Mapping dynamical systems onto complex networks

A procedure to characterize chaotic dynamical systems with concepts of complex networks is pursued, in which a dynamical system is mapped onto a network. The nodes represent the regions of space visited by the system, while edges represent the transitions between these regions. Parameters used to quantify the properties of complex networks, including those related to higher order neighborhoods, are used in the analysis. The methodology is tested for the logistic map, focusing the onset of chaos and chaotic regimes. It is found that the corresponding networks show distinct features, which are associated to the particular type of dynamics that have generated them.Comment: 13 pages, 8 eps files in 5 figure

Investigation of the soil properties that affect Olsen P critical values in different soil types and impact on P fertiliser recommendations

Optimization of phosphorus (P) fertiliser use is desired to ensure more sustainable use of fertiliser, economic food production and reduction of eutrophication of water bodies. Presently, the Olsen P values on which fertiliser recommendations are based to achieve optimum yield are frequently the same for all soils. The aim of this study was to identify the properties of different soils that affect their critical Olsen P values in order to develop better, soil specific P fertiliser recommendations. A pot experiment using 10 soils with low available P with different P additions was carried out to investigate the impact of wide-ranging soil properties on the relationship between P addition, resultant Olsen P values and yield response of ryegrass to Olsen P values. The relationship between added P and Olsen P varied greatly between the individual soils. These relationships were affected by pH, manganese oxide, crystalline aluminium oxide and amorphous iron oxide contents of the soil. Different soils had widely varying critical Olsen P values for ryegrass. However, these could not be related to the measured soil properties. Fertiliser recommendations and critical values for optimum yield of ryegrass based on the Olsen P test should be soil specific. The complexity and lack of clarity over which combination of soil properties governs critical Olsen P values calls for further investigation with more soil types and additional soil property measurements to elucidate the different factors controlling critical Olsen P values in different soils

Yield Gaps and Ecological Footprints of Potato Production Systems in Chile

In Chile, potatoes are grown in a wide range of ecological zones and levels of technology resulting in wide ranges of crop management and yields. The aim of the present study was to assess yield gaps, resource use efficiencies and foot-printing in different potato cropping zones between 18 and 53° South considering early and late crops, small and large holdings (>10 ha/year) and ware and seed potato crops. Two mathematical tools were used to generate data for comparisons: the light interception and utilization simulator for potato crops (LINTUL-Potato) to calculate potential yields and water need of each system and the Cool Farm Tool – Potato (CFT) to calculate the amount of CO2 associated with the production of 1 ton of potato. Meteorological data for LINTUL-Potato came from official services, and data needed to complete the CFT came from a survey carried out for the 10 sites yielding amounts of inputs and number of operations, potato yields and planting and harvesting dates. The survey yielded 20 cropping systems with an average yield of 31 t ha−1. Yields were related to daily growth rate and not to the length of the growing season. Considerable variation was found in resource-use efficiency and CO2 emission. It was concluded that large farms show a lower land footprint than small farms due to a higher technological level, but while applying more water and fertilizer, they result in higher water and CO2 footprints. Late crops may fetch higher off-season prices but have higher land, water and CO2 footprints. The most suitable potato production systems are the rain-fed summer crops in the South with the lowest footprints. The highest footprints have the irrigated winter crops in the centre of Chile. The subsistence high altitude Andean crop in the utmost North has the highest land footprint but the lowest CO2 emission. The description,analysis and benchmarking of the potato production systems in Chile allow strategies for improving footprints and profitability and yields information about future investments in research, development and production of the crop.http://link.springer.com/journal/11540hb201

Revisiting the relationship between nitrogen nutrition index and yield across major species

Crop nitrogen (N) fertilization diagnoses via the N nutrition index (NNI)-yield relationship have been tested forseveral crop species, but a cross-species comparison of that relationship has not been performed yet. This studyaimed to perform a cross-species comparison of the relationship between NNI and yield with emphasis on theyield sensitivity to N deficiency, slope of the models. Additionally, we conducted an evaluation to determine thebest NNI sampling moment to predict relative yield, with focus on major grain crops. Based on a recently publishedglobal dataset to parametrize critical dilution curves, we calculated integrated NNI, instantaneous NNI,relative yield, and relative shoot biomass for annual ryegrass, tall fescue, maize, potato, rice, and wheat. Weobtained 238 observations to fit integrated NNI-relative yield linear mixed-effects models and 1606 observationsto fit instantaneous NNI-relative yield models. Subsequently, we performed a sensitivity analysis to determinethe best NNI sampling moment to predict relative yield, with focus on major grain crops (maize, rice, and wheat).Our results show that there was low inter-species variation of sensitivity to N deficiency, i.e., the slope of therelationship between relative yield and integrated NNI. For grain crops, instantaneous NNI around anthesisdemonstrated a better predictive capability for relative yield, outperforming other vegetative stages. This findingcontributed to improving the understanding of the association between relative yield and NNI with implicationsfor breeding programs, nutrient management practices, and crop modelling. Most importantly, this study is acontribution to improving the N nutrition diagnosis for several crop species, by using an integral, comparativeapproach.Fil: Rodriguez, Ignacio Martin. Universidad Nacional de Mar del Plata; Argentina. Kansas State University; Estados Unidos. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce. Agencia de Extensión Rural Balcarce; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; ArgentinaFil: Lacasa, Josefina. Kansas State University; Estados UnidosFil: van Versendaal, Emmanuela. Kansas State University; Estados UnidosFil: Lemaire, Gilles. Institut National de la Recherche Agronomique; FranciaFil: Belanger, Gilles. Quebec Research And Development Centre; CanadáFil: Jégo, Guillaume. Quebec Research And Development Centre; CanadáFil: Sandaña, Patricio G.. Universidad Austral de Chile; ChileFil: Soratto, Rogério P.. Universidade Federal de Sao Paulo; BrasilFil: Djalovic, Ivica. National Institute of the Republic of Serbia; SerbiaFil: Ata Ul Karim, Syed Tahir. State University of Pennsylvania; Estados UnidosFil: Reussi Calvo, Nahuel Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; Argentina. Universidad Nacional de Mar del Plata; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce. Agencia de Extensión Rural Balcarce; ArgentinaFil: Giletto, Claudia Marcela. Universidad Nacional de Mar del Plata; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce. Agencia de Extensión Rural Balcarce; ArgentinaFil: Zhao, Ben. Chinese Academy Of Agricultural Sciences; ChinaFil: Ciampitti, Ignacio Antonio. Kansas State University; Estados Unido