This study investigated the crop density in a New Holland BB960 (branch of CNH Global N.V.) large square baler as examined by crop trajectory from the precompression room to the bale chamber. This study also examined both the top and bottom plunger pressures and critical factors affecting the final top and bottom bale densities.<p>The crop trajectories (wad of crop) were measured using a high-speed camera from the side of the baler through viewing windows. The viewing windows were divided into four regions for determining the crop displacement, velocity and acceleration. Crop strain was used to evaluate the potential change in density of the crop before being compressed by the plunger. Generally, the vertical crop strain was found to be higher in the top half of the bale compared to the bottom. <p>Average strain values for side measurements were 12.8% for the top and 2.1% for the bottom. Plunger pressures were measured to compare peak pressures between the top and bottom halves of each compressed wad of crop, and to develop pressure profiles based on the plungers position. Results of comparing the mean peak plunger pressures between the top and bottom locations indicated the mean pressures were significantly higher at the top location with the exception of one particular setting. Resulting pressure profile graphs aided in qualitatively describing the compression process for both top and bottom locations.<p>A stepwise regression model was developed to examine the difference in material quantity in the top half of the bale compared to the bottom, based on bale weights. The model indicated that flake setting, stuffer ratio and number of flakes had the greatest effect on maintaining consistent bale density by comparing top to bottom halves of each bale. The R2 (coefficient of determination) value for the developed model was of 59.9%. The R2 was low although could be accounted for due to the limited number of data points in the developed model.

The environmental risk of growing genetically modified (GM) crops and particularly the spreading of GM genes to related non-GM crops is currently a concern in European agriculture. Because the risks of contamination are linked to the spatial and temporal arrangements of crops within the landscape, scenarios of crop arrangement are required to investigate the risks and potential coexistence measures. However, until recently, only manual methods were available to create scenarios. This thesis aims to provide a flexible referenced tool to create such scenarios. The model, called LandSFACTS, is a scientific research tool which allocates crops into fields, to meet user-defined crop spatio-temporal arrangements, using an empirical and statistical approach. The control of the crop arrangements is divided into two main sections: (i) the temporal arrangement of crops: encompassing crop rotations as transition matrices (specifically-developed methodology), temporal constraints (return period of crops, forbidden crop sequences), initial crops in fields regulated by temporal patterns (specifically-developed statistical analyses) and yearly crop proportions; and (ii) the spatial arrangements of crops: encompassing possible crops in fields, crop rotation in fields regulated by spatial patterns (specifically-developed statistical analyses), and spatial constraints (separation distances between crops). The limitations imposed by the model include the size of the smallest spatial and temporal unit: only one crop is allocated per field and per year. The model has been designed to be used by researchers with agronomic knowledge of the landscape. An assessment of the model did not lead to the detection of any significant flaws and therefore the model is considered valid for the stated specifications. Following this evaluation, the model is being used to fill incomplete datasets, build up and compare scenarios of crop allocations. Within the GM coexistence context, the model could provide useful support to investigate the impact of crop arrangement and potential coexistence measures on the risk of GM contamination of crops. More informed advice could therefore be provided to decision makers on the feasibility and efficiency of coexistence measures for GM cultivation.

This study evaluates how future climatic projections will affect the suitability of bambara groundnut (Vigna subterranean(L) Verdc.), a type of underutilised crop in Sikasso and Ségou, southern Mali. This study was performed using a simulation approach, which considered the potential changes in suitability due to projected changes in two climate variables; temperature and precipitation. Monthly outputs of the two climate variables from 10 CORDEX bias-corrected regional projections under the Representative Concentration Pathway (RCP) 8.5 were applied. The suitability index range of bambara groundnut was projected, using the Ecocrop suitability model, considering three time periods: historical (1975-2005), near-term (2011-2040), and end of century (2070-2099). The results of this study showed that the model captured a long planting window for the crop in the regions across the time periods. With the projected increase in future climatic conditions, the suitability index range of bambara groundnut is projected to increase across the months suitable for planting the crop. Furthermore, Sikasso is projected to maintain a high suitability index in the near-term, and by the end of century, Ségou is expected to experience a potential increase in suitability index range and suitable areas, especially by the end of century. The results indicate that the CORDEX projections and suitability modelling technique applied in the study captured well the suitability of bambara groundnut in the regions which can help the farmers in making planting decisions. These results suggest an opportunity for optimal utilisation of the crop in the regions, as with a long planting window and expansion in suitable areas, farmers in the regions can plant multiple times and have more suitable areas to cultivate. This study contributes to improving the decision-making surrounding the promotion of underutilised crops as part of the strategy for climate-resilient agriculture and food security in Sikasso and Ségou.

