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Enhancing Farm-Level Decision Making through Innovation

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A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Agricultural Systems and Management".

Deadline for manuscript submissions: closed (25 November 2021) | Viewed by 18276

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Special Issue Editor

Prof. Dr. Matt J. Bell

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Guest Editor

Agriculture Department, Hartpury University, Gloucester GL19 3BE, UK
Interests: farming systems; innovation; environmental impact; animal-plant interactions; sustainable production
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

New information and knowledge are important aspects of innovation in modern farming systems. There is currently an abundance of digital and data-driven solutions that can potentially transform our food systems. At a time when the general public has concerns about how food is produced and the impact of farm production systems on the environment, strategies to increase public acceptance and the sustainability of food production are required more than ever. New tools and technology can provide timely insights into aspects such as nutrient profiles, tracking of animal or plant wellbeing, and land use options to enhance inputs and outputs associated with the farm business. Such solutions have the ultimate aim to enhance efficiencies of production and contribute to the process of learning about the advantages of the innovation, while ensuring more sustainable food supplies. At the farm level, any new information needs to be in a useful format and beneficial for management and farm decision making. We welcome papers to this Special Issue on studies investigating agri-business innovation that can enhance farm-level decision making.

Prof. Dr. Matt J. Bell
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agriculture is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

smart systems
precision farming
digital solutions
Big Data
agri-business
innovation
decision making
knowledge and information sources
sustainability
management

Published Papers (6 papers)

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Research

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13 pages, 3606 KiB

Open AccessArticle

Optimizing the Optimal Planting Period for Potato Based on Different Water-Temperature Year Types in the Agro-Pastoral Ecotone of North China

by Jinpeng Yang, Yingbin He, Shanjun Luo, Xintian Ma, Zhiqiang Li, Zeru Lin and Zhiliang Zhang

Agriculture 2021, 11(11), 1061; https://doi.org/10.3390/agriculture11111061 - 28 Oct 2021

Cited by 3 | Viewed by 1864

Abstract

Potato is the fourth staple crop in China after wheat, maize and rice. The agro-pastoral ecotone (APE) in North China is a main region for potato production. However, potato yield has been seriously constrained by water shortages because of low precipitation and highly variable precipitation patterns during the growing season in this area. In this study, the Agricultural Production Systems Simulator (APSIM) model was used to simulate potato water-limited yield and historical years were divided into different water-temperature year types to optimize the optimal planting period (OPP) and cultivar of potato. The results showed that the potato yield varied in different water-temperature year types. Fast-developing cultivar Favorita could obtain the highest yield in most places and water-temperature year types due to its relatively short length of tuber formation stage. In this study, we suggest changing the planting date according to the water-temperature year type, which offers a new way to adapt to a highly variable climate. However, our method should be adopted carefully because we only considered climate factors; other agronomic management practices (adjusting planting density, plastic film mulch, conservation tillage etc.) also have a great effect on planting date and cultivar selection, which should be further investigated in the future. Full article

(This article belongs to the Special Issue Enhancing Farm-Level Decision Making through Innovation)

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18 pages, 4017 KiB

Open AccessArticle

Image Generation of Tomato Leaf Disease Identification Based on Adversarial-VAE

by Yang Wu and Lihong Xu

Agriculture 2021, 11(10), 981; https://doi.org/10.3390/agriculture11100981 - 09 Oct 2021

Cited by 13 | Viewed by 2161

Abstract

The deep neural network-based method requires a lot of data for training. Aiming at the problem of a lack of training images in tomato leaf disease identification, an Adversarial-VAE network model for generating images of 10 tomato leaf diseases is proposed, which is used to expand the training set for training an identification model. First, an Adversarial-VAE model is designed to generate tomato leaf disease images. Then, a multi-scale residual learning module is used to replace single-size convolution kernels to enrich extracted features, and a dense connection strategy is integrated into the Adversarial-VAE networks to further enhance the image generation ability. The training set is expanded by the proposed model, which generates the same number of images by training 10,892 images of 10 leaves. The generated images are superior to those of InfoGAN, WAE, VAE, and VAE-GAN measured by the Frechet Inception Distance (FID). The experimental results show that using the extension dataset that is generated by the Adversarial-VAE model to train the Resnet identification model could improve the accuracy of identification effectively. The model proposed in this paper could generate enough images of tomato leaf diseases and provide a feasible solution for data expansion of tomato leaf disease images. Full article

(This article belongs to the Special Issue Enhancing Farm-Level Decision Making through Innovation)

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9 pages, 751 KiB

Open AccessArticle

Changes in Dairy Cow Behavior with and without Assistance at Calving

by Bethan Cavendish, John McDonagh, Georgios Tzimiropoulos, Kimberley R. Slinger, Zoë J. Huggett and Matt J. Bell

Agriculture 2021, 11(8), 722; https://doi.org/10.3390/agriculture11080722 - 29 Jul 2021

