[Truncated abstract] In southern Australian farming systems the replacement of deep-rooted perennial native vegetation with shallow-rooted annual crops and pastures has resulted in rising groundwater tables and an increased incidence of dryland salinity. It has been suggested that to address this issue by restoring hydrological balance, large areas of agricultural land need to be vegetated with perennial plants. One of the most agriculturally productive ways to do this is to introduce perennial pastures, both into upslope groundwater

The effectiveness of land sharing and land sparing (LS/LS) approaches to conservation in the face of rising agricultural demands has been widely debated. While numerous studies have investigated the LS/LS framework from an ecological lens (yield-biodiversity relationship) the relevance of the framework to real life depends on broader considerations. Some of the key caveats include: i) limited knowledge regarding the feasibility of interventions given diverse stakeholders’ interests, ii) the social acceptability (uptake) of these contrasting strategies to direct land users, and iii) limited knowledge regarding their impacts on individuals’ livelihoods and food security. Without considering these social science dimensions proponents of the framework risk an incomplete picture that is not grounded in local realities and can paradoxically force into opposition the very conservation and development interests they seek to reconcile. Using a Companion Modelling approach, which comprises the development of a role-playing game (RPG) and an agent-based model (ABM), this thesis addressed these caveats. The research was based in the Nilgiris of Western Ghats India, a tropical agricultural system at the forest frontier. The main findings show that through engaging local stakeholders in a participatory process, plausible land use strategies that align with their objectives could be identified. Stakeholders proposed three land use interventions. Two of them resemble a form of land sparing (‘monofunctional’ landscapes) on the farms: sparing land for Wildflower Meadows or Tree Plantations while increasing yield on the remaining land. The third intervention asks farmers to accept yield penalties for Intercropping more trees on their farms, a form of land sharing (‘integrated’ landscapes). In terms of decision-making regarding the adoption of these three interventions by direct land users, the study reveals several findings. Firstly there are three main types of motivations that influence farmers’ decision to adopt interventions, in order of importance: monetary benefits, pro-environmental motivations and social norms. Secondly, land use, the type of management preferred on the farm and whether land users accept trees on the farm or not are factors that influence what type of interventions is socially acceptable on individual farms. These factors have been detected in the in-depth household survey and also validated by the RPG. When assessing the adoption of the three interventions, ex ante their implementation, using an ABM, there are some important differences observed between the interventions. Wildflower Meadows is the intervention adopted by the largest number of households, whereas Intercropping is adopted across the largest area of land. Forest Plantations is significantly more unpopular than the other two interventions. The third line of investigation, about the outcomes of adoption, has important policy implications. Adding a socioeconomic dimension to the ecological one adds a level of complexity and creates a less straightforward choice between the LS/LS strategies. None of the three interventions can provide optimal outcomes for production, aspects of biodiversity conservation, livelihoods and food security. Each intervention has indicators that score better compared to the other two interventions. The findings demonstrate that the ecological focus of the LS/LS framework is insufficient to deal with real-world complexities and lends itself to overly simplistic policy prescriptions. More meaningful policies could be achieved when bridging natural and social sciences to better understand the merits and limitations of the LS/LS approaches.

