Holger Meinke

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Professor Holger Meinke is the Chair of the ISDC, a standing panel of experts appointed by the CGIAR’s System Council to serve as an independent advisor on science and research matters, including strategies for effective partnerships along the research for development continuum.
Holger is also an Adjunct Research Professor for Global Food Sustainability at the University of Tasmania, Australia. He is internationally renowned as a leader in climate, agriculture and food systems research and has published over 130 refereed papers in disciplinary and transdisciplinary journals.
In 2020 Holger founded the Science Advice Network (SAN), a consulting business that aims to advance societies through scientific knowledge. As the Principal Consultant of SAN, he assists Government, not-for-profit organisations and private sector businesses in translating scientific knowledge into impact.
From 2011 to 2019, Holger was Director of the Tasmanian Institute of Agriculture at the University of Tasmania. Prior to that (2007-2011) he held the Chair of Crop and Weed Ecology at Wageningen University.

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Papers by Holger Meinke

From rainfall to farm incomes—transforming advice for Australian drought policy. II. Forecasting farm incomes

Australian journal of agricultural research, 2007

Intra-seasonal climate predictions - linking weather and climate forecasts

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Using seasonal climate forecasts for more effective grain-cotton production systems

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Quantificação de riscos climáticos via modelos de regressão de cox

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A quantitative trait loci (QTL) regulating early flowering in barley ( Hordeum vulgare L.)

Spatial environmental variability, including changes in climate to frost, heat and extreme drough... more Spatial environmental variability, including changes in climate to frost, heat and extreme drought frequencies is penalising yield of barley. To achieve higher yield and improved grain quality, genotypes must be highly adaptable to the changing environment. Identification and understanding the genetic factors and their complex interactions with environment regulating heading date will help barley crop adaptation and consequently increase yield gain and improve grain quality.

Tasmania’s Bioeconomy: Employing the Seven Capitals to Sustain Innovative and Entrepreneurial Agrifood Value Chains

Economic complexity and evolution, 2017

Tasmania, Australia’s southernmost and smallest island state, depends strongly on its bioeconomy.... more Tasmania, Australia’s southernmost and smallest island state, depends strongly on its bioeconomy. Currently the farm gate production of Tasmania’s bioeconomy contributes around 7.4% to the overall Gross State Product (GSP). This figure is considerably higher than for Australia, where the bioeconomy contributes 2.5% to the overall Gross Domestic Product (GDP). Based on this measure, Tasmania’s economy is more in line with the economies of Brazil (5.7%) or New Zealand (7.2%). It is estimated that Tasmania’s bioeconomy currently contributes 16–20% of overall economic output, when taking into account the economic impact of related value chains that reach from agricultural suppliers to retailers. Government policy for economic growth in Tasmania aims to build up this sector over the following decades. To achieve the stated growth targets, technologies must be combined with business capabilities in order to effectively and efficiently commercialize innovation while maintaining sound environmental practices. A technology-driven, irrigation-led transformation is currently underway in the state, turning Tasmania’s bioeconomy into a highly knowledge-intensive sector of the economy. To fully realize the economic, environmental and social potential of investment in irrigation infrastructure, there must be similar investments in research, knowledge creation, marketing, value chain innovations and capability development.

Managing the quality of wheat grain under global change

Modelling and Simulation Society of Australia and New Zealand eBooks, 2003

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Tertiary agricultural education in Australasia: where to from here?

