Self-Organising and Self-Learning Model for Soybean Yield Prediction

Machine learning has arisen with advanced data analytics. Many factors influence crop yield, such as soil, amount of water, climate, and genotype. Determining factors that significantly influence yield prediction and identify the most appropriate predictive methods are important in yield management. It is critical to consider and study the combination of different crop factors and their impact on the yield. The objectives of this paper are: (1) to use advanced data analytic techniques to precisely predict the soybean crop yields, (2) to identify the most influential features that impact soybean predictions, (3) to illustrate the ability of Fuzzy Rule-Based (FRB) sub-systems, which are self-organizing, self-learning, and data-driven, by using the recently developed Autonomous Learning Multiple-Model First-order (ALMMo-1) system, and (4) to compare the performance with other well-known methods. The ALMMo-1 system is a transparent model, which stakeholders can easily read and interpret. The model is a datadriven and composed of prototypes selected from the actual data. Many factors affect the yield, and data clouds can be formed in the feature/data space based on the data density. The data cloud is the key to the IF part of FRB sub-systems, while the THEN part (the consequences of the IF condition) illustrates the yield prediction in the form of a linear regression model, which consists of the yield features or factors. In addition, the model can determine the most influential features of the yield prediction online. The model shows an excellent prediction accuracy with a Root Mean Square Error (RMSE) of 0.0883, and Non-Dimensional Error Index (NDEI) of 0.0611, which is competitive with state-of-the-art methods

Towards resilience through systems-based plant breeding. A review

How the growing world population can feed itself is a crucial, multi-dimensional problem that goes beyond sustainable development. Crop production will be affected by many changes in its climatic, agronomic, economic, and societal contexts. Therefore, breeders are challenged to produce cultivars that strengthen both ecological and societal resilience by striving for six international sustainability targets: food security, safety and quality; food and seed sovereignty; social justice; agrobiodiversity; ecosystem services; and climate robustness. Against this background, we review the state of the art in plant breeding by distinguishing four paradigmatic orientations that currently co-exist: community-based breeding, ecosystem-based breeding, trait-based breeding, and corporate-based breeding, analyzing differences among these orientations. Our main findings are: (1) all four orientations have significant value but none alone will achieve all six sustainability targets; (2) therefore, an overarching approach is needed: “systems-based breeding,” an orientation with the potential to synergize the strengths of the ways of thinking in the current paradigmatic orientations; (3) achieving that requires specific knowledge development and integration, a multitude of suitable breeding strategies and tools, and entrepreneurship, but also a change in attitude based on corporate responsibility, circular economy and true-cost accounting, and fair and green policies. We conclude that systems-based breeding can create strong interactions between all system components. While seeds are part of the common good and the basis of agrobiodiversity, a diversity in breeding approaches, based on different entrepreneurial approaches, can also be considered part of the required agrobiodiversity. To enable systems-based breeding to play a major role in creating sustainable agriculture, a shared sense of urgency is needed to realize the required changes in breeding approaches, institutions, regulations and protocols. Based on this concept of systems-based breeding, there are opportunities for breeders to play an active role in the development of an ecologically and societally resilient, sustainable agriculture

Analysis of facilities in OFF research in participating countries of CORE Organic

Report lists the following research facilities: research farms, experimental fields, on-farm studies, networks, animal research facilities, leaching fields and long-term experiments. Other facilities like facilities for laboratory analyses, food processing, greenhouses, climate chambers and growth cabinets are left out from this analysis, because they are seldom exclusively used for OFF research and because their use for OFF research does not require particular characteristics. On the other hand, when required, these facilities can easily be converted to OFF research

State of the art of existing breeding initiatives & actions planned to strengthen collaborations

State of the art of existing breeding initiatives3Introduction In order to strengthen organic breeding, it is important to know the state of the art of existing initiatives, programs and networks of organic breeding and breeding for organic, and in what crops most organic breeding is currently conducted. Although the number of organic breeding initiatives are growing, as a whole, organic breeding is still relatively marginal compared to conventional breeding. Next to more financial support, another solution to make organic breeding more effective is by improving collaborations. Collaboration can entail, among others, improved exchange of knowledge (breeding tools and approaches) or the exchange of material. In LIVESEED, several activities have been set up to improve collaboration, such as crop‐specific breeding activities, crop‐group activities and systems‐based breeding approaches. For each of these activities, timelines have been developed to strengthen collaborations. This shall improve on one side the capacity building of existing organic plant breeding programs for respective crops and help to identify breeding gaps for those crops, where no activity could be mapped so far