The overall objective of the whole study was to assess whether conservation agriculture (CA) systems can work in the Eastern Cape Province (EC). The CA systems were engaged through cover cropping to address land degradation problems by emphasizing high biomass production in order to realize short term benefits such as moisture conservation, weed suppression and soil fertility benefits under rainfed conditions in the central region of the Eastern Cape province. Since rainfall is the most limiting factor to crop production in the EC, a within season rainfall distribution analysis was conducted to expose the quality of the season (onset, end and duration) and hence the feasibility of CA systems to guide agronomic decisions by farmers in EC. To assess season parameters, thirty four years of daily rainfall was collected from the University of Fort Hare Research station and used to conduct the rainy pentad (5 day rainfall totals) analysis and the daily rainfall analysis using INSTAT software programme. Based on the pentad analysis, results showed that Alice does not have a rainy season in 1 out of 2 years (50% probability) but has one in 1 out of 4 years (25% probability level). This criterion proved to be harsher and conservative when compared to the daily rainfall approach which is more precise in measuring trends on season parameters. The daily rainfall analysis indicated a 65% feasibility for the dry land cropping systems in the EC. The pentad analysis however was effective in illustrating seasonality and it showed that the wet season begins on the 1st of November, ending on the 22nd of March lasting for 140 days. Though the season duration appeared too long, the existence of dry spells during critical growth stages adversely affects the quality of the season. The daily rainfall analysis also managed to derive a signal which can guide planting decisions. For planting to be successful, this analysis determined that 20 mm of rain should be received in two consecutive days after the 1st of November. A screening trial for cover crop biomass production and weed suppression was conducted on-station Fort Hare Research Farm (32°46' S and 26° 50' E), and Msobombvu village (MSBV) (32°44' S, and 26° 55' E) over two seasons (2007/08 and 2008/09). Six summer cover crops i.e. cowpea (Vigna unguiculata), dolichos lablab (Dolichos argenteus), sunnhemp (Crotalaria juncea), buckwheat (Fagopyrum sagittatum), forage sorghum (Sorghum bicolor) and sunflower (Helianthus annus) were evaluated for biomass yield, and weed suppression. Decomposition rates, moisture conservation and residual effects of these cover crops on the succeeding main crop were also evaluated under dryland conditions. The screening trial was laid in randomized complete block design replicated three times. Forage sorghum (Sorghum bicolor) and sunflower (Helianthus annus) were identified as high biomass producers and their dry matter yields ranged from 8 -12 t ha-1. These cover crops can be useful in generating high biomass in rainfed cropping systems in the EC. Other cover crops produced 3 - 4 t ha-1 of biomass which fell short of the 6 t ha-1 expected benchmark. However, these biomass yields were important in weed management since all cover crop species showed a similar degree of weed suppression which surpassed the weed fallow treatment. As dead mulches, the cover crops failed to show residual moisture conservation and weed control benefits for the succeeding maize crop mainly because of poor residue persistence, and low harvestable fallow rainfall. Buckwheat (Fagopyrum esculentum), was selected for further investigations in a follow up trial on station in 2008/09 season because of its weed smothering qualities, suitability to short cycle rotations, and possible allelopathic properties. The trial aimed at finding weed and cost effective management options of buckwheat that are none detrimental to the succeeding maize crop. Results showed that cropping systems where buckwheat is followed by a main crop may not work as they are unprofitable with respect to R100 rand invested. Though perceived to have allelopathic properties, buckwheat failed to demonstrate the possibilities of allelopathic action against weeds. Intercropping trial was conducted on-station in 2007/8-2008/09 seasons to try and find better ways of fitting legume cover crops into maize based cropping systems without compromising production of staple cereals on limited landholdings. The trials evaluated three factors in factorial combination, cover crop planting date, intercropping strategy, and cover crop species. The trial was laid as 2 x 2 x 3 factorial arranged in a split-split plot design. The main plot factor was cover crop planting date, cover crops simultaneously planted with maize and cover crop planted two weeks after planting maize (DKC 61-25). The sub-plot factor was intercropping strategy, strip intercropping and betweenrow intercropping. The sub-sub-plot factor was cover crop species, Dolichos lablab (Dolichos argenteus (Highworth), and Cowpea Vigna ungiculata (Agrinawa) plus control plots of sole maize. Results showed that same time planting of leguminous cover crops with maize using the in-between row intercropping patterns can derive appreciable system biomass (maize/cover crop) yields, utilize land efficiently whilst getting favourable maize grain yield. Based on the rainfall analysis, results showed that the probability of success when relay seeding cover crops after two weeks into standing maize is low (15% chances of success). This suggests that relay intercropping strategies would not work due to the unavailability of a good quality season.

