Diss. (sammanfattning) Alnarp : Sveriges lantbruksuniv. / Härtill 4 uppsatser.

Winterfat (Krascheninnikovia lanata) a native shrub has superior forage quality for livestock and wildlife, and is important in the structure and the function of the Northern Mixed Prairie of North America. Seedbeds in the Northern Mixed Prairie are characterized by high fluctuations in temperature and soil water, especially at the soil surface during the spring under unpredictable weather conditions. High seedling mortality is a major limitation for establishing winterfat from direct seeding. Objectives of this study were to: 1) quantify germination responses to temperature and water potential; 2) predict seed germination and seedling emergence using constructed threshold models; and 3) investigate physiological mechanisms and the ecological relevance of model parameters. The constructed thermal and hydrothermal time models predicted germination time in most controlled temperature and water potential regimes with the modification of model assumptions in winterfat. For the first time, it was proved that winterfat seeds have a subzero base temperatures (Tb) for germination, achieving 43 to 67% germination at 3oC. The estimated Tb was lower in the large seeds (-4.5oC) than in the small seeds (-3.5oC) and the difference between seed collections was also about 1oC. Lower Tb favors large seeds to accumulate more thermal time at a given temperature, especially in early spring or fall when temperatures are low. Basic assumptions of hydrothermal time model, such as the constancy of model parameters, are invalid in winterfat. Model parameters varied with water potential, temperature and seed size within a seed collection. The predictability of constructed models is acceptable for seedling emergence only at optimal conditions in the field. Adverse seedbed conditions such as high soil temperatures (> 15oC) and limited soil water (< -0.5 MPa) reduced predictability of seedling emergence with the hydrothermal time model. Pre- and post-germination events that affect seed deterioration, seedling mortality and seedling elongation may reduce the predictability of the hydrothermal time model. Small seeds required approximately twice as long as large seeds to reach 50% germination at -1 to -3oC. Greater cold tolerance in large seeds was correlated with greater membrane integrity, less cold imbibition damage, higher contents of soluble cryoprotective sugars, such as glucose, raffinose and sucrose during germination at low temperature. These sugars prevent from dysfunctions of cell membrane and enzymes at freezing temperatures.

Winterfat (Krascheninnikovia lanata) a native shrub has superior forage quality for livestock and wildlife, and is important in the structure and the function of the Northern Mixed Prairie of North America. Seedbeds in the Northern Mixed Prairie are characterized by high fluctuations in temperature and soil water, especially at the soil surface during the spring under unpredictable weather conditions. High seedling mortality is a major limitation for establishing winterfat from direct seeding. Objectives of this study were to: 1) quantify germination responses to temperature and water potential; 2) predict seed germination and seedling emergence using constructed threshold models; and 3) investigate physiological mechanisms and the ecological relevance of model parameters. The constructed thermal and hydrothermal time models predicted germination time in most controlled temperature and water potential regimes with the modification of model assumptions in winterfat. For the first time, it was proved that winterfat seeds have a subzero base temperatures (Tb) for germination, achieving 43 to 67% germination at 3oC. The estimated Tb was lower in the large seeds (-4.5oC) than in the small seeds (-3.5oC) and the difference between seed collections was also about 1oC. Lower Tb favors large seeds to accumulate more thermal time at a given temperature, especially in early spring or fall when temperatures are low. Basic assumptions of hydrothermal time model, such as the constancy of model parameters, are invalid in winterfat. Model parameters varied with water potential, temperature and seed size within a seed collection. The predictability of constructed models is acceptable for seedling emergence only at optimal conditions in the field. Adverse seedbed conditions such as high soil temperatures (> 15oC) and limited soil water (< -0.5 MPa) reduced predictability of seedling emergence with the hydrothermal time model. Pre- and post-germination events that affect seed deterioration, seedling mortality and seedling elongation may reduce the predictability of the hydrothermal time model. Small seeds required approximately twice as long as large seeds to reach 50% germination at -1 to -3oC. Greater cold tolerance in large seeds was correlated with greater membrane integrity, less cold imbibition damage, higher contents of soluble cryoprotective sugars, such as glucose, raffinose and sucrose during germination at low temperature. These sugars prevent from dysfunctions of cell membrane and enzymes at freezing temperatures.

