The cultivation of fast growing tree species on arable land is deemed to be one of the most promising alternatives amongst the approaches to the cultivation of renewable resources currently available. As the site factors influencing the growth of poplar and willow have not yet been sufficiently identified and quantified, it is not possible to provide reliable national yield expectations as a function of the prevailing soil and climate conditions at a particular site on the basis of the data currently available. The main objective of this study, therefore, was to develop a supraregional site-based yield model for the assessment of yield expectations for poplar and willow grown on arable land over short rotations. In order to achieve this goal, a total of 38 research sites were established across as many regions of Germany as possible. The clones selected for the experiment were the poplar clones Max 1, Hybride 275 (H 275) and AF 2, and the willow clones Inger and Tordis. Each site was also characterised, on the basis of climate and soil data. From the site parameters recorded, variables were defined for modelling purposes. The climate variables comprised temperature (T), precipitation (N) and aridity index (TI) derived from total or average values from selected periods during the year and during the vegetation period. Within the variables the months were indexed as numbers (January = 1), periods were separated by comma. Parameters of the German soil appraisal such as the condition grade (ZS), the soil quality index value (BZ) and the arable land quality index value (AZ) were used as soil variables. The selected texture parameters were the proportions of sand (S[%]), silt (U[%]) and clay (T[%]). Variables of the pore space parameters were the available field moisture capacity (nFK), the air capacity (LK) and the dry bulk density (TRD). Both the texture and the pore space variables referred to a soil depth of 0 to 60 cm. To increase the precision of the results, site clusters were derived, differentiated by main soil type, and the variables were aggregated. The dGZ, measured in odt ha-1 a-1,at the end of the first three-year rotation was chosen as the dependent variable. The main results of the study are outlined in the following: Negative correlations between dGZ and temperatures in the vegetation period, especially in the months July to September, were observed on the sandy and loamy soils. Precipitation variables always had a positive effect on the growth of poplar and willow across all clones and site clusters. Taking into consideration all experimental sites, precipitation in the period from May to July was most important. The parameters of the German soil appraisal revealed a moderate correlation with growth for all sites. From the appraisal of all sites and of the individual site clusters it became apparent that U[%] represents the most important texture variable for poplar and willow growth. Considered for all sites simultaneously, the nFK had the greatest significance for growth across all of the parameters examined. The site-based yield models were all univariate and often comprised aggregated variables. Under consideration of all sites, the model predictors for Max 1, Inger and Tordis were (nFK * TI5.7) and (nFK * N5.7) for AF 2. Each model was calculated with the inverse or sigmoid approach and revealed an R²korr between 0.45 and 0.64 with a RMSE of an average of 2.0 odt ha-1 a-1. The division into site clusters improved the accuracy of the models considerably. In the sandy site cluster, the models exhibited an R²korr of 0.77 to 0.97 and an RMSE of 0.95 to 1.36 odt ha-1 a-1. These comprised (U[%]* N6.7) for Max 1 and F 2, (S[%]/ TI6.7) for H 275 and (S[%]* T7.8) for Inger and Tordis. For the silty site cluster, significant models could only be determined for the clones Max 1 and Tordis. The model predictor for Max 1 was (BZ * TI4.5) and for Tordis solely (BZ). The calculated R²korr values were 0.84 and 0.95 with a corresponding RMSE of 0.22 and 0.62 odt ha-1 a-1, respectively. For the loamy soils the models for Max 1 and Inger comprised the variable nFK, for AF 2 the variables (nFK * N5.6). R²korr varied between 0.86 and 0.98 with RMSE between 0.56 and 1.21 odt ha-1 a-1.