Achieving high biomass yields of cover crops has been a challenge to the success of Conservation Agriculture (CA) practices in the Eastern Cape (EC). A study was conducted to evaluate strategies for optimizing cover crop biomass production. Trials were carried out to screen summer and winter cover crops, as well as evaluate intercropping patterns and planting dates for biomass, weed suppression and subsequent maize yield under irrigation. Four summer legume cover crop species were evaluated under a Randomised Complete Block Design (RCBD) design. The cover crops were fertilized with 13.34 kg ha-1 of N, 20 kg ha-1 P and 26.66 kg ha-1 K. In the 2008/09 summer season a maize crop was superimposed on the 2007/08 screening trial under no-till. The crop was fertilized with 60 kg ha-1 of N. An intercropping trial was conducted over two seasons as a way of investigating the best way of incorporating cover crops into farmers cropping systems. This was done bearing in mind the limitation of resources such as land. The trial evaluated 3 factors laid as a 2 x 2 x 3 factorial arranged in a split-plot design. The main factor was cover crop planting date (planting at maize planting or 2 weeks after maize planting). The sub plot factor was intercropping pattern (strip intercropping and between row intercropping). A trial was also conducted to evaluate the effect of planting date (End of April and mid May) and four winter legume cover crop species on cover crop biomass, weed suppression and maize grain yield. The experiment was laid out as a Randomised Complete Block Design (RCBD) replicated 3 times. In the subsequent summer season a maize crop was superimposed on the winter trial to test the residual effects of the cover crop species. Another study was conducted to evaluate winter cereal cover crop species for biomass accumulation, weed suppression and subsequent maize grain yield. The cover crops as well as a weedy fallow control plot treatments were laid out as a Randomised Complete Block Design replicated 3 times. In the subsequent summer season a maize crop was superimposed on the site under no-till to evaluate the residual effect of the cover crops on maize. The results showed sunhemp, cowpea and lablab as the best cover crops with high biomass and weed suppression whilst mucuna was the least. Sunhemp consistently yielded higher cover biomass averaging 11200 kg ha-1 over the two seasons whilst mucuna had a consistently lowest average biomass yield of 4050 kg ha-1. These cover crops were above the critical 6 t ha-1 for effective weed suppression. There was a significant (p<0.01) relationship of cover crop dry weight and weed dry weight in both seasons. Subsequent maize grain yield was significantly higher in the sunhemp plots (64.2 %) than the weedy fallow plot. Mucuna, lablab and cowpea had maize grain yield increases of 16.6%, 33% and 43.2% respectively. Intercropping cover crops at maize planting yielded higher cover crop dry weights than a delay in intercropping cover crops. A delay in intercropping resulted in significantly higher average maize grain yield of 4700 kg ha-1 compared to intercropping at maize planting (3800 kg ha-1) and sole maize (4300 kg ha-1) over the two seasons. Strip intercropping also yielded higher (5000 kg ha- 1) average maize grain yield compared to row intercropping (3600 kg ha-1) and sole maize (4300 kg ha-1). There was a significant (p<0.05) relationship between cover crop dry weight in the 2007/08 season and maize grain yield in the 2008/09 season. Early planting grazing vetch gave the highest biomass yield of 8100 kg ha-1 whilst early planted red clover had the lowest biomass of 635 kg ha-1. Low weed dry weights were also obtained from the early planted grazing vetch as opposed to the other treatments. There was a significant (p<0.001) relationship of cover crop dry weight and weed dry weight. In the subsequent 2008/09 summer season early planted grazing vetch had the highest maize yield of 7500 kg ha-1 which was 56.3 % more than the weedy fallow plot had 4800 kg ha-1. The weedy fallow plot also had high weed infestation than the cover crop plots. There were significant (p<0.01) relationships between cover crop dry weight and maize grain yield, winter weed dry weight and maize grain yield and summer weed dry weight and maize grain yield. The results also showed triticale (13900 kg ha-1) as the best winter cover crop for biomass production. Italian ryegrass (6500 kg ha-1) produced the least amount of biomass. In The subsequent maize crop white oats gave highest maize grain yield (6369 kg ha-1) which was 33 % more than the weedy fallow plot (4784 kg ha- 1). There were also significant (p< 0.01) relationships of maize grain yield and winter weed dry weight, maize grain yield and summer growing weeds. The various studies demonstrated that there is opportunity for high biomass production under small scale farmers irrigated conditions using cover crops both in winter and summer. Best bet cover crops were sunhemp, cowpea and lablab for summer and triticale, white oats, barley, Italian ryegrass and grazing vetch for winter. Cover crops can also be incorporated into farmers cropping systems as sole crops or intercrops within the maize based cropping systems. Strip intercropping can be used by farmers as a way of introducing cover crops. Critical to achievement of high biomass is the time of planting cover crops with high biomass when planting is done early. A 2 week delay in strip intercropping cover crop into maize can be used as a way of incorporating cover crops into farmers cropping systems with minimal maize yield reduction.

