A three-year data set of gaseous field emissions from crop sequence at three sites in Germany.

The purpose of the StaPlaRes project was to evaluate two innovative techniques of urea fertiliser application and to quantify greenhouse gas (GHG) emissions. All GHG emissions, as well as other gaseous emissions, agronomic and environmental variables were collected for three years (2016/2017-2018/2019) at three experimental field sites in Germany. All management activities were consistently documented. Multi-variable data sets of gas fluxes (N2O and NH3), crop parameters (grain and straw yield, N content, etc.), soil characteristics (NH4-N, NO3-N, etc.), continuously recorded meteorological variables (air and soil temperatures, radiation, precipitation, etc.), management activities (sowing, harvest, soil tillage, fertilization, etc.), were documented and metadata (methods, further information about variables, etc.) described. Additionally, process-related tests were carried out using lab (N2 emissions), pot and lysimeter experiments (nitrate leaching). In total, 2.5 million records have been stored in a Microsoft Access database (StaPlaRes-DB-Thuenen). The database is freely available for (re)use by others (scientists, stakeholders, etc.) on the publication server and data repository OpenAgrar for meta-analyses, process modelling and other environmental studies.

UAV-Based RGB Imagery for Hokkaido Pumpkin (Cucurbita max.) Detection and Yield Estimation.

Pumpkins are economically and nutritionally valuable vegetables with increasing popularity and acreage across Europe. Successful commercialization, however, require detailed pre-harvest information about number and weight of the fruits. To get a non-destructive and cost-effective yield estimation, we developed an image processing methodology for high-resolution RGB data from Unmanned aerial vehicle (UAV) and applied this on a Hokkaido pumpkin farmer's field in North-western Germany. The methodology was implemented in the programming language Python and comprised several steps, including image pre-processing, pixel-based image classification, classification post-processing for single fruit detection, and fruit size and weight quantification. To derive the weight from two-dimensional imagery, we calculated elliptical spheroids from lengths of diameters and heights. The performance of this processes was evaluated by comparison with manually harvested ground-truth samples and cross-checked for misclassification from randomly selected test objects. Errors in classification and fruit geometry could be successfully reduced based on the described processing steps. Additionally, different lighting conditions, as well as shadows, in the image data could be compensated by the proposed methodology. The results revealed a satisfactory detection of 95% (error rate of 5%) from the field sample, as well as a reliable volume and weight estimation with Pearson's correlation coefficients of 0.83 and 0.84, respectively, from the described ellipsoid approach. The yield was estimated with 1.51 kg m-2 corresponding to an average individual fruit weight of 1100 g and an average number of 1.37 pumpkins per m2. Moreover, spatial distribution of aggregated fruit densities and weights were calculated to assess in-field optimization potential for agronomic management as demonstrated between a shaded edge compared to the rest of the field. The proposed approach provides the Hokkaido producer useful information for more targeted pre-harvest marketing strategies, since most food retailers request homogeneous lots within prescribed size or weight classes.

A systems approach toward climate resilient livelihoods: A case study in Thai Nguyen province, Vietnam.

This study aims to identify strategic actions towards climate resilient livelihoods and secure income for smallholder farmers in Thai Nguyen province of Vietnam using a systems approach and system dynamic modelling tools. Information and data for this research was collected through surveys, interviews, focus group discussions and workshops with relevant stakeholders and 187 farmers in two vulnerable districts during October 2019-April 2020. Findings of this study uncovered a number of shortcomings of the government policies and approaches in climate change adaptation. Local initiatives, community learning and ownership seem to be neglected. This research has substantiated the effectiveness and validity of systems approaches and tools in structuring and solving complex issues in agricultural research and development under the interwoven relationships between environmental and human factors. Climate resilient production models and practices are just part of the systemic interventions that need to be implemented in a coordinated manner towards a more resilient future of the farming communities. This study has addressed the current knowledge gap and the need for using integrated approaches and decision support systems for unravelling ill-structured and/or complex issues of climate change adaptation (CCA). It also provided practical recommendations for informed CCA policies and implementation.

Spatio-temporal analysis of agricultural land-use intensity across the Western Siberian grain belt.

The Western Siberian grain belt covers 1millionkm² in Asiatic Russia and is of global importance for agriculture. Massive land-use changes took place in that region after the dissolution of the Soviet Union and the collapse of the state farm system. Decreasing land-use intensity (LUI) in post-Soviet Western Siberia was observed on grassland due to declining livestock whilst on cropland trends of land abandonment reversed in the early 2000s. Recultivation of abandoned cropland as well as increasing fertilizer inputs and narrowing crop rotations led to increasing LUI on cropland during the last two decades. Beyond that general trend, no information is available about spatial distribution and magnitude but a crucial precondition for the development of strategies for sustainable land management. To quantify changes and patterns in LUI, we developed an intensity index that reflects the impacts of land-based agricultural production. Based on subnational yearly statistical data, we calculated two separate input-orientated indices for cropland and grassland, respectively. The indices were applied on two spatial scale: at seven provinces covering the Western Siberian grain belt (Altay Kray, Chelyabinsk, Kurgan, Novosibirsk, Omsk, Sverdlovsk and Tyumen) and at all districts of the central province Tyumen. The spatio-temporal analysis clearly showed opposite trends for the two land-use types: decreasing intensity on grassland (-0.015 LUI units per year) and intensification on cropland (+0.014 LUI units per year). Furthermore, a spatial concentration towards intensity centres occurred during transition from a planned to a market economy. A principal component analysis enabled the individual calculations of both land-use types to be combined and revealed a strong link between biophysical conditions and LUI. The findings clearly showed the need for having a different strategy for future sustainable land management for grassland (predominantly used by livestock of households) and cropland (predominantly managed by large agricultural enterprises), which have to be addressed specifically by the different land users. As all input data are publicly available, the approach described is readily transferable to other regions or countries of the former Soviet Union.