Search

Based on a panel data from National Rural Fixed Point Survey (NRFP), this study explored the effects of information and communication technologies (ICTs) on the fertilizer and pesticide use efficiency of China's grain production and the mechanisms using stochastic frontier analysis (SFA) and two‐way fixed‐effects model with two‐stage least square (2SLS) estimation. The results show that the average fertilizer use efficiency (FE), pesticide use efficiency (PE) and comprehensive fertilizer and pesticide use efficiency (CFPE) of grain production were 0.286, 0.404, and 0.364 respectively during 2003–2011. It should be noted that the CFPE decreased by 48.39% during 2003–2011 and showed a descending trend in all regions. ICTs had significant positive effects on FE, PE, and CPFE which could be explained by providing farmers more sustainable knowledge and hence shifting farming practices from overusing fertilizer towards using farmyard manure as a substitute. ICTs' positive effects were more pronounced for farmers with higher‐level education and in central region. Additionally, ICTs had significant spillover effects, extending from users to nonusers within the villages. These results suggest that ICTs could be considered as an effective way to increase the fertilizer and pesticide use efficiency and promote the sustainable development of agriculture in China. [ABSTRACT FROM AUTHOR]

Reduction of milk yield is one of the principal components in the cost of mastitis. However, past research into the association between milk yield and mastitis indicators is limited. Past research has not been based on online or in-line daily measurements and has not fully explored nonlinearity and the thresholds at which milk yield starts to decrease. In dairy herds with automated milking systems equipped with sensors, mastitis indicators of individual cows are measured on an intraday frequency, which provides unprecedented avenues to explore such effects in detail. The aim of this observational study was primarily to investigate the nonlinear associations of lactate dehydrogenase (LDH), electrical conductivity (EC), and somatic cell count (SCC) with milk yield at various stages of lactation, parity, and mastitis chronicity status (i.e., whether the cow had SCC ≥200,000 SCC/mL for the last 28 d). We also investigated thresholds at which mastitis indicators (LDH, EC, and SCC) started to be negatively associated with milk yield. We used data from 21 automated milking system herds measuring EC and online SCC. Of these herds, 7 of the 21 additionally measured online LDH. We operationalized milk yield as milk synthesis rate in kilograms per hour. Applying a generalized additive model, we estimated the milk synthesis rate as a function of the 3 mastitis indicators for 3 different subgroups based on parity, stage of lactation, and mastitis chronicity. Partial dependence plots of the mastitis indicators were used to evaluate the milk synthesis rate to study if the milk synthesis rate was associated with mastitis indicators at a specific level. Results showed that milk synthesis rate decreased with increasing SCC, LDH, and EC, but in a nonlinear fashion. The thresholds at which milk synthesis rate started to decrease were 2.5 LnSCC (12,000 SCC/mL) to 3.75 LnSCC (43,000 SCC/mL), 0 to 1 LnLDH (1−2.7 U/L), and 5.0 to 6.0 mS/cm for EC. Additionally, another substantial decrease of milk synthesis rate was observed at thresholds of 5.625 LnSCC (277,000 SCC/mL) and 3 LnLDH (20 LDH U/L) but not for EC. Having chronic mastitis decreased milk synthesis rate in all models. The identified nonlinearities between mastitis indicators and milk synthesis rate should be incorporated in statistical models for more accurate estimations of milk loss due to mastitis. [ABSTRACT FROM AUTHOR]