No description available.

MENVSC / Department of Geography and Geo-Information Science / See the atttached abstract below

<p>Cadmium (Cd) is a highly mobile and toxic heavy metal that negatively affects plants, soil biota, animals and humans, even in very low concentrations. Currently, Cd contamination of cocoa produced in Latin American countries is a significant problem, as concentrations can exceed acceptable levels set by the European Union (0.5 mg/kg), sometimes by more than 10 times allowable levels. In South America, <i>Theobroma cacao</i> is an essential component of the basic market basket. This crop contributes to the Latin-American trade balance, as these countries export cacao and chocolate-based products to major consumer countries such as the United States and Europe. Some soil amendments can alter the bioavailability and uptake of Cd into edible plant tissues, though cacao plants can accumulate Cd without displaying any visible symptoms of phytotoxicity, which makes it difficult to determine if potential remediation strategies are successful. Currently, the only effective way to quantify Cd accumulation in plant tissues is via destructive post-harvest practices that are time-consuming and expensive. New hyperspectral imaging (HSI) technologies developed for use in high-throughput plant phenotyping are powerful tools for monitoring environmental stress and predicting the nutritional status in plants. Consequently, the experiments described in this thesis were conducted to determine if HSI technologies could be adapted for monitoring plant stress caused by Cd, and estimating its concentration in vegetative plant tissues. Two leafy green crops were used in these experiments, basil (<i>Ocimum basilicum L.</i> var. Genovese) and kale (<i>Brassica oleracea L</i>. var. Lacinato), because they are fast growing, and therefore, could serve as indicator crops on cacao farms. In addition, we expected these two leafy green crops would differ in their morphological responses to Cd stress. Specifically, we predicted that stress responses would be visible in basil, but not kale, which is known to be a hyperaccumulator. The plants were subject to four levels of soil Cd (0, 5, 10 and 15 ppm), and half of the pots were amended with biochar at a rate of 3% (v/v), as this amendment has previously been demonstrated to improve plant health and reduce Cd uptake. The experiments were conducted at Purdue’s new Controlled Environment Phenotyping Center (CEPF). The plants were imaged weekly and manual measurements of plant growth and development were collected at the same times, and concentrations of Cd as well as many other elements were determined after harvest. Fourteen vegetation indices generated using HSI images collected from the side and top view of plants were evaluated for their potential to identify subtle signs of plant stress due soil Cd and the biochar amendment. In addition, three mathematical models were evaluated for their potential to estimate Cd concentrations in the plant biomass and determine if they exceed safe standards (0.28 mg/kg) set by the Food and Agriculture Organization (FAO) for leafy greens. Results of these studies confirm that like many plants, these leafy green crops can accumulate Cd levels that are well above safety thresholds for human health, but exhibit few visible symptoms of stress. The normalized difference vegetation index (NDVI) and the chlorophyll index at the red edge (CI_RE) were the best indices for detecting Cd stress in these crops, and the plant senescence and reflectance index (PSRI) and anthocyanin reflectance index (ARI) were the best at detecting subtle changes in plant physiology due to the biochar amendment. The heavy metal stress index (HMSSI), developed exclusively for detecting heavy metal stress, was only able to detect Cd stress in basil when images were taken from the top view. Results of the mathematical models indicated that principal components analysis (PCA) and partial least squares (PLS) models overfit despite efforts to transform the data, indicating that they are not capable of predicting Cd concentrations in these crops at these levels. However, the artificial neural networks (ANN) was able to predict whether leafy greens had levels of Cd that were above or below critical thresholds suggested by the FAO, indicating that HSI could be further developed to predict Cd concentrations in plant tissues. Further research conducted in the field and in the presence of other environmental stress factors are needed to confirm the utility of these tools, and determine whether they can be adapted to monitor Cd uptake in cacao plants.</p>