Sustainability aspects of biobased applications : comparison of different crops and products from the sugar platform BO-12.05-002-008

In this study different uses of biomass are compared. In order to allow for a systematic comparison the study focuses on three different chemicals that can be produced from sugar. In this way it is also, in principle, possible to compare different crops for the production of the same product. The study focuses on the production of PLA (polylactic acid, a bioplastic), ethanol, and biopolyethylene (bio-PE, which is produced via ethanol). These three products can presently be produced from biomass and therefore form realistic cases. All three products are produced from sugars, and thus the systems can be decoupled at the sugar step. The sugar can be produced from different crops. In this study five different crops are compared: wheat, maize, sugar beet, sugar cane and Miscanthus. The sustainability aspects that we studied are non-renewable energy use (NREU), greenhouse gas (GHG) emission in the crop-product chain and direct land use for producing the bio-materials

CropM - progress overview

Activities in the first 1 ½ years of CropM were related to key issues identified as critical at the beginning of the FACCE MACSUR the knowledge Hub. These include: Model intercomparison,Generation of new data for model improvement, Methods for scaling and model linking, Uncertainty analysis, Building research capacity, Climate scenario data for crop models. The key ambition of CropM has been to develop scientific excellence on methods for a comprehensive assessment of climate change impact, adaptation and policy on European crop production, agriculture and food security. Much progress has been made in developing a first shared continental assessment and tool for: A range of important crops, Important crop rotations, Advanced scaling methods, Advanced link to farm and sector models, Novel impact uncertainty assessment and reporting, State-of-the-art scenario construction. A number of concrete studies towards this aim have been launched in CropM workpackages (WPs): WP1-2: Two multi-facetted studies on crop rotation, launched in summer 2013, WP3: comprehensive scaling exercises, launched in March 2013, WP4: Studies on (a) Climate scenario development, (b) impact response surface method and (c) Extremes, launched in summer 2013, WP5: Analysis of transect across Europe with temperature effect (Space for Time). In addition, extended activities related to capacity building including several PhD courses (WP5) workshops (in WPs1-4) and an International Symposium (10-12 Feb, Oslo, Norway) have been organized. Present and future work is and will be focused on framing and advancing crop modelling as integrated part of comprehensive climate risk assessment and modelling of agricultural systems for food security from farm to supra-national level

Some Physiochemical Parameters and Phytoplankton Standing Crop in Four Northeast Arkansas Commercial Fish Ponds

Physicochemical conditions and chlorophyll a standing crop were studied from July 1970 through June 1971 in four commercial catfish ponds at the Arkansas State University Experiment Farm near Walcott, Greene County, Arkansas. Determinations of dissolved oxygen, free carbon dioxide, total alkalinity, temperature, pH, transparency, and chlorophyll a standing crop were made at two-week intervals except during fish harvesting operations. One diurnal measurement of dissolved oxygen, free carbon dioxide, and temperature was conducted 25-26 June 1971. Increased oxygen concentrations coincided with increased chlorophyll α concentrations. Free carbon dioxide and chlorophyll α values varied inversely throughout the study. Diurnal concentrations of free carbon dioxide were greatest between 0300 and 0700 hours. Phenolphthalein and total alkalinity values fluctuated throughout the study period, and could not be correlated with other parameters measured. Thermal stratification occurred during the summer and was more pronounced in the more turbidponds. Diurnal temperature measurements indicated that stratification was diurnal. An inverse relationship was found between carbon dioxide and hydrogen-ion concentrations, and all ponds were essentially alkaline. Transparency was relatively constant before the ponds were drained but increased when the ponds were refilled. Suspended particulate matter contributed significantly to turbidity. Peaks of chlorophyll α concentration were found in summer, early autumn, and late winter