Farm Management Software Research Articles

Electronic tracking and identification of animals in agriculture for monitoring herd development and health

Agricultural business methods are no longer possible without computer technology, which helps to identify animals, record productivity and monitor their health. Therefore, the study aims to conduct a comparative analysis of the functionality of the main dairy farm management software for their objective assessment from the point of view of milk producers. For this purpose, information from the official websites of software products was analysed and scientific articles analysing this software were collected. As a result, all software products were divided into 3 categories: software for controlling dairy productivity and managing processes on a dairy farm from dairy equipment manufacturers; such software products include DairyPlan C21 (GEA), DATAFLOW II (Milkline), AfiFarm (Afimilk), DelPro (DeLaval) and FARMSOFT (Farmtec a. s.); dairy farm management software DairyComp 305 and UNIFORM-Agri, as well as farm software from post-Soviet developers – MilkCentre (CIAB Expert). Programmes from dairy equipment manufacturers were almost at the same level and had virtually all the necessary blocks for identifying, controlling and managing animals on a dairy farm. The software developed for dairy farm management is more effective in tandem with one of the milking parlour programmes. This class of software expands the possibilities for managing reproductive and veterinary work on the farm, as well as reduces the workload on staff by generating automatic reports for regulatory authorities and management. The third class of programs is the least developed compared to the above, but it compensates for this shortcoming by constant improvement due to the continuous contact of developers with the manufacturer. Thus, there is a significant number of software products on the software market that can provide dairy management for farms of various sizes

Technological Applications in Smart Farming: A Bibliometric Analysis

Precision agriculture, often known as smart farming, employs cutting-edge technology to change farming processes. This paper covers smart farming's history, important advancements, and possible impacts on agriculture. The essay begins with the history of "smart farming," or precision agriculture, and how GPS technology improved fertilization, pest control, and irrigation. Since then, automation and sensor technology have enabled continuous crop vitality, moisture, and environmental monitoring, enabling empirical decision-making. Networking technologies, especially the Internet of Things, have transformed smart farming. Farmers may gather, analyze, and apply real-time data using connected drones, satellite images, and farm management software. Big data analytics and AI allow farmers to leverage enormous datasets for crop health monitoring, production forecasts, and resource management. Smart farming improves accuracy and sustainability. By using resources efficiently, farmers may reduce their environmental impact. Due to remote monitoring and control, farmers can adapt to changing conditions and manage their operations from anywhere. Smart farming has numerous benefits, but various barriers limit its broad implementation. Due to expensive startup costs, specialized training, and limited Internet connection, some farmers are unwilling to use these technologies. Governments, NGOs, and industry actors must collaborate to provide financing, training, and improved connections to address these difficulties. This review and study of smart farming's growth highlights its revolutionary potential in agriculture. Smart farming may enhance output, optimize input returns, and ensure long-term viability, making it an exciting new route for agriculture.

Analysis of Jersey versus Holstein breed profitability on north central US dairies

With over 9 million cows in the United States, Holstein is the dominant breed in the US dairy population; however, the US Jersey population is growing. The objective of this study was to determine the profitability of Holstein and Jersey cows managed similarly on the same farms. Holstein and Jersey economic performance was compared within 3 north central US dairies, each milking more than 500 cows. The herds' average distribution was 21% Jersey (27 ± 0.67 kg/d milk, 4.92% ± 0.24 fat, 3.72% ± 0.03 protein) and 79% Holstein (37 ± 1.98 kg/d milk, 3.85% ± 0.21 fat, 3.17% ± 0.17 protein). A comparative budget approach was used to assess economic factors that differed between the breeds on a per cow annual basis, based on the assumption that an existing farm would be constrained by stalls and parlor to an equal number of Jersey and Holstein cows. Data from 2020 were gathered from farm management software, on-farm evaluations, and producer interviews. Sensitivity analysis was performed to determine which conditions would lead to different conclusions. Factors considered in the analysis included milk and component production, milk bonuses, ration prices, and dry matter (DM) intake. In a 2021 price scenario, Holstein cows ranged from $345 to $601 more profitable than Jersey cows on a per cow annual basis. Although Jersey cows had an advantage in component concentration, Holstein cows produced 13 ± 4.7% more fat and 22 ± 6.6% more protein annually due to greater milk yield. This accounted for most of the profitability advantage for Holsteins; 78% of the revenue advantage for Holstein cows came from increased component production. Few health and reproductive differences were found. The sensitivity analysis revealed, if all other factors remained the same, Jersey profitability would equal that of Holstein if any of the following changes occurred (assuming no change in Holstein metrics): mean Jersey milk production increased to 31 kg/d; milk price adjustments decreased from −$0.008 to −$0.11 per kg fluid milk; lactating cow ration price increased from $0.27 per kg DM to $0.53 per kg DM; or Jersey DM intake decreased from 20 to 15 kg/d. The study did not consider crossbred profitability or new infrastructure investments. In conclusion, Holstein cows were more profitable than Jersey cows on these 3 north central US dairies.