The cultivation of fast growing tree species on arable land is deemed to be one of the most promising alternatives amongst the approaches to the cultivation of renewable resources currently available. As the site factors influencing the growth of poplar and willow have not yet been sufficiently identified and quantified, it is not possible to provide reliable national yield expectations as a function of the prevailing soil and climate conditions at a particular site on the basis of the data currently available. The main objective of this study, therefore, was to develop a supraregional site-based yield model for the assessment of yield expectations for poplar and willow grown on arable land over short rotations. In order to achieve this goal, a total of 38 research sites were established across as many regions of Germany as possible. The clones selected for the experiment were the poplar clones Max 1, Hybride 275 (H 275) and AF 2, and the willow clones Inger and Tordis. Each site was also characterised, on the basis of climate and soil data. From the site parameters recorded, variables were defined for modelling purposes. The climate variables comprised temperature (T), precipitation (N) and aridity index (TI) derived from total or average values from selected periods during the year and during the vegetation period. Within the variables the months were indexed as numbers (January = 1), periods were separated by comma. Parameters of the German soil appraisal such as the condition grade (ZS), the soil quality index value (BZ) and the arable land quality index value (AZ) were used as soil variables. The selected texture parameters were the proportions of sand (S[%]), silt (U[%]) and clay (T[%]). Variables of the pore space parameters were the available field moisture capacity (nFK), the air capacity (LK) and the dry bulk density (TRD). Both the texture and the pore space variables referred to a soil depth of 0 to 60 cm. To increase the precision of the results, site clusters were derived, differentiated by main soil type, and the variables were aggregated. The dGZ, measured in odt ha-1 a-1,at the end of the first three-year rotation was chosen as the dependent variable. The main results of the study are outlined in the following: Negative correlations between dGZ and temperatures in the vegetation period, especially in the months July to September, were observed on the sandy and loamy soils. Precipitation variables always had a positive effect on the growth of poplar and willow across all clones and site clusters. Taking into consideration all experimental sites, precipitation in the period from May to July was most important. The parameters of the German soil appraisal revealed a moderate correlation with growth for all sites. From the appraisal of all sites and of the individual site clusters it became apparent that U[%] represents the most important texture variable for poplar and willow growth. Considered for all sites simultaneously, the nFK had the greatest significance for growth across all of the parameters examined. The site-based yield models were all univariate and often comprised aggregated variables. Under consideration of all sites, the model predictors for Max 1, Inger and Tordis were (nFK * TI5.7) and (nFK * N5.7) for AF 2. Each model was calculated with the inverse or sigmoid approach and revealed an R²korr between 0.45 and 0.64 with a RMSE of an average of 2.0 odt ha-1 a-1. The division into site clusters improved the accuracy of the models considerably. In the sandy site cluster, the models exhibited an R²korr of 0.77 to 0.97 and an RMSE of 0.95 to 1.36 odt ha-1 a-1. These comprised (U[%]* N6.7) for Max 1 and F 2, (S[%]/ TI6.7) for H 275 and (S[%]* T7.8) for Inger and Tordis. For the silty site cluster, significant models could only be determined for the clones Max 1 and Tordis. The model predictor for Max 1 was (BZ * TI4.5) and for Tordis solely (BZ). The calculated R²korr values were 0.84 and 0.95 with a corresponding RMSE of 0.22 and 0.62 odt ha-1 a-1, respectively. For the loamy soils the models for Max 1 and Inger comprised the variable nFK, for AF 2 the variables (nFK * N5.6). R²korr varied between 0.86 and 0.98 with RMSE between 0.56 and 1.21 odt ha-1 a-1.:Inhaltsverzeichnis 1 Einleitung 1 1.1 Problemstellung 1 1.2 Zielstellung 3 2 Hintergrund 4 2.1 Energieziele Deutschlands 4 2.1.1 Flächen- und Nutzungskonkurrenz mit der Forstwirtschaft 4 2.1.2 Flächen- und Nutzungskonkurrenz mit der Landwirtschaft 5 2.2 Anbau schnellwachsender Baumarten 6 2.2.1 Anbau im Kurzumtrieb 7 2.3 Verwendete Baumarten in