Global ETD Search

61	Marginal agricultural land identification in the Lower Mississippi Alluvial Valley Tiwari, Prakash 12 May 2023 (has links) (PDF) This study identified marginal agricultural lands in the Lower Mississippi Alluvial Valley using crop yield predicting models. The Random Forest Regression (RFR) and Multiple Linear Regression (MLR) models were trained and validated using county-level crop yield data, climate data, soil properties, and Normalized Difference Vegetation Index (NDVI). The RFR model outperformed MLR model in estimating soybean and corn yields, with an index of agreement (d) of 0.98 and 0.96, Nash-Sutcliffe model efficiency (NSE) of 0.88 and 0.93, and root mean square error (RMSE) of 9.34% and 5.84%, respectively. Marginal agricultural lands were estimated to 26,366 hectares using cost and sales price in 2021 while they were estimated to 623,566 hectares using average cost and sales price from 2016 to 2021. The results provide valuable information for land use planners and farmers to update field crops and plan alternative land uses that can generate higher returns while conserving these marginal lands. Marginal Agricultural Land Machine Learning Remote Sensing Soybean Corn Crop Yield Prediction Lower Mississippi Alluvial Valley Random Forest Model Agriculture Food Science
62	Genetic Variation in Photosynthesis as a Tool for Finding Principal Routes to Enhancing Photosynthetic Efficiency Tomeo, Nicholas J. 20 September 2017 (has links) No description available. Ecology Plant Biology Physiology Agronomy Photosynthesis gas exchange soybean crop yield genetic variation photorespiration Arabidopsis thaliana mesophyll conductance water use efficiency
63	Modèles d'impact statistiques en agriculture : de la prévision saisonnière à la prévision à long terme, en passant par les estimations annuelles / Impact models in agriculture : from seasonal forecast to long-term estimations, including annual estimates Mathieu, Jordane 29 March 2018 (has links) En agriculture, la météo est le principal facteur de variabilité d’une année sur l’autre. Cette thèse vise à construire des modèles statistiques à grande échelle qui estiment l’impact des conditions météorologiques sur les rendements agricoles. Le peu de données agricoles disponibles impose de construire des modèles simples avec peu de prédicteurs, et d’adapter les méthodes de sélection de modèles pour éviter le sur-apprentissage. Une grande attention a été portée sur la validation des modèles statistiques. Des réseaux de neurones et modèles à effets mixtes (montrant l’importance des spécificités locales) ont été comparés. Les estimations du rendement de maïs aux États-Unis en fin d’année ont montré que les informations de températures et de précipitations expliquent en moyenne 28% de la variabilité du rendement. Dans plusieurs états davantage météo-sensibles, ce score passe à près de 70%. Ces résultats sont cohérents avec de récentes études sur le sujet. Les prévisions du rendement au milieu de la saison de croissance du maïs sont possibles à partir de juillet : dès juillet, les informations météorologiques utilisées expliquent en moyenne 25% de la variabilité du rendement final aux États-Unis et près de 60% dans les états plus météo-sensibles comme la Virginie. Les régions du nord et du sud-est des États-Unis sont les moins bien prédites. Le rendements extrêmement faibles ont nécessité une méthode particulière de classification : avec seulement 4 prédicteurs météorologiques, 71% des rendements très faibles sont bien détectés en moyenne. L’impact du changement climatique sur les rendements jusqu’en 2060 a aussi été étudié : le modèle construit nous informe sur la rapidité d’évolution des rendements dans les différents cantons des États-Unis et localisent ceux qui seront le plus impactés. Pour les états les plus touchés (au sud et sur la côte Est), et à pratique agricole constante, le modèle prévoit des rendements près de deux fois plus faibles que ceux habituels, en 2060 sous le scénario RCP 4.5 du GIEC. Les états du nord seraient peu touchés. Les modèles statistiques construits peuvent aider à la gestion sur le cours terme (prévisions saisonnières) ou servent à quantifier la qualité des récoltes avant que ne soient faits les sondages post-récolte comme une aide à la surveillance (estimation en fin d’année). Les estimations pour les 50 prochaines années participent à anticiper les conséquences du changement climatique sur les