Bovine viral diarrhea virus (BVDV) infection has a major effect on the health of cows and consequently on herd performance. Many countries have implemented control or eradication programs to mitigate BVDV infection and its negative effects. These negative effects of BVDV infection on dairy herds are well documented, but there is much less information about the effects of new introduction of BVDV on dairy herds already participating in a BVDV control program. The objective of our study was to investigate the effect of a new BVDV introduction in BVDV-free herds participating in the Dutch BVDV-free program on herd performance. Longitudinal herd-level surveillance data were combined with herd information data to create 4 unique data sets, including a monthly test-day somatic cell count (SCC) data set, annual calving interval (CIV) and culling risk (CR) data sets, and a quarterly calf mortality rate (CMR) data set. Each database contained 2 types of herds: herds that remained BVDV free during the whole study period (defined as free herds), and herds that lost their BVDV-free status during the study period (defined as breakdown herds). The date of losing the BVDV-free status was defined as breakdown date. To compare breakdown herds with free herds, a random breakdown date was artificially generated for free herds by simple random sampling from the distribution of the breakdown month of the breakdown herds. The SCC and CIV before and after a new introduction of BVDV were compared through linear mixed-effects models with a Gaussian distribution, and the CR and CMR were modeled using a negative binomial distribution in generalized linear mixed-effects models. The explanatory variables for all models included herd type, BVDV status, year, and a random herd effect. Herd size was included as an explanatory variable in the SCC, CIV, and CMR model. Season was included as an explanatory variable in the SCC and CMR model. Results showed that free herds have lower SCC, CR, CMR, and shorter CIV than the breakdown herds. Within the breakdown herds, the new BVDV introduction affected the SCC and CMR. In the year after BVDV introduction, the SCC was higher than that in the year before BVDV introduction, with a factor of 1.011 [2.5th to 97.5th percentile (95% PCTL): 1.002, 1.020]. Compared with the year before BVDV breakdown, the CMR in the year of breakdown and the year after breakdown was higher, with factors of 1.170 (95% PCTL: 1.120; 1.218) and 1.096 (95% PCTL: 1.048; 1.153), respectively. This study reveals that a new introduction of BVDV had a negative but on average relatively small effect on herd performance in herds participating in a BVDV control program. [ABSTRACT FROM AUTHOR]

In automatic milking systems (AMS), sensors can measure cow behavior and milk composition at every milking. The aim of this observational study of previously collected data was to gain insight into the differences in dynamics of udder inflammation indicators between cows that recover and those that do not recover after detection of an initial inflammation. Milk diversion (milk separated from the bulk tank and thus indicating farmer intervention), conductivity, and somatic cell count (SCC) data from 4 wk before the initial inflammation to 12 wk after the initial inflammation were used to analyze 2,584 cases of udder inflammation. An udder inflammation case was defined as an initial observation of SCC ≥200,000 cells/mL as well as 1 additional SCC measurement >200,000 cells/mL within 10 d after the initial case, among other requirements. The data originated from 15 AMS herds in 6 countries. Four subsets of cows were created based on whether milk was diverted after the initial inflammation and whether the udder inflammation cases recovered, using a 10-d rolling average SCC threshold of 200,000 cells/mL and checking whether this rolling mean was below the threshold within 90 d after the initial inflammation as the indication of recovery. This formed the following subsets of cow lactations: milk diverted–recovered, milk diverted–not recovered, no milk diverted–not recovered, no milk diverted–recovered. Thresholds of 100,000 SCC/mL and 300,000 SCC/mL for the definition of case and recovery were also applied in a sensitivity analysis but with no substantial difference in results. Linear mixed models were used for each subset to study the variation in SCC (natural logarithm of SCC divided by 1,000) and σ-conductivity (natural logarithm of standard deviation of quarter conductivities). When observing the fraction of cows with SCC <200,000 cells/mL in the recovery subsets, most cows recovered within 20 d after the initial inflammation. In the recovery subsets, both σ-conductivity and SCC stabilized, mostly within 3 to 4 wk after the initial inflammation. σ-Conductivity stabilized above the pre-onset level in all subsets and did not show a clear increase in the no-milk-diverted subgroups, whereas SCC stabilized closer to the pre-onset level. Overall, this study indicated a cutoff point between nonchronic and chronic changes in indicators 3 to 4 wk after the initial inflammation for SCC and σ-conductivity. [ABSTRACT FROM AUTHOR]

Digital Twins are computational representations of both living and non-living entities and processes, which can be used to analyse and simulate interventions in these entities and processes. When developing Digital Twins, it is important to anticipate on the societal, ethical and safety impacts they may have. Since in the agri-food domain Digital Twins are still in its infancy, it is possible to include societal values from the beginning onwards, during the research and development process. In this paper, we present four themes (i.e. resources, representations, actions and implementations) to organise the anticipation of and reflection on potential impacts of Digital Twins in the agri-food domain. Using insights from the smart farming literature, we assess for each theme which issues and questions require further research and attention, in order to develop an agenda for responsible research and innovation on Digital Twins. [ABSTRACT FROM AUTHOR]