The increasing demand for food and fiber, the need for climate change mitigation and adaptation as well as for environmental protection impose severe challenges on land systems worldwide. Solutions to support the transformation towards a sustainable development of land systems are needed. One response to the multiple challenges is the introduction of policy options aimed at steering land use activities towards a bundle of societal goals. However, it is difficult to empirically foresee the effectiveness and unintended consequences of policy options prior to their deployment. A second response is environmental education because human consumption behavior, among other factors, strongly influences natural ecosystems. However, it is a non-trivial task to develop effective communication strategies for complex topics such as sustainable land management. In both cases, modeling can help to overcome the different obstacles along the way. In this thesis, dynamic process-based social-ecological models at the individual scale are developed and analyzed to study effectiveness and unintended side effects of policy options, which promote agricultural management strategies and were intentionally designed to cope with multiple societal challenges. Two case studies of political intervention are investigated: the promotion of perennial woody crops in European agricultural landscapes for a sustainable bioeconomy and governmental supplementary feeding programs to cope with climate risks in pastoral systems in drylands. These two case studies are complemented by the development of a serious online game on sustainable land management in general that bridges the gap between land use modeling and environmental education. Simulation results of this thesis provide insights into (i) the performance of the politically promoted agricultural management strategies in meeting various intended goals such as poverty alleviation or the maintenance of biodiversity and ecosystem services, (ii) the emergence of unintended (environmental and social) side effects such as land use conflicts, land degradation or cost explosion and (iii) the mitigation of such side effects by appropriately adjusting the design of the policy options. These insights are enabled by representing temporal as well as spatial variability in the developed models. Furthermore, different mechanistic approaches of transferability analyses based on stylized landscapes are developed and applied. They enable to check whether and in what respect policy impacts actually differ substantially between regional contexts, to identify what regional factors steer the impact and to derive indicators for grouping regions of similar policy impacts. Finally, based on a conducted survey-based evaluation and experiences from various applications, the value of the developed serious game for environmental education is revealed and discussed.Altogether, this thesis contributes to model-based decision support for steering transformation towards the sustainable development of land systems in an appropriate way. This is done by developing appropriate social-ecological modeling approaches, by performing specific policy impact analyses in two transformative agricultural systems using these models and by providing a model-based communication tool for environmental education.

<p> Soybean seed cost has dramatically increased in recent decades which has led to producer interest in lowering input cost through reductions in seeding rate. Fifty-eight seeding rate trials of soybean were conducted at field-scale in Indiana from 2010 to 2021 to update recommendations of seeding rates and plant population. The objectives were to determine the agronomic optimal seeding rate (AOSR) and plant population (AOPP) based on planting equipment, tillage practices, and planting date. Economic optimal seeding rate (EOSR) was also determined based on these field scenarios. Harvest AOPP was not influenced by planting equipment (~212,000 plants ha-1) or tillage (~239,000 plants ha-1), but AOSR varied. Soybean seeded with a row-crop planter optimized grain yield with 352,600 seeds ha-1; whereas, the grain drill required 75,200 more seeds ha-1. Soybean seeded into conventional tillage maximized grain yield at 380,400 seeds ha-1; whereas, under no-till conditions 41,400 more seeds ha-1 were required. Timely planting required 417,300 seeds ha-1 to optimize grain yield, which resulted in harvest AOPP of 216,700 plants ha-1. Conversely, late plantings required 102,800 fewer seeds ha-1 but 36,200 more plants ha-1 than timely planting. Depending on seed cost and soybean market price, seeding rates could be reduced 13,700 to 92,800 seeds ha-1 below AOSR to maximize profit.