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Impact of climatic change on planting date and crop development in wheat and sorghum in the summer rainfall areas of Queensland and New South Wales

Europe's diminishing bread basket

Nature Climate Change, Jun 25, 2014

Global demand for wheat is projected to increase significantly with continuing population growth.... more

Integrating physiological understanding and plant breeding via crop modelling and optimization

Labour productivity: The forgotten yield gap

Agricultural Systems

Designing high-yielding wheat crops under late sowing: a case study in southern China

Agronomy for Sustainable Development

Cropping of rice and wheat (Triticum aestivum L.) in rotation contiguously in the same field is a... more Cropping of rice and wheat (Triticum aestivum L.) in rotation contiguously in the same field is a fundamental pillar of double-cropping systems in southern China. Yields of such cropping systems are increasingly challenged as climate change (CC) drives increases in autumnal rainfall, delaying rice harvesting and subsequent sowing of wheat. Here, our purpose was to identify prospective traits of wheat crops enabling adaptation to later sowing and successively truncated growing seasons caused by CC. To identify traits that maintained or improved yields, we constructed 4,096 hypothetical genotypes underpinned by step-wise variations in parameters regulating phenology, growth and yield components. We then assimilated biophysical response surfaces through genotype (G) by environment (E) by management (M) analyses (G×E×M) using six locations spread across the breadth of southern China. We showed that later sowing reduced cumulative radiation interception, cumulative thermal time and crop ...

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S5-O.10-Multi-resolution analysis of aggregated spatial data to simulate yield and irrigation water demand at regional scales

Second International Crop Modelling Symposium (iCROPM 2020): Crop Modelling for Agriculture and Food Security under Global Change, 2020

Multi-resolution analysis of aggregated spatial data to simulate yield and irrigation water demand at regional scales

Input data aggregation influences crop model estimates at the regional level (Eyshi Rezaei et al.... more Input data aggregation influences crop model estimates at the regional level (Eyshi Rezaei et al., 2015). Previous studies have focused on the impact of aggregating the climate data used to compute crop yields (Hoffmann et al., 2015; van Bussel et al., 2011; Zhao et al., 2015). Little is known about the combined data aggregation effect of climate (DAEc) and soil (DAEs) model inputs. This study explores the implications of using coarse resolution input data on model outputs (irrigated and rainfed yield and irrigation water demand [IWD]) in Tasmania, Australia by (i) separately assessing the DAEc and DAEs of model input data and (ii) assessing the combined impact of DAEc and DAEs. We provide a framework to quantifying the input uncertainty introduced by using aggregated data to meet the objectives of modelling exercises.

AgLTAS: A network of practice that has defined national Learning and Teaching Academic Standards for Agriculture

Modelling carbon and nitrogen dynamics in upland rice systems

Light interception in intercropping by using the FSPM

Implications of data aggregation method on crop model outputs – The case of irrigated potato systems in Tasmania, Australia

European Journal of Agronomy, 2021

Abstract Crop models were originally developed for application at the field scale but are increas... more Abstract Crop models were originally developed for application at the field scale but are increasingly used to assess the impact of climate and/or agronomic practices on crop growth and yield and water dynamics at larger scales. This raises the question of how data aggregation approaches affect outputs when using crop models at large spatial scales. This study investigates how input and output data aggregation affected simulated rainfed and irrigated potato yield and irrigation water requirement (IWR) across potato production areas in Tasmania, Australia. First, the yield and IWR with aggregated model inputs at 15, 25 and 40 km resolutions (input aggregation) was simulated. Second, simulated model outputs generated with high-resolution input data were aggregated to 15, 25 and 40 km resolutions (output aggregation) and compared to the corresponding yield and IWR with simulations based on input data aggregation. Finally, the differences ( D ) ( D Y and D I W R for yield and IWR, respectively) between grids using input and output aggregation were evaluated. The results indicate that the effect of input and output data aggregation on yield depends on water-driven factors including plant available water capacity (PAWC), rainfall and irrigation. Maximum D values were found for rainfed yield (4.4 t ha−1) and IWR (137 mm). D Y variations were correlated with the differences of PAWC caused by data aggregation in 82 % of potato production areas. Differences between aggregation methods were reduced when growing season rainfall increased. We conclude that PAWC and the source of water (rainfall or rainfall + irrigation) explained the larger errors associated with the input and output data aggregation on simulated potato yield and IWR. Future studies should consider the data aggregation method in their assessment to minimize errors and therefore produce higher quality advice or farming decisions.