Kurzumtriebsplantagen 11 2.3.1 Pappel 11 2.3.2 Weide 13 2.3.3 Anmerkung Züchtung 14 2.4 Leistung von Kurzumtriebsplantagen mit Pappel und Weide in zwei- bis dreijähriger Umtriebs-zeit 15 2.5 Ertragsdynamik und leistungsbeeinflussende Faktoren von Kurzumtriebsplantagen 17 2.5.1 Baumart und Klon 18 2.5.2 Umtriebszeit und Bestandesdichte 19 2.5.3 Standortanforderungen 21 2.5.3.1 Reichliche Wasserversorgung 21 2.5.3.2 Lockere Böden 22 2.5.3.3 Ausreichende Mineralstoffzufuhr 23 2.5.3.4 Wachstumsangepasste Bodenreaktion 23 2.5.3.5 Wärme 23 2.5.3.6 Bodenschätzungskennwerte 24 2.5.3.7 Textur 24 2.5.3.8 Standorte der Pappel 24 2.6 Waldwachstumsmodellierung mit Schwerpunkt in Kurzumtriebsplantagen 25 2.6.1 Empirische Modelle 26 2.6.1.1 Pappelmodell nach ALI (2009) 26 2.6.1.2 Pappel- und Weidenmodell nach AYLOTT et al. (2008) 27 2.6.1.3 Leistungsbeeinflussende Standorteigenschaften für Pappel und Weide nach BERGANTE et al. (2010) 28 2.6.2 Mechanistische Modelle 29 3 Material und Methoden 33 3.1 Flächenanlage und Versuchsdesign 33 3.1.1 Flächenvorbereitung und Pflegemaßnahmen 37 3.1.2 Prüfglieder 39 3.1.2.1 Pappelklon Max 1 39 3.1.2.2 Pappelklon H 275 39 3.1.2.3 Pappelklon AF 2 40 3.1.2.4 Weidenklon Inger 41 3.1.2.5 Weidenklon Tordis 41 3.2 Klimatische Standortcharakterisierung 42 3.2.1 Temperatur 44 3.2.2 Niederschlag 46 3.3 Bodenkundliche Standortcharakterisierung 47 3.3.1 Profilansprache und allgemeine Standortinformationen 48 3.3.2 Durchführung der Probennahme 48 3.3.3 Bodenphysikalische Untersuchungen 49 3.3.4 Grundwassereinfluss 49 3.3.5 Bodenchemische Untersuchungen 50 3.3.6 Bodenschätzungskennwerte 50 3.3.7 Zusammenfassende Betrachtung der bodenkundlichen Charakteristika 51 3.4 Bestandesaufnahmen und Ernte 55 3.4.1 Anwuchs- und Überlebensrate 55 3.4.2 Anzahl an Höhentrieben 55 3.4.3 Höhe 55 3.4.4 Durchmesser 55 3.4.5 Ertragsbestimmung 55 3.4.5.1 Biomassefunktionen 56 3.4.5.2 Systematische Teilbeernteung der Kernparzelle 56 3.5 Identifizierung von leistungsbeeinflussenden Standorteigenschaften und Entwicklung standortbasierter Ertragsmodelle 57 3.5.1 Variablenwahl und -bildung 58 3.5.1.1 Klima 58 3.5.1.2 Bodenschätzung 59 3.5.1.3 Textur 59 3.5.1.4 Porenraum 59 3.5.1.5 Aggregierte Variablen 60 3.5.1.6 Weitere Einflussgrößen 60 3.5.1.7 Abhängige Variable 61 3.5.2 Standortcluster 61 3.6 Datenbasis der standortbasierten Ertragsmodellierung 61 3.7 Ertragsteigerungfaktor 63 3.8 EDV und Statistik 65 3.8.1 Varianzanalyse 65 3.8.2 Korrelationsanalyse 66 3.8.3 Regressionsanalyse 66 3.8.3.1 Nicht-lineare Regression 68 3.8.3.2 Evaluierungsgrößen 69 4 Ergebnisse 71 4.1 Waldwachstumskundliche Ergebnisse 71 4.1.1 Anwuchs- und Überlebensrate 71 4.1.2 Anzahl an Höhentrieben 72 4.1.3 Übersicht Bestandesdimensionen und Wuchsleistung 73 4.1.4 Höhe 75 4.1.5 Durchmesser 77 4.1.6 Durchschnittlicher Gesamtzuwachs 81 4.1.7 Biomassefunktionen 84 4.1.7.1 Wahl der unabhängigen Variablen 84 4.1.7.2 Einfluss der Gattung und des Klons 85 4.1.7.3 Einfluss der Bestandesmittelhöhe 85 4.1.7.4 Allgemeingültige Biomassefunktionen 87 4.2 Leistungsbeeinflussende Standorteigenschaften 88 4.2.1 Temperatur 88 4.2.2 Niederschlag 90 4.2.3 Trockenheitsindex 92 4.2.4 Bodenschätzungskennwerte 94 4.2.5 Textur 96 4.2.6 Porenraum 99 4.2.7 Korrelation zwischen unabhängigen Variablen 102 4.2.8 Aggregierte Variablen 102 4.2.9 Weitere Einflussgrößen 105 4.2.9.1 Vornutzung 105 4.2.9.2 Bodentyp 106 4.2.9.3 Grundwasser 106 4.3 Standortbasierte Ertragsmodellierung 107 4.3.1 Standortcluster ALL 107 4.3.2 Standortcluster S 110 4.3.3 Standortcluster U 113 4.3.4 Standortcluster L 115 4.4 Ertragssteigerungsfaktoren und Ertragssteigerung in Folgerotationen 118 5 Diskussion 122 5.1 Material und Methodik 122 5.1.1 Versuchsflächen 122 5.1.2 Modellierung und Variablenbildung 123 5.2 Waldwachstumskundliche Ergebnisse 124 5.2.1 Anwuchs- und Überlebensrate 124 5.2.2 Anzahl an Höhentrieben 125 5.2.3 Höhe 125 5.2.4 Durchmesser 126 5.2.5 Durchschnittlicher Gesamtzuwachs 126 5.2.6 Biomassefunktionen 128 5.3 Leistungsbeinflussende Standorteigenschaften 129 5.3.1 Temperatur 129 5.3.2 Niederschlag 130 5.3.3 Trockenheitsindex 132 5.3.4 Bodenschätzungskennwerte 132 5.3.5 Textur 133 5.3.6 Porenraum 134 5.3.7 Aggregierte Variablen 135 5.3.8 Weitere Einflussgrößen 136 5.4 Standortbasierte Ertragsmodellierung 136 5.4.1 Standortcluster ALL 137 5.4.2 Standortcluster S 138 5.4.3 Standortcluster U 138 5.4.4 Standortcluster L 138 5.4.5 Vergleich mit anderen Modellen 138 5.5 Ertragssteigerungsfaktoren und Ertragsteigerung in Folgerotationen 140 6 Schlussfolgerungen und Ausblick 141 7 Zusammenfassung 144 8 Literatur 156 9 Anhang 176