Sweetpotato (Ipomoea batatas (L.) Lam) is a major food resource of much of the world. Sweetpotato production is damaging to the soils in which it is grown due to the nature of production and the condition the soil is left to overwinter. There is potential for future regulation on the production of sweetpotato as related to runoff and nonpoint source pollution as the sediment from runoff erosion is easy to pinpoint in nearby waterways. Two studies were carried out to determine the effect of a reduced tillage practice and cover crops on the accumulation of nutrients in cover crops and yield of sweetpotato. The use of stale seed beds will reduce the number of tillage operations in a field and will allow earlier access to the field. Cover crops will benefit the soil with cover during rains while accumulating and recycling nutrients that may otherwise be lost and improving soil structure with the addition of organic matter.

Studies were conducted in 2004, 2005, and 2006 in south Texas to evaluate fall, winter, and spring weed control for commercial rice production, study tillage intensity and herbicide rate interactions for rice production, and determine the tolerance of red rice ecotypes from Texas rice fields using selected herbicides at varying rates. A single application of any herbicide or combination of herbicides was not adequate for weed control throughout the fall, winter, and spring. Fall applications of clomazone plus flumioxazin provided consistent weed control. Addition of flumioxazin to glyphosate provided excellent winter annual grass control with winter application. A residual herbicide, coupled with the proper contact herbicide is the key to extending control. In 2006, all tillage by herbicide treatments in all studies provided ≥ 90% control of all weed species. The conventional tillage treatment with low herbicide input provided the highest rice grain yield in 2005 and 2006, though they were not significantly different from the spring stale seedbed program with medium or high herbicide input in 2006. In 2006, fall stale seedbed treatments were among the lowest in yield. A stale seedbed program may be useful, but with substantial weed pressure, increasing the intensity of herbicide applications is necessary to overcome the absence of tillage. All rice ecotypes were adequately controlled by glyphosate and only one ecotype was found to be tolerant to 2x rates of both imazethapyr and imazamox. All ecotypes were adequately controlled by 2x rates of more than two of the four herbicides which included imazethapy, imazamox, glufosinate, and glyphosate. Ecotypes from the 3.2 group, genetically similar to the ecotype TX4, appear to be the most likely to exhibit tolerance to a given herbicide. Tolerance to glufosinate was found in 70% of the group 3.2 ecotypes. Sixty percent of ecotypes from group 3.1, genetically similar to Oryza rufipogon were not adequately controlled by glufosinate.

Studies were conducted in 2004, 2005, and 2006 in south Texas to evaluate fall, winter, and spring weed control for commercial rice production, study tillage intensity and herbicide rate interactions for rice production, and determine the tolerance of red rice ecotypes from Texas rice fields using selected herbicides at varying rates. A single application of any herbicide or combination of herbicides was not adequate for weed control throughout the fall, winter, and spring. Fall applications of clomazone plus flumioxazin provided consistent weed control. Addition of flumioxazin to glyphosate provided excellent winter annual grass control with winter application. A residual herbicide, coupled with the proper contact herbicide is the key to extending control. In 2006, all tillage by herbicide treatments in all studies provided ≥ 90% control of all weed species. The conventional tillage treatment with low herbicide input provided the highest rice grain yield in 2005 and 2006, though they were not significantly different from the spring stale seedbed program with medium or high herbicide input in 2006. In 2006, fall stale seedbed treatments were among the lowest in yield. A stale seedbed program may be useful, but with substantial weed pressure, increasing the intensity of herbicide applications is necessary to overcome the absence of tillage. All rice ecotypes were adequately controlled by glyphosate and only one ecotype was found to be tolerant to 2x rates of both imazethapyr and imazamox. All ecotypes were adequately controlled by 2x rates of more than two of the four herbicides which included imazethapy, imazamox, glufosinate, and glyphosate. Ecotypes from the 3.2 group, genetically similar to the ecotype TX4, appear to be the most likely to exhibit tolerance to a given herbicide. Tolerance to glufosinate was found in 70% of the group 3.2 ecotypes. Sixty percent of ecotypes from group 3.1, genetically similar to Oryza rufipogon were not adequately controlled by glufosinate.