Environmental and biological factors that influence feeding behavior of Holstein calves in automated milk feeding systems

Automated milk feeders (AMF) used for dairy calves continuously provide individual feeding behavior measurements. The objective of this retrospective cohort study was to evaluate the association between temperature-humidity index (THI), birth weight, and dam parity characteristics on feeding behavior (i.e., milk consumption and drinking speed). Historical data sets generated from a single commercial dairy farm, where healthy (not treated for bovine respiratory disease, enteric disease, or injury) Holstein calves were fed up to 24 L/d of milk, were used for the analysis. A total of 5,312 female Holstein calves born between August 2015 and August 2021 (mean birth weight ± standard deviation: 40.7 ± 4.7 kg) on a commercial dairy farm were fed up to 24 L/d of nonsaleable milk for the first 32 d. For the analyses, feeding behavior data from the AMF system were combined with demographic data from the farm management software, and weather records from the closest public weather station (7 km away). Linear mixed models used to analyze daily milk consumption and drinking speed included THI, birth weight, dam parity, and feeding day as fixed effects, and feeder and calf within feeder as random effects. These models explained 57% of the total variation in milk consumption and 48% of the variation in drinking speed. Calves born from primiparous cows had the lowest milk consumption and the greatest drinking speed in comparison to calves born from multiparous cows. Calves with heavier birth weights had higher milk consumption and faster drinking speed than lighter calves. Drinking speed was negatively associated with THI. Including data derived from individual calves and their environmental conditions in data sets exploring feeding behavior from AMF would control for variation and improve the predictive models for performance assessment.

Effect of inducing accessory corpus luteum formation with gonadotropin-releasing hormone or human chorionic gonadotropin on the day of embryo transfer on fertility of recipient dairy heifers and lactating cows

The objective was to determine the effect of inducing an accessory corpus luteum (CL) using GnRH or human chorionic gonadotropin (hCG) on the day of in vitro-produced (IVP) embryo transfer (ET) on pregnancy per ET (P/ET) and calving/ET in dairy heifers and lactating cows. Dairy heifers (11–15 mo of age; n = 1,547) and lactating cows (n = 1,480) detected in estrus by tail chalk (d 0) were used as recipients. Before ET, the presence of a CL was evaluated by transrectal palpation from d 6 to 9 of the estrous cycle. Animals with a CL were randomly assigned to receive 1 of 3 treatments immediately before ET: control (no treatment; n = 1,009), GnRH (86 µg of GnRH; n = 1,085) and hCG (2,500 IU; n = 1,069). Embryos were implanted in the uterine horn ipsilateral to the ovary with a CL (fresh IVP embryos, n = 2,544; vitrified IVP embryos n = 545; slow-freezing IVP embryos, n = 74). Pregnancy diagnosis was performed on d 37 ± 3 of gestation by transrectal palpation. Pregnancy loss data and calving records were collected from the dairy farm management software. Treatment did not affect P/ET, calving/ET, or pregnancy loss either overall or within parity. When treatments inducing CL formation were combined (GnRH + hCG), heifers tended to have greater P/ET than controls (67.7 vs. 63.5%, respectively). Yet, calving/ET were similar. Response variables were also analyzed within embryo type and parity. For heifers receiving stage 6 (blastocyst) fresh IVP embryos, hCG had greater P/ET than controls (74.5 vs. 51.1%, respectively). In addition, GnRH tended to have greater P/ET than controls (67.8 vs. 51.2%, respectively). However, calving/ET in heifers receiving blastocyst fresh IVP embryos was similar among treatments. When only stage 7 (expanded blastocyst) fresh IVP embryos were considered, primiparous GnRH cows had greater P/ET (59.3 vs. 47.1%) and calving/ET (48.6 vs. 38.1%) than hCG. Moreover, hCG showed decreased calving/ET compared with controls in primiparous cows transferred with expanded blastocyst fresh IVP embryos. In summary, the effects of hCG or GnRH at ET on P/ET and calving/ET were inconsistent according to different embryo characteristics (e.g., embryo stage) and parity of recipients. Furthermore, treatment did not improve the overall fertility outcomes for recipient animals receiving IVP embryos.

