Supplementary Materials- Model-based Yield gap assessment in Nepal's diverse agricul-ture landscape

Supplementary modelling results of Manuscript "Model-based Yield gap assessment in Nepal’s diverse agricul-ture landscape

Supplementary Materials- Model-based Yield gap assessment in Nepal's diverse agricul-ture landscape

A table with the district-wise simulated current and attainable yields, yields simulated with additional water and irrigation applications, yield gaps, nitrogen, phosphorous and irrigation requirements, agricultural area and total attainable yields per district in Nepal

Supplementary Materials- Model-based Yield gap assessment in Nepal's diverse agricul-ture landscape

A table with the district-wise simulated current and attainable yields, yields simulated with additional water and irrigation applications, yield gaps, nitrogen, phosphorous and irrigation requirements, agricultural area and total attainable yields per district in Nepal

Model-Based Yield Gap Assessment in Nepal’s Diverse Agricultural Landscape

Rice, wheat, maize, millet, and barley are the five major staple cereal crops in Nepal. However, their yields are low, and imports are needed to meet domestic demand. In this study, we quantify the gap between current and potentially attainable yields in Nepal, estimate how much additional fertilizer and irrigation are required to close the gap, and assess if self-sufficiency can thus be achieved. For this, we first test the ability of the crop model EPIC to reproduce reported yields in 1999–2014 accurately. On average, simulated and reported yields at the national level were in the same range, but at the district level, the error was large, as the resolutions of the available climate and soil input data were not high enough to depict the heterogenic conditions in Nepal adequately. In the main study, we show that average yield gaps in Nepal amount to 3.0 t/ha (wheat), 2.7 t/ha (rice), 2.9 t/ha (maize), 0.4 t/ha (barley), and 0.5 t/ha (millet). With additional irrigation and fertilization, yields can be increased by 0.1/2.3 t/ha (wheat), 0.4/1.3 t/ha (rice), 1.6/1.9 t/ha (maize), 0.1/0.3 t/ha (barley), and 0.1/0.4 t/ha (millet), respectively. The results show that providing reliable and affordable access to fertilizer should be a priority for closing yield gaps in Nepal

LHC computing stability emphasized at CHEP ’07

Despite the current drop in price, many fossil fuel resources are becoming increasingly scarce and their consumption is associated with climate change and harmful effects on ecosystems and human health. At the same time, population growth and corresponding pressures on natural resources have risen beyond safe ecological limits. In response to these societal challenges, countries have adopted ambitious global goals such as the 2°C limit to global warming, the Aichi Biodiversity Targets, and the Sustainable Development Goals. However, this unprecedented global awareness has yet to be matched with appropriate action towards achieving these goals and targets. An increasing number of countries look to the bio-economy as a strategy to reduce reliance on fossil fuel and enable sustainable development through a "biologization" of the regular economy. At global scale, however, bio-economies are diverse with sectors, such as agriculture, forestry, energy, chemicals & pharmaceuticals as well as science and education. We thus also expect large variation in the factors driving sustainability outcomes of bio-based development strategies and the appropriate strategies to promote them. In this study, we develop a typology of bio-economies based on country-specific characteristics. We describe five different bio-economy types with varying degrees of importance of the primary and the high-tech sector. While the importance of the high-tech sector is mirrored by the availability of skills, the importance of the primary sector for the national economy is apparently not dependent on the amount of bioproductive land but rather determined by lower levels of skill availability. In terms of sustainability performance, indicators suggest that diversified high-tech economies have experienced a slight improvement especially in terms of resource consumption and material footprints. Levels remain however at the highest levels compared to all other types with large amounts of resources and raw materials being imported from other countries, especially for non-food purposes. Increased competition between food, energy and the environment can push innovations for more efficient use of land, biomass and other resources but it can also increase imports of biomass, especially primary raw materials and associated externalization effects of environmental costs. In an increasingly telecoupled world, these results highlight the following priorities for sustainable development: the necessity of developed high-tech bio-economies to further decrease their environmental footprint domestically and internationally and the importance of biotechnology innovation transfer after critical and comprehensive sustainability assessments.peerReviewe

Towards improved land use mapping of irrigated croplands: performance assessment of different image classification algorithms and approaches

Accurate agricultural land use (LU) map is essential for many agro-environmental applications. With advances in technology, object-based image classification and non-parametric machine learning algorithms evolved. Still, no particular method has universal applicability. This paper compares robust non-parametric machine learning algorithms, random forest (RF) and support vector machine (SVM), and a common parametric algorithm maximum likelihood (MLC) based on multiple Landsat 8 images. We have also assessed the classifier performance relative to the choice either pixel-based (PB) or field-based (FB) approach. The study area, a semi-desert irrigated region, lies in Khorezm province and Republic of Karakalpakstan in Uzbekistan. Accuracy assessment showed higher overall accuracy (OA) and kappa index (KI) of the nonparametric machine learning FB-RF and FB-SVM algorithms over the PB-RF, PB-SVM and PB-MLC algorithms. The lowest OA and KI occurred with the parametric FB-MLC. Based on the results, the FB machine learning non-parametric algorithms are recommended for mapping irrigated croplands

In Memoriam: Gunter Menz

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