The agricultural industry is constantly evolving with automation as one of the current main focuses. This thesis involves the automation of a seedbed tine harrow, specifically the control of the tillage depth. The tillage depth is instrumental to farming as it determines the quality of the tilth, how well clods are broken up, and how well the soil aggregates are sorted. Poor control of the tillage depth could result in a bad harvest for the farmer. To control the tillage depth, several pulse radar sensors are installed on the harrow. The sensors measure the distance from the tines of the harrow to the ground. This distance is used in a control-loop that controls the hydraulic actuators that lifts and pushes down the frame of the harrow. Because of the rough working conditions of the tine harrow, the pulse radar sensors are in danger of being damaged or disturbed. A sensor not working as intended will lead to poor control of the tillage depth or even an unstable control system. The purpose of this thesis is to develop diagnosis systems to detect and generate an alarm if the output of a sensor is faulty. Four different systems are developed, three machine learning approaches and one model based approach. To be able to test and train models without having to go out on a field with a real harrow, a test rig is available. The test rig emulates a harrow driving on a field and the tests are designed to imitate plausible sensor errors. The models trained on and tuned to the test rig data are validated with data gathered from a real tine harrow.  The validation data from the harrow reveal that the main difference between the field data and test rig data are the vibrations and the sensor heights. The test rig produces negligible amounts of vibrations whereas the vibrations on a real harrow are immense. These differences affect the performances of the models and some tuning have to be done to the models to accommodate for the vibrations. The performance of the model based approach is good and no larger adjustments have to be made to it. The machine learning models created from the test rig data do not work in the field and new models are trained using field data. The new models are accurate and show great potential; albeit, it would be necessary to collect a lot more data for further training. Specifically, training the machine learning models on varying heights. In conclusion, the test rig data is similar to the field data but the vibrations in the system is missing and the heights differ. The missing vibrations results in that the models do not work as intended on field data. The conventional diagnostics approach works, but the generated alarms are binary meaning that the alarm only reveal if the signal is good or bad and does not provide any nuance. The machine learning models does provide nuance, meaning that the model can detect errors, what is causing the error, and warn if an error is about to occur. However, the machine learning models need a lot of data to train on to make this happen.

Modeling the seedling recruitment microsite involves characterization of the soil environment of the shallow profile from which weed seedlings recruit. Understanding the environment of the seedling recruitment microsite is the prelude to weed emergence studies. Because of spatial and temporal heterogeneity of the recruitment microsite, sufficient measurements are often not feasible. An experiment was established in 2003 and 2004 across topography within an annually cropped field in south-central Manitoba to determine the effect that hillslope aspect and position, and soil residue and depth would have on microsite environment within the shallow seedling recruitment zone. Microclimatic, topographic, soil surface and soil properties were assessed in the context of the weed recruitment microsite. The soil water retention characteristic was measured by pressure plate to determine water availability to germinating seeds at the various topographic positions. The soil water characteristic was evaluated across topography and soil depth. Evaluation of the soil water characteristic by pedotransfer function indicated that a single soil water characteristic is representative of the recruitment zone. Field and laboratory experimental measurements were used as parameterization for the simultaneous heat and water (SHAW) model to generate continuous water and temperature profiles for the recruitment zone. Soil temperature and temperature fluctuation decreased with depth in the recruitment zone. Despite differences of texture, bulk density, and organic matter across topography and soil depth, the soil water characteristic differed only across topography. Soil water potential fluctuated considerably at the soil surface due to numerous precipitation events and direct evaporation. Implications for germinating seeds is that the seedling recruitment zone is influenced by spatial effects of topography and the vertical location of the seed microsite. Physical process based modeling used in this study to predict temperature and water within the seedling recruitment zone enables better understanding of interactions between above-ground microclimate and the recruitment microsite. Such interactions enable linkage between atmospheric models and recruitment models that can enhance our ability to evaluate crop management decisions.