rendements agricoles, pour définir des stratégies d’adaptation ou d’atténuation. La méthodologie utilisée dans cette thèse se généralise aisément à d’autres cultures et à d’autres régions du monde. / In agriculture, weather is the main factor of variability between two consecutive years. This thesis aims to build large-scale statistical models that estimate the impact of weather conditions on agricultural yields. The scarcity of available agricultural data makes it necessary to construct simple models with few predictors, and to adapt model selection methods to avoid overfitting. Careful validation of statistical models is a major concern of this thesis. Neural networks and mixed effects models are compared, showing the importance of local specificities. Estimates of US corn yield at the end of the year show that temperature and precipitation information account for an average of 28% of yield variability. In several more weather-sensitive states, this score increases to nearly 70%. These results are consistent with recent studies on the subject. Mid-season maize crop yield forecasts are possible from July: as of July, the meteorological information available accounts for an average of 25% of the variability in final yield in the United States and close to 60% in more weather-sensitive states like Virginia. The northern and southeastern regions of the United States are the least well predicted. Predicting years for which extremely low yields are encountered is an important task. We use a specific method of classification, and show that with only 4 weather predictors, 71% of the very low yields are well detected on average. The impact of climate change on yields up to 2060 is also studied: the model we build provides information on the speed of evolution of yields in different counties of the United States. This highlights areas that will be most affected. For the most affected states (south and east coast), and with constant agricultural practice, the model predicts yields nearly divided by two in 2060, under the IPCC RCP 4.5 scenario. The northern states would be less affected. The statistical models we build can help for management on the short-term (seasonal forecasts) or to quantify the quality of the harvests before post-harvest surveys, as an aid to the monitoring (estimate at the end of the year). Estimations for the next 50 years help to anticipate the consequences of climate change on agricultural yields, and to define adaptation or mitigation strategies. The methodology used in this thesis is easily generalized to other cultures and other regions of the world. Modélisation statistique Modèles à effets mixtes Réseaux de neurones Régression et classification Modèle à large échelle Statistical modeling Mixed effects models Neural networks Regression and classification Application to crop yield prediction Large scale model 519
64	Vliv předplodiny na výnos a kvalitu ovsa / The influence of foregoing crop on oats yield and quality POLÁČKOVÁ, Jitka January 2013 (has links) Nowadays trend in Europe is returning to harvesting of traditional feeding crops. One of these crops is oat which has been universally used since time immemorial. Even today oats are processing and using in food industry and as a feed for farm animals. Further using of oats is in cosmetics and pharmaceutical industry. Oats contain high amount of proteins and fats, beta-glukan and mineral elements. Oats include vitamins B and E, lecitin, niacin and antioxidants. Research was executing on fields of University of South Bohemia in České Budějovice during one year. An impact on crop yield and quality of oats was observed in dissertation. There were used three crops corn, rape and cereal (spring wheat) in research. After these crops were sown ten varieties of oats, four naked and six husked varieties. At these varieties were observed an impact on crop yield and quality of oats. Evaluated characters were monitored during the vegetation, pre-harvest and post-harvest. There were observed germination and methane, height of vegetation, the degree of lodging oats, the number of lat m2, weeds, diseases and pests, after harvest was determine harvested grain yield, moisture and density, number of grains per panicle, thousand grain weight and grain on a network share during the vegetation. From the measured values follows that the best crop in year 2012 was corn and the best results were achieved by variety Abel, Avenuda, Atego, Pogon, Salo.
