In search of pragmatic soil moisture mapping at the field scale : a review
- Weir, Peter, Dahlhaus, Peter
- Authors: Weir, Peter , Dahlhaus, Peter
- Date: 2023
- Type: Text , Journal article , Review
- Relation: Smart Agricultural Technology Vol. 6, no. (2023), p.
- Full Text:
- Reviewed:
- Description: Soil moisture is a major limiting factor in most dryland agricultural production systems around the globe. In dryland agriculture the amount of water available to grow a crop is determined primarily by the in-season rainfall and the amount of water stored in the soil profile prior to seeding of the crop. Soil water content and water storage capacity are key parameters. Soil moisture data measurements are a compromise between the spatial scale of the investigated site, the required spatial resolution, and the depth of investigation of the applied method. A bibliographic search of the measurement of soil moisture content at field-scale was done, giving an overview of current practices available to determine the spatial variability within a field, and its applicability to farm management practices. Articles published between April 2013 and March 2023 were searched, retaining only the articles with horizontal resolution less than or equal to 100 m, minimum vertical support at a depth greater than or equal to 30 cm from the soil surface, a minimum of two vertical layer depths, and the topic of the document was associated with the measurement of soil moisture at field-scale. The results of this review highlight progress in the past decade but currently there is no one method that can achieve absolute continuous spatial soil moisture in 3D at the field level. Some areas of research show promise but is still some distance away from a reliable, timely, and accurate soil moisture mapping required for many extensive dryland farming systems. © 2023
- Authors: Weir, Peter , Dahlhaus, Peter
- Date: 2023
- Type: Text , Journal article , Review
- Relation: Smart Agricultural Technology Vol. 6, no. (2023), p.
- Full Text:
- Reviewed:
- Description: Soil moisture is a major limiting factor in most dryland agricultural production systems around the globe. In dryland agriculture the amount of water available to grow a crop is determined primarily by the in-season rainfall and the amount of water stored in the soil profile prior to seeding of the crop. Soil water content and water storage capacity are key parameters. Soil moisture data measurements are a compromise between the spatial scale of the investigated site, the required spatial resolution, and the depth of investigation of the applied method. A bibliographic search of the measurement of soil moisture content at field-scale was done, giving an overview of current practices available to determine the spatial variability within a field, and its applicability to farm management practices. Articles published between April 2013 and March 2023 were searched, retaining only the articles with horizontal resolution less than or equal to 100 m, minimum vertical support at a depth greater than or equal to 30 cm from the soil surface, a minimum of two vertical layer depths, and the topic of the document was associated with the measurement of soil moisture at field-scale. The results of this review highlight progress in the past decade but currently there is no one method that can achieve absolute continuous spatial soil moisture in 3D at the field level. Some areas of research show promise but is still some distance away from a reliable, timely, and accurate soil moisture mapping required for many extensive dryland farming systems. © 2023
Matching the model to the available data to predict wheat, barley, or canola yield : a review of recently published models and data
- Clark, Robert, Dahlhaus, Peter, Robinson, Nathan, Larkins, Jo-ann, Morse-McNabb, Elizabeth
- Authors: Clark, Robert , Dahlhaus, Peter , Robinson, Nathan , Larkins, Jo-ann , Morse-McNabb, Elizabeth
- Date: 2023
- Type: Text , Journal article , Review
- Relation: Agricultural Systems Vol. 211, no. (2023), p.
- Full Text:
- Reviewed:
- Description: CONTEXT: Continued increases in global population and rising living standards in many countries are driving a surge in demand for energy and protein-rich foods. Wheat, barley, and canola are important crops that are grown and traded globally. However, climate change, geopolitical tensions and competition from other crops threaten the ability to satisfy global demand. Accurate predictions of crop production and its spatial variation can play a significant role in their reliable and efficient production, marketing, and distribution. OBJECTIVE: This review examined recently published models and data used to predict wheat, barley, and canola yield to identify which factors produced the best yield predictions. METHODS: A literature search was conducted across the Scopus, EBSCOhost and Web of Science databases over seven years between 2015 and 2021. Data extracted from the papers identified by the literature search were investigated using graphical and quantitative analytical techniques to determine if the type of algorithm, input data, prediction timing, output scale or extent and climate variability both in isolation and in combination affected the model's predictive ability. RESULTS AND CONCLUSIONS: The literature search produced 11, 908 results which was reduced to 118 papers after applying the review criteria (peer reviewed papers focussed on models predicting yield at greater than plot scale across extensive areas using accessible data). China produced almost one third of all yield prediction models over the study period and 87% of models were used to predict wheat yield. Statistical models were the most common algorithm in most regions and in total. However, there was a surge in machine learning models after 2018. They were the most common model from 2019 to 2021, with one third developed in China. The review concluded that only the choice of modelling technique and the input data had a significant effect on model performance with the machine learning techniques Random Forest, Boosting algorithms and Deep Learning models as well as process-based Light Use Efficiency models that used a combination of remotely sensed and agrometeorological data performing best. SIGNIFICANCE: The review showed that matching the model to the available data could improve the ability to predict wheat, barley or canola yield. The use of quantitative statistical techniques in this review, should give modellers trying to predict wheat, barley or canola yield more confidence in matching their approach to the available data than previous reviews that relied on visual interpretation of data. © 2023 The Authors
- Authors: Clark, Robert , Dahlhaus, Peter , Robinson, Nathan , Larkins, Jo-ann , Morse-McNabb, Elizabeth
- Date: 2023
- Type: Text , Journal article , Review
- Relation: Agricultural Systems Vol. 211, no. (2023), p.