Myth or reality? The Digitalization of Climate-Smart Agriculture (DCSA) practices in smallholding agriculture in the Bono East Region of Ghana

Digitalization of Climate-Smart Agriculture practices leverages the power of digital agriculture tools/services (DATs) of any form (hardware, software, or data) in Climate-Smart Agriculture (CSA) practices to promote enhanced adaptation, GHGs emissions mitigation and increase productivity for smallholding agriculture. This research used a mixture of participatory and learning approaches with an emphasis on Expert Interviews and a Large-scale Household Survey involving 1219 farmers in the Bono East of Ghana to assess the awareness and utilization of DATs in smallholder farmers' CSA practices. Precisely, we assess farmers' engagement with Digital Agriculture Services (DAS) and DAT such as; TVs, Radios, Mobile phones/Tablets, Unmanned Aerial Vehicles (UAV)/Drones, Soil Sensor, Moisture Meters, Rain Gauges, Farm Management Software, Smartphone Applications, and Field Thermometers. The research suggests that the ubiquity of TVs, Radios, and feature phones in rural communities makes these tools the most used devices in farmers' climate-smart practices. However, the level of awareness, availability, accessibility, and utilization of complex tools such as UAVs and simpler tools such as soil sensors, moisture meters, field thermometers, rain gauges, smartphone applications (Facebook, WhatsApp, Twitter, etc.), and farm management software is minimal among rural farmers.The DAS facilitating farmers' climate-smart practices is limited to Digital Agroadvisory Purposes (digital extension), Agri-Digital Finance, and Digital Procurement services, while engagement with other DAS, such as Agri E-Commerce which facilitates most CSA Institution/Market Smart practice, is non-existing in rural communities. In addition, the Digitalization of Climate-Smart Agriculture, in its present form, is only limited to a few CSA practices and DATs engagement among smallholders owing to unmet training and information needs for most Climate-Smart Agriculture practices and interventions. Challenges such as DATs' unavailability, inaccessibility, high cost, high (digital)illiteracy, and inadequate extension support for the digitalization of CSA practices limit uptake. The study proposes increased capacity building for smallholders on CSA practice and interventions. Likewise, a strong public–private partnership across multiple scales is needed to stimulate needed investment to enhance farmers' access to affordable, easy-to-use, and tailor-made DATs while recognizing the power dependence and inequalities these digital tools may unleash in rural communities. Finally, increasing sensitization on DAT's use and benefits in rural communities and the larger population is critical to enhancing the widespread Digitalization of Climate-Smart Agriculture practices in smallholding agriculture.

Adoption of Precision Technologies by Brazilian Dairy Farms: The Farmer's Perception.