Cited by 2 | Viewed by 2091

Abstract

The aim of this study was to characterize calving behavior of dairy cows and to compare the duration and frequency of behaviors for assisted and unassisted dairy cows at calving. Behavioral data from nine hours prior to calving were collected for 35 Holstein-Friesian dairy cows. Cows were continuously monitored under 24 h video surveillance. The behaviors of standing, lying, walking, shuffle, eating, drinking and contractions were recorded for each cow until birth. A generalized linear mixed model was used to assess differences in the duration and frequency of behaviors prior to calving for assisted and unassisted cows. The nine hours prior to calving was assessed in three-hour time periods. The study found that the cows spent a large proportion of their time either lying (0.49) or standing (0.35), with a higher frequency of standing (0.36) and shuffle (0.26) bouts than other behaviors during the study. There were no differences in behavior between assisted and unassisted cows. During the three-hours prior to calving, the duration and bouts of lying, including contractions, were higher than during other time periods. While changes in behavior failed to identify an association with calving assistance, the monitoring of behavioral patterns could be used as an alert to the progress of parturition. Full article

(This article belongs to the Special Issue Enhancing Farm-Level Decision Making through Innovation)

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10 pages, 1199 KiB

Open AccessArticle

Changes in Sheep Behavior before Lambing

by Beatrice E. Waters, John McDonagh, Georgios Tzimiropoulos, Kimberley R. Slinger, Zoë J. Huggett and Matt J. Bell

Agriculture 2021, 11(8), 715; https://doi.org/10.3390/agriculture11080715 - 29 Jul 2021

Cited by 1 | Viewed by 3942

Abstract

The aim of this study was to assess the duration and frequency of behavioral observations of pregnant ewes as they approached lambing. An understanding of behavioral changes before birth may provide opportunities for enhanced visual monitoring at this critical stage in the animal’s life. Behavioral observations for 17 ewes in late pregnancy were recorded during two separate time periods, which were 4 to 6 weeks before lambing and before giving birth. It was normal farm procedure for the sheep to come indoors for 6 weeks of close monitoring before lambing. The behaviors of standing, lying, walking, shuffling and contraction behaviors were recorded for each animal during both time periods. Over both time periods, the ewes spent a large proportion of their time either lying (0.40) or standing (0.42), with a higher frequency of standing (0.40) and shuffling (0.28) bouts than other behaviors. In the time period before giving birth, the frequency of lying and contraction bouts increased and the standing and walking bouts decreased, with a higher frequency of walking bouts in ewes that had an assisted lambing. The monitoring of behavioral patterns, such as lying and contractions, could be used as an alert to the progress of parturition. Full article

(This article belongs to the Special Issue Enhancing Farm-Level Decision Making through Innovation)

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8 pages, 1477 KiB

Open AccessCommunication

Detecting Dairy Cow Behavior Using Vision Technology

by John McDonagh, Georgios Tzimiropoulos, Kimberley R. Slinger, Zoë J. Huggett, Peter M. Down and Matt J. Bell

Agriculture 2021, 11(7), 675; https://doi.org/10.3390/agriculture11070675 - 17 Jul 2021

Cited by 16 | Viewed by 4027

Abstract

The aim of this study was to investigate using existing image recognition techniques to predict the behavior of dairy cows. A total of 46 individual dairy cows were monitored continuously under 24 h video surveillance prior to calving. The video was annotated for the behaviors of standing, lying, walking, shuffling, eating, drinking and contractions for each cow from 10 h prior to calving. A total of 19,191 behavior records were obtained and a non-local neural network was trained and validated on video clips of each behavior. This study showed that the non-local network used correctly classified the seven behaviors 80% or more of the time in the validated dataset. In particular, the detection of birth contractions was correctly predicted 83% of the time, which in itself can be an early warning calving alert, as all cows start contractions several hours prior to giving birth. This approach to behavior recognition using video cameras can assist livestock management. Full article

(This article belongs to the Special Issue Enhancing Farm-Level Decision Making through Innovation)

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Review

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12 pages, 611 KiB

Open AccessReview

Proximal Sensing in Grasslands and Pastures

by Shayan Ghajar and Benjamin Tracy

Agriculture 2021, 11(8), 740; https://doi.org/10.3390/agriculture11080740 - 04 Aug 2021

Cited by 6 | Viewed by 2786

Abstract

Reliable measures of biomass, species composition, nitrogen status, and nutritive value provide important indicators of the status of pastures and rangelands, allowing managers to make informed decisions. Traditional methods of sample collection necessitate significant investments in time and labor. Proximal sensing technologies have the potential to collect more data with a smaller investment in time and labor. However, methods and protocols for conducting pasture assessments with proximal sensors are still in development, equipment and software vary considerably, and the accuracy and utility of these assessments differ between methods and sites. This review summarizes the methods currently being developed to assess pastures and rangelands worldwide and discusses these emerging technologies in the context of diffusion of innovation theory. Full article

(This article belongs to the Special Issue Enhancing Farm-Level Decision Making through Innovation)

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