impacts of agriculture on the environment using regional and farm-related eco-indicators. This is done by the monitoring of the Swiss agri-environmental system (MAUS).

Thematically, these indicators cover a wide range of agroecological hotspots, such as humus, heavy metal and nutrient balances, use and risks of plant protection products, potential impact on biodiversity, greenhouse gas emissions and others. Agroscope bases the calculation of the indicators largely on existing data. To supplement and improve the quality of this data, MAUS is currently launching projects to acquire and integrate data from remote sensing, online surveys and farm management information systems (FMIS).

Integrating FMIS data essentially means requesting data that is already collected by farmers for their farm management and in order to receive direct payments. A large part of this is field calendar data, which describes what happened in a field after the previous crop was harvested: e.g., how was the seedbed prepared, what fertilisation and plant protection measures were carried out before the crop was harvested, etc.

There are various large gaps in the level of detail and scope of the FMIS available on the market compared to what is needed to calculate the indicators. Therefore, solutions are needed that allow the farms providing data to supplement missing information and, where necessary, to specify the entries for MAUS.

As part of a pilot project, a technical solution was developed with one of the Swiss providers and is currently being implemented. This has shown that, in addition to a precise definition of requirements, constant and lively dialogue is important. A comprehensive data set that exemplifies how operating data must arrive at MAUS not only helps with final testing, but also with understanding the implementation.

In the near future, other interested FMIS are to supplement their platforms so that data can be supplied to MAUS. In the collaboration between Agroscope and the interested providers, both parties will benefit from the preliminary work and the findings of the pilot project.

How to cite: Blaser, S., Baumgartner, S., Schneuwly, J., and Gilgen, A.: Integration of data from agricultural practice into the Swiss agri-environmental monitoring project MAUS, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3443, https://doi.org/10.5194/egusphere-egu24-3443, 2024.

The European Commission’s strategies under the European Green Deal aim at reducing the risks to human health and the environment from pesticide use. One of the proposed policies to achieve that goal is a restriction in use of pesticides within and near urban areas. 
In this study, we aim to estimate the impact of a full pesticide use restriction on crops near urban areas at the EU scale, by combining available EU data on urban settlements and crops. We will achieve this by utilizing spatial layers from the Joint Research Centre (JRC), including the Global Human Settlement Layer (GHSL) and the EUCROPMAP 2018 integrated with information from the Corine Land Cover (CLC) 2018.
Using various buffer distances from urban areas, the study seeks to quantify the agricultural area and crop types that will be impacted by the full restriction in use. The results will also provide insights into the percentage of treated vs. non-treated crops present in these buffer zones, highlighting country and regional differences. The economic importance of crops, together with reduced crop yields can be explored as well. Additionally, reduction in health risk to residents can be estimated from information on crop-specific intensities in pesticide use.

How to cite: Galimberti, F., Catarino, R., Fellmann, T., Florio, P., Kempeneers, P., Klinnert, A., Olvedy, M., Pistocchi, A., and D'Andrimont, R.: Evaluation of pesticide use restrictions near urban areas in the European Union, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16609, https://doi.org/10.5194/egusphere-egu24-16609, 2024.

Accurate and timely information about expected crop production is crucial for various applications including agricultural monitoring, policy making, and food security assessment. Policy makers can use near-real time crop maps to better determine crop support prices, storage infrastructure, and imports. In the context of India, absence of farm-level crop maps r the government to work with aggregate statistics based on manual surveys, and therefore are fundamentally limited in scale and accuracy. Surveys over large regions such as entire states or countries are slow and provide information only after large delays. Indian farms also go through up to three crop rotations a year necessitating continual monitoring. We put forward a nation-wide, farm-level, weekly agricultural monitoring and event detection model for the study area of India. Our model leverages remote sensing and machine learning to build a crop map that allows us to accurately monitor individual farms across large areas. 

We utilize the rich spectral and temporal information provided by Sentinel-2 satellite to provide near-real time crop monitoring, including sowing, crop type, and harvesting information. The predictions are done on an individual farm level with farm boundaries coming from a field segmentation model. Making predictions on a farm level scale helps getting more accurate yield estimates and allows monitoring individual fields for credit, insurance, resource allocation, etc. Currently, the model is able to identify major winter crops with an accuracy of up to 80% as early as 2 months after sowing. Equipped with the ability to provide weekly sowing and harvesting information makes the model near-real time for agricultural purposes. We also demonstrate the scalability of the model by showing results pan-India, across several diverse agro climatic zones. The model successfully generalizes to many unseen regions without requiring regional data. Using satellite data to provide accurate and timely crop cover information has the potential of saving millions of dollars spent by the government on manual surveys.

How to cite: Deshpande, I., Reehal, A. K., Singh, G., Nath, C., Singh, R., and Talekar, A.: Towards Live, Nation Wide, Farm-Level  Crop Monitoring , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15266, https://doi.org/10.5194/egusphere-egu24-15266, 2024.

Agriculture and its land use are associated with 22% of global annual anthropogenic greenhouse gas (GHG) emissions [1]. Reducing these emissions requires insight into how much emissions are caused by specific agricultural commodities and where they occur. Commodity-specific GHG footprints are a useful tool in this regard as they enable producers to determine the emission intensity and environmental impact of their products [2]-[3]. Further, they can help identify emission reduction strategies and region-specific mitigation efforts [4]-[6].

Spatially-explicit GHG footprints are particularly useful since they show the geographic distribution of commodities’ emission intensity and allow for the comparison across countries [7]. Several past studies have produced crop-specific footprints but considered emissions solely from land use change and did not include emissions from agricultural practices [8]-[11]. Attribution was mostly conducted at aggregate level such as country and region level [16],[17]. Those studies that employed spatially-explicit attribution methods are characterized by limited geographical coverage [14] and a limited selection of crops [15]. Studies also applied largely different methods for attributing emissions to crops, making comparison across studies not possible. 