65	Managing phenology for agronomic adaptation of global cropping systems to climate change Minoli, Sara 27 November 2020 (has links) Der Klimawandel fordert die Anbausysteme heraus, um das derzeitige Produktionsniveau zu verbessern oder sogar aufrechtzuerhalten. Es wird erwartet, dass zukünftige Trends bei Temperatur und Niederschlag die Ernteproduktivität beeinträchtigen. Es ist daher notwendig, möglicher Lösungen zur Anpassung der Anbausysteme an den Klimawandel zu untersuchen. Ziel dieser Arbeit ist es, das Wissen über die Anpassung von weltweit relevanten Getreidepflanzen an den Klimawandel zu erweitern. Die zentrale Fragestellung ist, ob globale Anbausysteme an den Klimawandel angepasst werden können, indem die Phänologie der Kulturpflanzen durch Anpassung von Wachstumsperioden und Sorten gesteuert wird. Die Phänologie und die Ertragsreaktionen sowohl auf den Temperaturanstieg als auch auf die Sortenselektion werden zunächst anhand eines Ensembles von “Global Gridded Crop Models” bewertet. Anschließend wird die Komplexität der Anpassung durch phänologisches Management analysiert, insbesondere unter Berücksichtigung der bestehenden großen Wissenslücken bei der Auswahl von Pflanzensorten. Das Ergebnis der Analyse ist ein regelbasierter Algorithmus, der phänologische Zyklen der Kulturpflanzen auswählt, um die Zeit für die Ertragsbildung zu maximieren und Temperatur- und Wasserbelastungen während der Wachstumszyklen der Kulturpflanzen zu minimieren. Die berechneten Aussaatdaten und Wachstumsperioden werden verwendet, um globale Muster von Sorten zu parametrisieren, die an aktuelle und zukünftige Klimaszenarien angepasst sind. Diese Arbeit zeigt, dass die Auswirkungen des Klimawandels auf die Pflanzenproduktivität erheblich variieren können, je nachdem, welche Annahmen für das agronomische Management getroffen werden. Änderungen im Management zu vernachlässigen, liefert die pessimistischste Prognose für die zukünftige Pflanzenproduktion. Relativ einfache Ansätze zur Berechnung angepasster Aussaatdaten und Sorten bieten eine Grundlage für die Berücksichtigung autonomer Anpassungsschemata als integraler Bestandteil globaler Modellierungsrahmen. / Climate change is challenging cropping systems to enhance or even maintain current production levels. Future trends in temperature and precipitation are expected to negatively impact crop productivity. It is therefore necessary to explore adaptation options of cropping systems to changing climate. The aim of this thesis is to advance knowledge on adaptation of world-wide relevant grain crops to climate change. The central research question is whether global cropping systems can be adapted to climate change by managing crop phenology through adjusting growing periods and cultivars. Phenology and yield responses to both temperature increase and cultivar selection are first assessed making use of an ensemble of Global Gridded Crop Models. Then, the complexity of adaptation through phenological management is analysed, particularly addressing the existing large knowledge gaps on crop cultivar choice. The outcome of the analysis is a rule-based algorithm that selects crop phenological cycles aiming at maximizing the time for yield formation and minimizing temperature and water stresses during the crop growth cycles. The computed sowing dates and growing periods are used to parametrize global patterns of cultivars adapted to present and future climate scenarios. This thesis demonstrates that the impacts of climate change on crop productivity can vary substantially depending on which assumptions are made on agronomic management. Neglecting any changes in management return the most pessimistic projection on future crop production. Relatively simple approaches to compute adapted sowing dates and cultivars provide a base for considering autonomous adaptation schemes as an integral component of global scale modelling frameworks. Ertrag Phänologie Wachstumsphase Pflanzensorten Klimawandel Anpassung Pflanzenmodellierung globaler Maßstab crop yield phenology growing period crop cultivar climate change adaptation crop modelling global scale 577 Ökologie 633 Feld- und Plantagenfrüchte ZA 57500 ZC 11300 ZC 31000 ddc:577 ddc:633
66	Effects of macadamia husk compost on physicochemical soil properties, growth and yield of Chinese cabbage (Brassica rapa L. Chinesis) on sandy soil Maselesele, Dembe 07 1900 (has links) MSCAGR (Plant Production) / Department of Plant Production / Poor soil fertility caused by inadequate supply of nutrients on soil is one of the major constraints limiting crop production especially in the Vhembe District Municipality, Limpopo, South Africa. Therefore, management practices such as application of organic manure to minimize soil infertility is considered as good practice for smallholder farmers. This study aimed at evaluating the effect of macadamia husk compost on selected soil properties as well as the growth and yield of Chinese cabbage on sandy loam soil. A field experiment was carried out during 2018 and 2019 winter season at the Agricultural Research Council (ARC) research farm in Levubu. The experiment was laid out in a randomized complete block design (RCBD) with 4 treatments (control (zero)), inorganic fertilizer (100:60:60 NPK Kg ha-1) and compost at 15t ha-1 & 30t ha-1 replicated 3 times. Soil was analyzed before planting and after harvesting to determine the influence of applied compost on selected physical properties (soil bulk density and water holding capacity) and