@article{LischeidWebberSommeretal.2022,
  author    = {Lischeid, Gunnar and Webber, Heidi and Sommer, Michael and Nendel, Claas and Ewert, Frank},
  title     = {Machine learning in crop yield modelling},
  series = {Agricultural and forest meteorology},
  volume    = {312},
  journal   = {Agricultural and forest meteorology},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0168-1923},
  doi       = {10.1016/j.agrformet.2021.108698},
  pages     = {23},
  year      = {2022},
  abstract  = {Provisioning a sufficient stable source of food requires sound knowledge about current and upcoming threats to agricultural production. To that end machine learning approaches were used to identify the prevailing climatic and soil hydrological drivers of spatial and temporal yield variability of four crops, comprising 40 years yield data each from 351 counties in Germany. Effects of progress in agricultural management and breeding were subtracted from the data prior the machine learning modelling by fitting smooth non-linear trends to the 95th percentiles of observed yield data. An extensive feature selection approach was followed then to identify the most relevant predictors out of a large set of candidate predictors, comprising various soil and meteorological data. Particular emphasis was placed on studying the uniqueness of identified key predictors. Random Forest and Support Vector Machine models yielded similar although not identical results, capturing between 50\% and 70\% of the spatial and temporal variance of silage maize, winter barley, winter rapeseed and winter wheat yield. Equally good performance could be achieved with different sets of predictors. Thus identification of the most reliable models could not be based on the outcome of the model study only but required expert's judgement. Relationships between drivers and response often exhibited optimum curves, especially for summer air temperature and precipitation. In contrast, soil moisture clearly proved less relevant compared to meteorological drivers. In view of the expected climate change both excess precipitation and the excess heat effect deserve more attention in breeding as well as in crop modelling.},
  language  = {en}
}
@article{BizicIonescuKarnataketal.2022,
  author    = {Bizic, Mina and Ionescu, Danny and Karnatak, Rajat and Musseau, Camille L. and Onandia, Gabriela and Berger, Stella A. and Nejstgaard, Jens C. and Lischeid, Gunnar and Gessner, Mark O. and Wollrab, Sabine and Grossart, Hans-Peter},
  title     = {Land-use type temporarily affects active pond community structure but not gene expression patterns},
  series = {Molecular ecology},
  volume    = {31},
  journal   = {Molecular ecology},
  number    = {6},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {0962-1083},
  doi       = {10.1111/mec.16348},
  pages     = {1716 -- 1734},
  year      = {2022},
  abstract  = {Changes in land use and agricultural intensification threaten biodiversity and ecosystem functioning of small water bodies. We studied 67 kettle holes (KH) in an agricultural landscape in northeastern Germany using landscape-scale metatranscriptomics to understand the responses of active bacterial, archaeal and eukaryotic communities to land-use type. These KH are proxies of the millions of small standing water bodies of glacial origin spread across the northern hemisphere. Like other landscapes in Europe, the study area has been used for intensive agriculture since the 1950s. In contrast to a parallel environmental DNA study that suggests the homogenization of biodiversity across KH, conceivably resulting from long-lasting intensive agriculture, land-use type affected the structure of the active KH communities during spring crop fertilization, but not a month later. This effect was more pronounced for eukaryotes than for bacteria. In contrast, gene expression patterns did not differ between months or across land-use types, suggesting a high degree of functional redundancy across the KH communities. Variability in gene expression was best explained by active bacterial and eukaryotic community structures, suggesting that these changes in functioning are primarily driven by interactions between organisms. Our results indicate that influences of the surrounding landscape result in temporary changes in the activity of different community members. Thus, even in KH where biodiversity has been homogenized, communities continue to respond to land management. This potential needs to be considered when developing sustainable management options for restoration purposes and for successful mitigation of further biodiversity loss in agricultural landscapes.},
  language  = {en}
}