Accurate forecasting of dairy cow milk yield is useful to dairy farmers, both in relation to financial planning and for detection of deviating yield patterns, which can be an indicator of mastitis and other diseases. In this study we developed a dynamic linear model (DLM) designed to forecast milk yields of individual cows per milking, as they are milked in milking robots. The DLM implements a Wood's function to account for the expected total daily milk yield. It further implements a second-degree polynomial function to account for the effect of the time intervals between milkings on the proportion of the expected total daily milk yield. By combining these 2 functions in a dynamic framework, the DLM was able to continuously forecast the amount of milk to be produced in a given milking. Data from 169,774 milkings on 5 different farms in 2 different countries were used in this study. A separate farm-specific implementation of the DLM was made for each of the 5 farms. To determine which factors would influence the forecast accuracy, the standardized forecast errors of the DLM were described with a linear mixed effects model (lme). This lme included lactation stage (early, middle, or late), somatic cell count (SCC) level (nonelevated or elevated), and whether or not the proper farm-specific version of the DLM was used. The standardized forecast errors of the DLM were only affected by SCC level and interactions between SCC level and lactation stage. Therefore, we concluded that the implementation of Wood's function combined with a second-degree polynomial is useful for dynamic modeling of milk yield in milking robots, and that this model has potential to be used as part of a mastitis detection system. [ABSTRACT FROM AUTHOR]

The impact of gastrointestinal (GI) nematode infections in dairy farming has traditionally been assessed using partial productivity indicators. But such approaches ignore the impact of infection on the performance of the whole farm. In this study, efficiency analysis was used to study the association of the GI nematode Ostertagia ostertagi on the technical efficiency of dairy farms. Five years of accountancy data were linked to GI nematode infection data gained from a longitudinal parasitic monitoring campaign. The level of exposure to GI nematodes was based on bulk-tank milk ELISA tests, which measure the antibodies to O. ostertagi and was expressed as an optical density ratio (ODR). Two unbalanced data panels were created for the period 2006 to 2010. The first data panel contained 198 observations from the Belgian Farm Accountancy Data Network (Brussels, Belgium) and the second contained 622 observations from the Boerenbond Flemish farmers' union (Leuven, Belgium) accountancy system (Tiber Farm Accounting System). We used the stochastic frontier analysis approach and defined inefficiency effect models specified with the Cobb-Douglas and transcendental logarithmic (Translog) functional form. To assess the efficiency scores, milk production was considered as the main output variable. Six input variables were used: concentrates, roughage, pasture, number of dairy cows, animal health costs, and labor. The ODR of each individual farm served as an explanatory variable of inefficiency. An increase in the level of exposure to GI nematodes was associated with a decrease in technical efficiency. Exposure to GI nematodes constrains the productivity of pasture, health, and labor but does not cause inefficiency in the use of concentrates, roughage, and dairy cows. Lowering the level of infection in the interquartile range (0.271 ODR) was associated with an average milk production increase of 27, 19, and 9 L/cow per year for Farm Accountancy Data Network farms and 63, 49, and 23 L/ cow per year for Tiber Farm Accounting System farms in the low- (0-90), medium- (90-95), and high- (95-99) efficiency score groups, respectively. The potential milk increase associated with reducing the level of infection was higher for highly efficient farms (6.7% of the total possible milk increase when becoming fully technically efficient) than for less efficient farms (3.8% of the total possible milk increase when becoming fully technically efficient). [ABSTRACT FROM AUTHOR]

Abstract: Subclinical infections with gastrointestinal nematodes and liver fluke are important causes of production losses in grazing cattle. Although there is an extensive compilation of literature describing the effect of these infections on animal performance, only a few attempts have been made to convert these production losses to an economic cost. Here, we propose a novel tool (ParaCalc®), available as a web-application, to provide herd-specific estimates of the costs of these infections on dairy farms. ParaCalc® is a deterministic spread-sheet model where results from diagnostic methods to monitor the helminth infection status on a herd and anthelmintic usage are used as input parameters. Default values are provided to describe the effects of the infections on production and the cost of these production losses, but the latter can be adapted to improve the herd-specificity of the cost estimate. After development, ParaCalc® was applied on input parameters that were available for 93 Belgian dairy herds. In addition, the tool was provided to 6 veterinarians and their user experiences were evaluated. The estimated median [25th–75th percentile] cost per year per cow was €46 [29–58] and €6 [0–19] for gastrointestinal nematode and liver fluke infection, respectively. For both infections, the major components in the total costs were those associated with milk production losses in the adult cows. The veterinarians evaluated ParaCalc® as a useful tool to raise the farmers’ awareness on the costs of worm infections, providing added value for their services. However, the score given for user-friendliness was diverse among users. Although the model behind ParaCalc® is a strong simplification of the real herd processes inducing economic losses, the tool may be used in the future to support economic decisions on helminth control. [Copyright &y& Elsevier]