Modeling the seedling recruitment microsite involves characterization of the soil environment of the shallow profile from which weed seedlings recruit. Understanding the environment of the seedling recruitment microsite is the prelude to weed emergence studies. Because of spatial and temporal heterogeneity of the recruitment microsite, sufficient measurements are often not feasible. An experiment was established in 2003 and 2004 across topography within an annually cropped field in south-central Manitoba to determine the effect that hillslope aspect and position, and soil residue and depth would have on microsite environment within the shallow seedling recruitment zone. Microclimatic, topographic, soil surface and soil properties were assessed in the context of the weed recruitment microsite. The soil water retention characteristic was measured by pressure plate to determine water availability to germinating seeds at the various topographic positions. The soil water characteristic was evaluated across topography and soil depth. Evaluation of the soil water characteristic by pedotransfer function indicated that a single soil water characteristic is representative of the recruitment zone. Field and laboratory experimental measurements were used as parameterization for the simultaneous heat and water (SHAW) model to generate continuous water and temperature profiles for the recruitment zone. Soil temperature and temperature fluctuation decreased with depth in the recruitment zone. Despite differences of texture, bulk density, and organic matter across topography and soil depth, the soil water characteristic differed only across topography. Soil water potential fluctuated considerably at the soil surface due to numerous precipitation events and direct evaporation. Implications for germinating seeds is that the seedling recruitment zone is influenced by spatial effects of topography and the vertical location of the seed microsite. Physical process based modeling used in this study to predict temperature and water within the seedling recruitment zone enables better understanding of interactions between above-ground microclimate and the recruitment microsite. Such interactions enable linkage between atmospheric models and recruitment models that can enhance our ability to evaluate crop management decisions.

Following wildfires, plant materials are direct-seeded to limit erosion and annual weed invasion. Seedlings often fail to establish because selected plant materials are not always well adapted to local soil moisture and temperature conditions. In an effort to help improve plant materials selection and to evaluate sites potential revegetation, we have worked toward developing methodology to predict germination and root growth based on site specific soil moisture and temperature conditions. First, we characterized the seedbed environment of 24 sagebrush (Artemisia spp.) steppe sites throughout the Intermountain West to determine the wet-thermal time of five temperature ranges relevant to germination response and thermal-time model accuracy (Chapter 1). Second, we predicted potential germination for 31 plant materials at those same sites (Chapter 2). Third, in preparation to predict root growth at multiple sites, we characterized the drying patterns and the associated plant-available water for in the seedling root zone across nine woodland (Juniperus spp. and Piñus spp.) sites (Chapter 3). For all of these studies, we determined the effects of tree reduction and tree infilling phase at time of tree reduction. Our key findings are that seedbeds generally sum most wet-thermal time at temperature ranges where the germination rates fit thermal accumulation models quite well (R2 ≥ 0.7). The majority of plant materials summed enough wet-thermal time for a potential germination at most sites during the fall, early spring, and late spring. Soil drying primarily occurs from the soil surface downward. Drying rates and Plant available water associated with the first drying event increased with increasing soil depth. Root zone (1-30 cm) plant-available water increased before and decreased after the first spring drying event with increasing soil depth. Tree removal with increasing pretreatment tree infilling phase generally added progress toward germination, plant available water, and wet-thermal time in the seedbed and root zones of the sagebrush steppe in the Great Basin. Because soil moisture and temperature does not appear to be limiting for potential germination, combining germination and root growth models to create a more comprehensive model may allow for a more robust prediction for seedling survival. For either root growth or combined germination and root growth models, plant available water and wet-thermal time before the first spring drying period hold the most potential for successfully predicting seedling survival.