<p><a>In the United States and Canada, weed interference in corn (<i>Zea mays </i>L.) costs farmers nearly $4 billion per year. Weed control has been achieved primarily through herbicides and tillage. As no-till corn acres have increased, dependence on herbicides has also increased. Herbicide-resistant weed infestations have pressured many growers into other weed management practices, such as adding winter cover crops into crop rotations. Field experiments were conducted in 2017 through 2018 and 2018 through 2019 at three locations in Indiana to determine residual herbicide efficacy applied at cereal rye termination and after corn planting in cereal rye (<i>Secale cereale</i> L.) and winter-fallow no-till corn. Weed biomass and density suppression was dependent on weed species and was influenced by cereal rye biomass at termination. Weed biomass was suppressed by up to 84% by cereal rye alone. Weed biomass reduction by a residual herbicide premix was similar in both cereal rye and non-cover crop treatments in most site-years, however cereal rye and the residual herbicide premix together resulted in decreased giant ragweed (<i>Ambrosia trifida </i>L.) and summer annual grass biomass compared to the residual herbicide premix applied alone in one site year. Late-season grass weed density was reduced by residual herbicides, but was unaffected by cover crop treatment. Late-season common cocklebur density and biomass increased in cereal rye treatments compared to non-cover crop treatments. </a></p> <p>Other field experiments were conducted at the same locations in 2017 through 2018 and 2018 through 2019 to determine the effect of cover crop species, termination timing, and chemical cover crop termination strategies on weed control and corn yield. Crimson cover (<i>Trifolium incarnatum </i>L.), cereal rye, and a cereal rye/crimson clover mix were terminated two weeks before, at, and two weeks after corn planting. All plots were terminated using glyphosate and atrazine, however others were also terminated with dicamba and acetochlor. The addition of acetochlor generally reduced early-season weed biomass or density, but not in cereal rye and cover crop mix treatments that were terminated at or after corn planting. Late-season summer annual grass biomass was reduced when cover crop biomass at termination was over 8000 kg ha^-1. Late-season common cocklebur density in 2018 was 450% to 800% higher in cover crops containing cereal rye, compared to crimson clover treatments. Corn yield was reduced by 23% to 67% in cereal rye and cover crop mix treatments in two out of three site-years in 2018, however corn yield was not reduced by crimson clover in either year, nor by cereal rye or the cover crop mix in 2019.</p>

By 2050, food consumption and agricultural water use will increase as a result of a global population that is projected to reach 9 billion people. To address this food and water security challenge, there has been increased attention towards the concept of sustainable agriculture, which has a broad aim of securing food and water resources while preserving the environment for future generations. An element of this is the use of precision agriculture, which is designed to provide the right inputs, at the right time and in the right place. In order to optimize nutrient application, water intakes, and the profitability of agricultural areas, it is necessary to improve our understating and predictability of agricultural systems at high spatio-temporal scales. The underlying goal of the research presented herein is to advance the monitoring of croplands and crop yield through high-resolution satellite data. In addressing this, we explore the utility of daily CubeSat imagery to produce the highest spatial resolution (3 m) estimates of leaf area index and crop water use ever retrieved from space, providing an enhanced capacity to provide new insights into precision agriculture. The novel insights on crop health and conditions derived from CubeSat data are combined with the predictive ability of crop models, with the aim of improving crop yield predictions. To explore the latter, a sensitivity analysis-linked Bayesian inference framework was developed, offering a tool for calibrating crop models while simultaneously quantifying the uncertainty in input parameters. The effect of integrating higher spatio-temporal resolution data in crop models was tested by developing an approach that assimilates CubeSat imagery into a crop model for early season yield prediction at the within-field scale. In addition to satellite data, the utility of even higher spatial resolution products from unmanned aerial vehicles was also examined in the last section of the thesis, where future research avenues are outlined. Here, an assessment of crop height is presented, which is linked to field biomass through the use of structure from motion techniques. These results offer further insights into small-scale field variabilities from an on-demand basis, and represent the cutting-edge of precision agricultural advances.

Agriculture plays a crucial role in ensuring global food security, but its intensification has resulted in groundwater depletion, particularly in arid regions like Saudi Arabia. Although the significance of agriculture in Saudi Arabia is well-recognized, there is limited understanding of the agroinformatics aspects required to manage them at a regional or national level. High-resolution satellite data has the potential to provide valuable insights, including the number, location, size, and crop type of agriculture fields, as well as patterns of behavior. Machine learning techniques have emerged as the state-of-the-art methods to extract agricultural informatics from satellite data due to their efficiency and accuracy. However, in regions like Saudi Arabia where even basic agroinformatics data is not routinely available, the lack of ground truth data required to drive machine learning approaches is a critical consideration in model selection. One potential solution is to create a dataset by collecting field data or interpreting satellite imagery using human interpreters, but this can be time-consuming and labor-intensive. Another option is to explore unsupervised techniques that require limited or no ground truth data, but this can result in accuracy sacrifices. Ultimately, we aim to strike a balance between usability, data availability, accuracy, and computational efficiency when developing solutions to address these issues. In this study, a hybrid machine learning framework was developed to accurately delineate agricultural fields in a regional scale of Saudi Arabia, with high accuracy and stable transferability when applied to different temporal and spatial regions across the country. The framework was used to conduct the first retrospective analysis of agriculture activity over three decades on a national scale, including changes in the number, acreage, field size distribution, and the dynamics of expansion and contraction of center-pivot fields. Additionally, a novel unsupervised framework was developed to identify within-field dynamics and map critical crop phenology stages and crop types, providing valuable information for in-field agricultural practices. The agroinformatics retrieved in this study can provide valuable insights for policymakers, farmers, and other stakeholders involved in agriculture and environmental management and exhibited significant implications for the management and sustainability of agricultural systems in Saudi Arabia and other regions facing similar challenges.

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Agricultural economists have long studied crop yields and risk to help farm-level risk management. Producers face difficult decisions every year regarding market prices, management practices, and the uncertainty of weather. In our research, we use crop yield records while incorporating the modern portfolio theory to find the optimal planting portfolios giving a specific risk level. Our assets are on corn, cotton, and soybeans yields from the Mississippi Delta region. This study is unique because there are not any previous studies using crop histories linked to the modern portfolio theory. The main idea is to realize how much of each asset or what percentage to invest in out of the specific portfolio. By having these portfolios readily available for farmers, we aim to diminish the risk to help producers with springtime decision-making. Armed with these findings, we can better understand the economic implications of how crop rotations factor into farm-level risk management.

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