</p> <p>Secondly, digital imagery with high spatial resolution was collected with an unmanned aerial vehicle (UAV) to develop a simple and practical method to segment soybean from non-plant pixels. The best vegetation indices were selected to segment young soybean plants (VC to V6). Two field-scale trials of soybean were planted in 2020 with the agronomic trial design of two varieties x five seeding rates with three replications. The imagery was collected during this period as it coincides with the time for determining whether a soybean stand should be replanted. Five relative vegetative indices based on the red, green, and blue (RGB) imagery were evaluated: excess greenness index (ExG), excess redness index (ExR), green leaf index (GLI), normalized green-red difference index (NGRDI) and visible atmospheric resistance index (VARI). Both GLI and ExG were superior in overall accuracy compared to all other vegetative indices with very small soybean plants (VC to V1 growth stages). VARI and NGRDI had relatively poor overall accuracy at VC and V1, but had similar overall accuracy to GLI as soybean plants grew larger (V2 to V6 growth stages). Across all growth stages and locations, ExR performed the poorest. Moreover, GLI had consistent performance across the range of growth stages, suggesting its suitability for early soybean stand assessment methods.</p> <p>Six field-scale trials were established in 2020 and 2021 in Indiana with two varieties seeded from 123,000 to 618,000 seeds ha-1. Canopy cover was calculated using GLI to create binary segmentation of plant pixels and non-plant pixels. UAV-derived canopy cover measurements were correlated with plant population of soybean from VC to V4 and growing degree days (GDD) after planting. Yield potential (75, 80, 85, 90, 95, 100%) was correlated with canopy cover from VC to V4 and GDD after planting. Canopy cover of 2.1, 5.0, 8.9 and 13.8% by 150, 250, 350, and 450 GDD°C after planting, respectively, would maximize yield. Canopy cover for 75% yield potential was one-fourth as much as the 100% yield potential. Recommended threshold for replant decisions should be based on canopy cover to attain 95% yield potential. Field observations below a canopy cover of 1.8, 4.2, 7.4, and 11.5% canopy cover by 150, 250, 350, and 450 GDD°C after planting respectively, would consider replanting. </p>

<p>The agricultural data landscape is largely dysfunctional because of the industry’s highvariability  in  scale,  scope,  technological  adoption,  and  relationships.   Integrated  data  andmodels of agricultural sub-systems could be used to advance decision-making, but interoperability  challenges  prevent  successful  innovation.   In  this  work,  temporal  and  geospatial indexing  strategies  and  aggregation  were  explored  toward  the  development  of  functional data  structures  for  soils,  weather,  solar,  and  machinery-collected  yield  data  that  enhance data context, scalability, and sharability.</p> <p>The data structures were then employed in the creation of decision support tools including web-based  applications  and  visualizations.   One  such  tool  leveraged  a  geospatial  indexing technique called geohashing to visualize dense yield data and measure the outcomes of on-farm yield trials.  Additionally, the proposed scalable, open-standard data structures were used to drive a soil water balance model that can provide insights into soil moisture conditions critical to farm planning, logistics, and irrigation.  The model integrates SSURGO soil data,weather data from the Applied Climate Information System, and solar data from the National Solar Radiation Database in order to compute a soil water balance, returning values including runoff, evaporation, and soil moisture in an automated, continuous, and incremental manner.</p> <p>The approach leveraged the Open Ag Data Alliance framework to demonstrate how the data structures can be delivered through sharable Representational State Transfer Application Programming Interfaces and to run the model in a service-oriented manner such that it can be operated continuously and incrementally, which is essential for driving real-time decision support tools.  The implementations rely heavily on the Javascript Object Notation data schemas leveraged by Javascript/Typescript front-end web applications and back-end services delivered through Docker containers.  The approach embraces modular coding concepts and several levels of open source utility packages were published for interacting with data sources and supporting the service-based operations.</p> <p>By making use of the strategies laid out by this framework, industry and research canenhance data-based decision making through models and tools.  Developers and researchers will  be  better  equipped  to  take  on  the  data  wrangling  tasks  involved  in  retrieving  and parsing unfamiliar datasets, moving them throughout information technology systems, and understanding those datasets down to a semantic level.</p>