Machine learning, particularly deep learning (DL), has become a central and state-of-the-art method for several computer vision applications and remote sensing (RS) image processing. Researchers are continually trying to improve the performance of the DL methods by developing new architectural designs of the networks and/or developing new techniques, such as attention mechanisms. Since the attention mechanism has been proposed, regardless of its type, it has been increasingly used for diverse RS applications to improve the performances of the existing DL methods. However, these methods are scattered over different studies impeding the selection and application of the feasible approaches. This study provides an overview of the developed attention mechanisms and how to integrate them with different deep learning neural network architectures. In addition, it aims to investigate the effect of the attention mechanism on deep learning-based RS image processing. We identified and analyzed the advances in the corresponding attention mechanism-based deep learning (At-DL) methods. A systematic literature review was performed to identify the trends in publications, publishers, improved DL methods, data types used, attention types used, overall accuracies achieved using At-DL methods, and extracted the current research directions, weaknesses, and open problems to provide insights and recommendations for future studies. For this, five main research questions were formulated to extract the required data and information from the literature. Furthermore, we categorized the papers regarding the addressed RS image processing tasks (e.g., image classification, object detection, and change detection) and discussed the results within each group. In total, 270 papers were retrieved, of which 176 papers were selected according to the defined exclusion criteria for further analysis and detailed review. The results reveal that most of the papers reported an increase in overall accuracy when using the attention mechanism within the DL methods for image classification, image segmentation, change detection, and object detection using remote sensing images. [ABSTRACT FROM AUTHOR]

• A conceptual framework is proposed to assess the value of information from Precision Livestock Farming (PLF) technologies. • The difference between the outcome with and without the information renders the value of information. • The value of information combines economic and non-economic considerations of the added value of using PLF technologies. • Ex-ante methods may be useful to design valuable PLF technologies. • Farm advisors could enhance the value of information provided by PLF technologies. Although precision livestock farming (PLF) technologies ensure various dimensions of more precise information, the question arises to what extent additional preciseness provides more value. Literature gives insufficient anchor points to estimate the value of information (VOI) obtained with PLF technologies. This study proposes a conceptual framework with building blocks to determine the VOI. Next, the framework is used to describe factors and existing gaps in the VOI assessment. This, finally, leads to reflections and recommendations about how to assess and improve the VOI of PLF. Literature reveals that the VOI surpasses the mere use of more precise information to take decisions, but encompasses a path from data collection to decisions with particular outcomes. The framework interlinks three building blocks: (i) data processing, (ii) decision making and (iii) impact analysis with factors influencing the VOI such as the process to transform data into information, level of precision, decision rules, social influences, the accuracy of information, herd size and prevalence of the condition measured. Besides profitability, outcomes from decisions include the impact on animal welfare, environment, food safety, and food security. The data-to-value framework allows for a better assessment of VOI and its potentials, and provides anchor points to design useful and valuable PLF technologies. The framework also helps to determine the role of advisors in interpreting the more precise information and in formulating farmer-tailored advice to apply the most optimal practices. Both technology design and advisors' role may enhance the VOI of future PLF developments and applications. [ABSTRACT FROM AUTHOR]