chemical properties (soil pH, soil organic matter, soil organic C, EC, total N, P, K, Ca, Mg, Na, Al, Zn and Mn). Number of leaves, fresh mass, dry mass and leaf area was collected at three harvests interval (28, 46 and 74 days after transplanting). After each harvest period, leaves were analysed for nutrient content (N, P, K, Ca, Mg, Zn, Cu, Mn and B). During the final harvest crops were uprooted and root biomass (fresh mass, dry mass and root length) were recorded. Analysis of variance (ANOVA) were conducted on all data using Genstat package 18th addition. Differences between treatment means were separated using the least significant differences (LSD) procedure and correlations analysis was determined using Pearson’s simple correlation coefficient. Macadamia husk compost application had a significant effect on soil bulk density and water holding capacity. Addition of macadamia husk compost significantly increased soil pH, OC, N, C: N K, P, Mg, Ca, Na, Al, Zn, Ca and Mn. In contrast, addition of macadamia husk compost had no effect on soil EC. Yield components (number of leaves, fresh mass, dry mass), root biomass, root length and leaf area increased with application of macadamia husk compost. Yield components, root biomass, root length and leaf area were significantly affected (p<0.01) by harvesting time. Yield components in the second cropping season was greater than yield components in the first season. Macadamia husk compost application showed no significant effect on leaf nutrient content of Chinese cabbage. However, leaf nutrient content was affected by harvesting time. It is evident from the results of this study that macadamia husk compost affects soil fertility and plant production. The results suggest that macadamia husk compost has a potential to be used as a reliable fertilizer by famers especially smallholder farmers who struggle to buy inorganic fertilizer because they are expensive. Since this study was conducted over two seasons and compost effect tend to be long term, further research is needed on application of macadamia husk compost on soil properties and yield of other crops over wide range of soils. / NRF Macadamia husk compost Soil properties Chinese cabbage Crop yield Harvesting time 631.8750968257 Compost -- South Africa -- Limpopo Fertilizers -- South Africa -- Limpopo Macadamia nut -- South Africa -- Limpopo Brassica -- South Africa -- Limpopo
67	Birds, bats and arthropods in tropical agroforestry landscapes: Functional diversity, multitrophic interactions and crop yield Maas, Bea 20 November 2013 (has links) No description available. 630 birds bats arthropods multitrophic interactions agroforestry tropical landscape forest margin landscape functional diversity crop yield exclosure experiments experimental exclusion arthropod suppression avian predation ecosystem services mist netting point counts artificial plasticine caterpillar dummy experiment biological control monitoring mapping Lemon-bellied White-eye Grosbeak Starling Zosterops chloris Scissiostrum dubium Theobroma cacao cacao agroforestry cacao diseases cacao harvest cacao pests flying vertebrates aerial insectivores volant vertebrates logging breeding colony flower herbivory mesopredator release predatory insects ants spiders aphids Lepidoptera Coleoptera Lepidopteran larvae sustainable agriculture sustainable agroforestry management implications management recommendations applied managament extensive land use management landscape management conservation biodiversity-friendly dbh distance to forest forest proximity habitat conversion herbivory Indonesia insectivorous bats insectivorous birds invertebrates Central Sulawesi Napu valley leaf gleaning leaf herbivory pests insects phytophagous insects plant productivity remnant forest trees shade tree managament South-east Asia Paleotropics tropical landscape tree height landscape gradient local shade management Cacao Pod Borer Cherelle Wilt fruit abortion Helopeltis sulawesi deforestation habitat fragmentation land use intensification forest margin species critical decline predation experiment species-specific functions Southeast Asia biodiversity conservation mesopredators food security human welfare human well-being poverty alleviation poverty reduction smallholder cacao smallholder plantation cacao farmers sustainable cacao management landscape perspective landscape scale large scale field experiment long term field experiment human wellbeing economic importance economic value valuation ecosystem services landscape matrix yield quality yield quantity fruit productivity species identity species diversity species richness ecosystem functioning pesticide use chemical compounds complex food web tritrophic interactions wildlife-friendly rural livelihoods endangered bird species endangered bat species endemic endemic birds endemic bats beneficial impact IUCN Lore Lindu National Park bioindicator indicator species forest margin agricultural habitat agroforestry systems population density predation activity species activity cacao flower cacao pod Orthoptera linear mixed effect models Tukey Post Hoc Test AICc model selection generalized least squares restricted maximum likelihood model simplification species richness estimator species accumulation linear mixed-effects models Likelihood Ratio Test Maximum Likelihood nocturnal insects diurnal arthropods nocturnal arthropods diurnal insects landscape context Land- und Forstwirtschaft (PPN621302791)

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