- Full Text:
- Reviewed:
- Description: CONTEXT: Continued increases in global population and rising living standards in many countries are driving a surge in demand for energy and protein-rich foods. Wheat, barley, and canola are important crops that are grown and traded globally. However, climate change, geopolitical tensions and competition from other crops threaten the ability to satisfy global demand. Accurate predictions of crop production and its spatial variation can play a significant role in their reliable and efficient production, marketing, and distribution. OBJECTIVE: This review examined recently published models and data used to predict wheat, barley, and canola yield to identify which factors produced the best yield predictions. METHODS: A literature search was conducted across the Scopus, EBSCOhost and Web of Science databases over seven years between 2015 and 2021. Data extracted from the papers identified by the literature search were investigated using graphical and quantitative analytical techniques to determine if the type of algorithm, input data, prediction timing, output scale or extent and climate variability both in isolation and in combination affected the model's predictive ability. RESULTS AND CONCLUSIONS: The literature search produced 11, 908 results which was reduced to 118 papers after applying the review criteria (peer reviewed papers focussed on models predicting yield at greater than plot scale across extensive areas using accessible data). China produced almost one third of all yield prediction models over the study period and 87% of models were used to predict wheat yield. Statistical models were the most common algorithm in most regions and in total. However, there was a surge in machine learning models after 2018. They were the most common model from 2019 to 2021, with one third developed in China. The review concluded that only the choice of modelling technique and the input data had a significant effect on model performance with the machine learning techniques Random Forest, Boosting algorithms and Deep Learning models as well as process-based Light Use Efficiency models that used a combination of remotely sensed and agrometeorological data performing best. SIGNIFICANCE: The review showed that matching the model to the available data could improve the ability to predict wheat, barley or canola yield. The use of quantitative statistical techniques in this review, should give modellers trying to predict wheat, barley or canola yield more confidence in matching their approach to the available data than previous reviews that relied on visual interpretation of data. © 2023 The Authors
Roles of selective agriculture practices in sustainable agricultural performance : a systematic review
- Ali, Basharat, Dahlhaus, Peter
- Authors: Ali, Basharat , Dahlhaus, Peter
- Date: 2022
- Type: Text , Journal article , Review
- Relation: Sustainability (Switzerland) Vol. 14, no. 6 (2022), p.
- Full Text:
- Reviewed:
- Description: Feeding the growing global population while improving the Earth’s economic, environmental, and social values is a challenge recognised in both the United Nations Sustainable Development Goals and the United Nations Framework Convention on Climate Change. Sustaining global agricultural performance requires regular revision of current farming models, attitudes, and practices. In systematically reviewing the international literature through the lens of the sustainability framework, this paper specifically identifies precision conservation agriculture (PCA), digital agriculture (DA), and resilient agriculture (RA) practices as being of value in meeting future challenges. Each of these adaptations carries significantly positive relationships with sustaining agricultural performance, as well as positively mediating and/or moderating each other. While it is clear from the literature that adopting PCA, DA, and RA would substantially improve the sustainability of agricultural performance, the uptake of these adaptations generally lags. More in-depth social science research is required to understand the value propositions that would encourage uptake of these adaptations and the barriers that prevent them. Recommendations are made to explore the specific knowledge gap that needs to be understood to motivate agriculture practitioners to adopt these changes in practice. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Ali, Basharat , Dahlhaus, Peter
- Date: 2022
- Type: Text , Journal article , Review
- Relation: Sustainability (Switzerland) Vol. 14, no. 6 (2022), p.
- Full Text:
- Reviewed:
- Description: Feeding the growing global population while improving the Earth’s economic, environmental, and social values is a challenge recognised in both the United Nations Sustainable Development Goals and the United Nations Framework Convention on Climate Change. Sustaining global agricultural performance requires regular revision of current farming models, attitudes, and practices. In systematically reviewing the international literature through the lens of the sustainability framework, this paper specifically identifies precision conservation agriculture (PCA), digital agriculture (DA), and resilient agriculture (RA) practices as being of value in meeting future challenges. Each of these adaptations carries significantly positive relationships with sustaining agricultural performance, as well as positively mediating and/or moderating each other. While it is clear from the literature that adopting PCA, DA, and RA would substantially improve the sustainability of agricultural performance, the uptake of these adaptations generally lags. More in-depth social science research is required to understand the value propositions that would encourage uptake of these adaptations and the barriers that prevent them. Recommendations are made to explore the specific knowledge gap that needs to be understood to motivate agriculture practitioners to adopt these changes in practice. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
- Bahlo, Christiane, Dahlhaus, Peter, Thompson, Helen, Trotter, Mark
- Authors: Bahlo, Christiane , Dahlhaus, Peter , Thompson, Helen , Trotter, Mark
- Date: 2019
- Type: Text , Journal article , Review
- Relation: Computers and Electronics in Agriculture Vol. 156, no. (2019), p. 459-466
- Full Text: false
- Reviewed:
- Description: Livestock industries are increasingly embracing precision farming and decision support tools. As a result, sensors, weather stations, individual animal tracking, feed monitoring and other sources create large data volumes, much of which is used only for a single purpose. There are unrealised potential benefits of making on farm data interoperable and accessible and federating it with public data sources. We reviewed recent literature on precision livestock farming (PLF) technologies in relation to the use of public data, open standards and interoperability. Livestock farms produce rising volumes of disparate private datasets, reflecting a variety of information needs and technological opportunities, but typically lacking interoperable formats and metadata. These as well as large amounts of accessible public datasets are currently underutilised in decision support tools. Tools that demonstrate the use of interoperable standards and bring together public and private data for decision support can enhance the value proposition and help lower barriers to the sharing and re-use of data. This review of interoperable standards in extensive livestock farming systems concludes that there is a need for not only a new type of decision support tool, but also a consensus on data exchange standards to prove the value of shared data at farm scale (commercial benefit) and a regional scale (public good). © 2018
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