Dairy cows are negatively affected by the introduction of bovine viral diarrhea virus (BVDV), and consequently, produce less milk. Existing literature on potential milk production losses is based on relatively outdated data and hardly evaluates milk production loss in relation to a new BVDV infection in a surveillance system. This study determined the annual and quarterly loss in milk production of BVDV introduction in 3,126 dairy herds participating in the Dutch BVDV-free program between 2007 and 2017. Among these herds, 640 were "breakdown-herds" that obtained and subsequently lost their BVDV-free status during the study period, and 2,486 herds obtained and retained their BVDV-free status during the study period. Milk yields before and after BVDV introduction were compared through annual and quarterly linear mixed models. The fixed variables for both models included herd type (breakdown-herd or free-herd), bovine viral diarrhea status (on an annual and quarterly basis), year, season, and a random herd effect. The dependent variable was the average daily milk yield on the test day. To define the possible BVDV-introduction dates, 4 scenarios were developed. In the default scenario, the date of breakdown (i.e., loss of the BVDV-free status) was assumed as the BVDV-introduction date. For the other 3 scenarios, the BVDV-introduction dates were set at 4, 6, and 9 mo before the date of breakdown, based on the estimated birth date of a persistently infected calf. In the default scenario, the loss in milk yield due to BVDV introduction occurred mainly in the first year after breakdown, with a reduction in yield of 0.08 kg/cow per day compared with the last year before breakdown. For the other 3 scenarios, the greatest yield reduction occurred in the second year after BVDV introduction, with a loss of 0.09, 0.09, and 0.1 kg/cow per day, respectively. For the first 4 quarters after BVDV introduction in the default scenario, milk yield loss was 0.14, 0.09, 0.02, and 0.08 kg/cow per day, respectively. These quarterly results indicated that milk yield loss was greatest in the first quarter after BVDV introduction. Overall, BVDV introduction had a negative, but on average a relatively small, effect on milk yield for herds participating in the BVDV-free program. This study will enable dairy farmers and policymakers to have a clearer understanding of the quantitative milk production effect of BVDV on dairy farms in a control program. [ABSTRACT FROM AUTHOR]

More and more European countries have implemented a bovine viral diarrhea virus (BVDV) control program. The economic effects of such programs have been evaluated in simulations, but empirical studies are lacking, especially in the final stage of the program. We investigated the economic (gross margin) and production effects (milk yield, somatic cell count, and calving interval) of the herds obtaining BVDV-free certification based on longitudinal annual accounting and herd performance data from Dutch dairy herds between 2014 and 2019, the final stages of the Dutch national BVDV-free program. This study was designed as a case-control study: two types of case herds were defined for two analyses. The case herds in the first analysis are herds where the BVDV status changed from "BVDV not free" to "BVDV free" during the study period. The not-free status refers to a herd that participated in the BVDV-free program but had not yet obtained the BVDV-free certification. In the second analysis, the case herds started participating in the Dutch BVDV-free program during the study period and obtained the BVDV-free certification. Control herds in both analyses were BVDV-free during the entire study period. Potential bias between the covariates of the two herd groups was reduced by matching case and control herds using the propensity score matching method. To compare the differences between case and control herds before and after BVDV-free certification, we used the time-varying Difference-in-Differences estimation (DID) methodology. The results indicate that there was no significant change in milk yield, somatic cell count, calving interval, and gross margin upon BVDV-free certification. There are several possible explanations for the non-significant effects observed in our study, such as the final stage of the BVDV control program, not knowing the true BVDV infection situation in case herds and not knowing if control measures were implemented in case herds prior to participating in the BVDV-free program. In our study, the effects of BVDV-free certification might have been underestimated, given that the Dutch BVDV control program became mandatory during the study period, and some of the case herds might have never experienced any BVDV infection. The results of this study suggest that in the final stage of the BVDV control program, the program may no longer have a clear benefit to the herd performance of participating dairy herds. When designing national programs to eradicate BVDV, it is therefore important to include incentives for such farms to motivate them to join the program., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Yue, Wu, van der Voort, Steeneveld and Hogeveen.)

Helminth infections of cattle affect productivity in all classes of stock, and are amongst the most important production-limiting diseases of grazing ruminants. Over the last decade, there has been a shift in focus in the diagnosis of these infections from merely detecting presence/absence of infection towards detecting its impact on production. This has been facilitated by studies observing consistent negative correlations between helminth diagnostic test results and measures of productivity. Veterinarians are increasingly challenged to consider the economic aspects of their work, and the use of these tests should now be integrated in economic evaluation frameworks for improved decision making. In this paper, we review recent insights in the farm-specific economic impact of helminth infections on dairy cattle farms as well as in farmer attitudes and behaviour regarding helminth control. Combining better economic impact assessments of helminth infections together with a deeper understanding of the non-economic factors that drive a farmer's animal health decisions should result in more effective control strategies and increased farmer satisfaction.