The current study aims at filling in this research gap by improving data resolution and the methodology for attributing dynamic land-use emissions to specific crops. We derive global spatially-explicit GHG emission footprints for 161 agricultural crops over the period of 1970 to 2021 at 15 arcmin resolution. We do so by quantifying spatially-explicit land-use emissions related to agriculture and then attributing them to specific agricultural commodities (Fig1). The analysis is conducted using the LUH2 dataset on land use dynamics over time [16] and IMAGE-LPJmL 3.2 for carbon stock data [17]. IMAGE-LPJmL is a dynamic global vegetation model that simulates vegetation dynamics and distribution based on carbon cycle and crop growth model [18], [19]. This allows leveraging advanced data in terms of dynamic, annual, and spatially specific carbon stocks (Tier-3), rather than constant and/or national level carbon stock data (Tier-1) as generally used in the literature.

This study also compares three different emission attribution methods (AM) (Fig2). AM1 uses an annual accounting period, attributing emissions to the land use change committed in the same year. AM2 uses a larger time step and attributes the emissions only to the land use type at the end of the accounting period. These two methods are the two most employed methods in carbon accounting studies. We also propose an alternative approach that reflects the dynamics of land use (AM3); we attribute the emissions based on occupation year of each land use type in the accounting period.

The expected results of this study are crop-specific GHG footprints in terms of land use emissions per production area (tCO2eq/ha) and per crop yield (tCO2eq/ton) at the grid level as well as means and variations per crop and country. Also, variations as a result of different AMs will be presented and its implications for research and application in e.g. corporate emission reporting and target setting will be discussed. 

How to cite: Adlan, C. A., Wicke, B., Hanssen, S. V., and Hendriks, C.: Spatially-explicit greenhouse gas footprints of agricultural commodities from around the world, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13479, https://doi.org/10.5194/egusphere-egu24-13479, 2024.

Agricultural regions worldwide face the dual challenge of producing food for a growing world population while simultaneously reducing the industry’s environmental footprint. The prairie region of western Canada, where more than 40 million ha are used as cropland or pasture, is one of the world’s major food producing regions. This complex landscape provides agroecosystem services associated with these agricultural lands and their millions of depressional wetlands. As a cold region, and one with a highly variable climate which is undergoing strong climate change, agricultural practices continue to evolve. One widely used tool for adaptation to wet periods and to maximize arable land area is to drain wetlands; however, a tradeoff exists between draining wetlands to support expansion of cropland, and conserving wetlands to maintain their valuable ecosystem services. Wetland drainage decisions are often made without identifying impacts to the services these systems provide.

We address this gap using a novel assessment to quantify impacts to ecosystem services via wetland drainage in the Canadian prairie landscape, and explore how wetland ecosystem services may be impacted by future climate. Quantifying response of a suite of indicators (median annual flows, total phosphorus export, riparian habitat, dabbling ducks, wetland-associated birds, carbon sequestration) to wetland drainage demonstrated that all respond strongly to the loss of depressional wetlands, but sensitivity varies among the indicators. Median annual flows and phosphorus export respond more strongly than longer return period flows, potentially tripling in magnitude with high levels of wetland loss. Dabbling ducks and wetland-associated bird abundances are even more sensitive, with abundances predicted to decrease by half with loss of as little as 20% of wetland area. As a relatively unique region, where inundated wetland area is highly dynamic both interannually as the system alternates between dry and wet phases, and intra-annually (across seasons), wetland ecosystem services response to climate change is more nuanced. In the Canadian prairie, there appears to be a delicate balance between future warming and changes in precipitation amount that could yield either increases or decreases in wetland area, with wetland ecosystem services anticipated to change accordingly. Our results illustrate the sensitivity of wetland ecosystem services to agroecosystem management and climate change in a major food producing region, highlighting the need to consider the tradeoff between loss of these services and benefits of agricultural expansion. Under a drier future climate, fewer remaining wetlands may both enhance the value of wetland-associated ecosystem services, and temper the demand for wetland drainage.

How to cite: Whitfield, C., Cavaliere, E., Baulch, H., Clark, R., Spence, C., Shook, K., Pomeroy, J., and He, Z.: Assessment of wetland ecosystem services associated with changing climate and agricultural wetland drainage in a major food producing region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7035, https://doi.org/10.5194/egusphere-egu24-7035, 2024.

Woody plant encroachment (WPE) into grasslands has been occurring globally and may be accelerated by climate change and human activities in the future. There are limited studies to document this ecological process and hamper our understanding to make sustainable management approaches for grassland conservation.  Here, we improved our previous studies on woody plant encroachment in the grasslands of Oklahoma, USA.  This study (1) summarized the detection of woody plant encroachment into grasslands over the typical regions in global through PALSAR, Sentinel-1, Sentinel-2, and Landsat images; (2) examined the drivers of woody plant encroachment into grasslands at local and global scales; and (3) developed approaches to quantify the effects of woody plant encroachment into grasslands on carbon, water, and local land surface temperature. The results provide some insights to understand the process and assocaited drivers of woody plant encroachment during the last decades and the roles on carbon and water cycles and the local environment.

How to cite: Wang, J., Chang, C., Wang, X., Yang, J., and Xiao, X.: Mapping, Attribution, and Environmental Effects of Woody Plant Encroachment in Grasslands under Climate Change and Human Activities, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13714, https://doi.org/10.5194/egusphere-egu24-13714, 2024.