Simple SummaryUnderstanding the perception of dairy farmers regarding precision livestock technologies is crucial for creating strategic actions that will increase the rate of adoption and usage of such technologies. A survey study was applied to 378 dairy farms located in Brazil. The farmers were characterized based on technology usage, farmer profile, farm characteristics, and production indexes. The farms were classified into seven clusters: (1) top yield farms; (2) medium–high yield, medium-tech; (3) medium yield and top high-tech; (4) medium yield and medium-tech (5); young medium–low yield and low-tech; (6) elderly medium–low yield and low-tech; and (7) low-tech grazing. Our study helped to elucidate the farmer’s perception about precision technologies and to shed light on challenges that need to be addressed by scientific research and extension programs.The use of precision farming technologies, such as milking robots, automated calf feeders, wearable sensors, and others, has significantly increased in dairy operations over the last few years. The growing interest in farming technologies to reduce labor, maximize productivity, and increase profitability is becoming noticeable in several countries, including Brazil. Information regarding technology adoption, perception, and effectiveness in dairy farms could shed light on challenges that need to be addressed by scientific research and extension programs. The objective of this study was to characterize Brazilian dairy farms based on technology usage. Factors such as willingness to invest in precision technologies, adoption of sensor systems, farmer profile, farm characteristics, and production indexes were investigated in 378 dairy farms located in Brazil. A survey with 22 questions was developed and distributed via Google Forms from July 2018 to July 2020. The farms were then classified into seven clusters: (1) top yield farms; (2) medium–high yield, medium-tech; (3) medium yield and top high-tech; (4) medium yield and medium-tech; (5) young medium–low yield and low-tech; (6) elderly medium–low yield and low-tech; and (7) low-tech grazing. The most frequent technologies adopted by producers were milk meters systems (31.7%), milking parlor smart gate (14.5%), sensor systems to detect mastitis (8.4%), cow activity meter (7.1%), and body temperature (7.9%). Based on a scale containing numerical values (1–5), producers indicated “available technical support” (mean; σ2) (4.55; 0.80) as the most important decision criterion involved in adopting technology, followed by “return on investment—ROI” (4.48; 0.80), “user-friendliness” (4.39; 0.88), “upfront investment cost” (4.36; 0.81), and “compatibility with farm management software” (4.2; 1.02). The most important factors precluding investment in precision dairy technologies were the need for investment in other sectors of the farm (36%), the uncertainty of ROI (24%), and lack of integration with other farm systems and software (11%). Farmers indicated that the most useful technologies were automatic milk meters systems (mean; σ2) (4.05; 1.66), sensor systems for mastitis detection (4.00; 1.57), automatic feeding systems (3.50; 2.05), cow activity meter (3.45; 1.95), and in-line milk analyzers (3.45; 1.95). Overall, the concerns related to data integration, ROI, and user-friendliness of technologies are similar to those of dairy farms located in other countries. Increasing available technical support for sensing technology can have a positive impact on technology adoption.

The effect of pegbovigrastim on early-lactation disease, production, and reproduction in dairy cows

The objective of this randomized, double-blind, controlled trial was to evaluate the effect of pegbovigrastim (pegylated recombinant bovine granulocyte colony-stimulating factor) on early-lactation disease, milk yield, and reproduction on commercial dairy farms. A total of 1,607 Holstein cows from 6 farms in Ontario and Québec, Canada, were randomly assigned to receive two 2.7-mL subcutaneous injections of either 15 mg of pegbovigrastim (n = 798; Imrestor, Elanco) or sterile physiological saline (placebo; n = 809). The first injection was administered by investigators 1 wk before expected calving, and the second by farm personnel within 24 h after calving, according to the product label. Producers inspected cows daily and using standardized disease definitions, recorded cases of retained placenta, metritis, displaced abomasum, and clinical mastitis until 63 d in milk. Progesterone concentration was measured in serum at wk 3, 5, 7, and 9 postpartum. Cows were examined for purulent vaginal discharge using the Metricheck (Simcro) device and endometritis using the cytobrush method at wk 5 postpartum. Milk production and reproduction data were obtained from farm management software and the national milk recording database. Disease and culling outcomes were assessed with logistic regression, milk production with linear regression, and time-to-event outcomes with proportional hazards regression. All analyses considered parity and pre-treatment body condition score and their interaction with treatment, and accounted for clustering of cows within farm. In a subset of 246 cows, the effect of treatment on metabolic markers (serum concentrations of glucose, β-hydroxybutyrate, nonesterified fatty acids, cholesterol, haptoglobin, albumin, and calcium) was assessed in wk 1 and 2 postpartum. Pegbovigrastim had no significant effects on the incidence of retained placenta, metritis, displaced abomasum, clinical or subclinical mastitis, purulent vaginal discharge, or endometritis. Treatment reduced the serum concentration of glucose, slightly reduced the concentration of albumin, and slightly increased concentrations of β-hydroxybutyrate and nonesterified fatty acids, with no effect on the other markers. There were no differences between treatments in culling risk, time to first insemination, pregnancy at first insemination, or time to pregnancy. Milk yield over the first 3 test days of lactation was 1.0 kg per day lower in the pegbovigrastim group, although a mechanism for that effect could not be explained through analysis of our data.

ІННОВАЦІЙНІ ПІДХОДИ ДО МОТИВАЦІЇ ПЕРСОНАЛУ АГРАРНИХ ПІДПРИЄМСТВ В УМОВАХ ЦИФРОВІЗАЦІЇ

The article considers modern innovative approaches that are used by agricultural enterprises to motivate staff in modern business conditions. Most farmers understand the benefits of using digital technologies in agriculture. According to representative polls conducted by the Bitkom industry association in Berlin, almost 90 percent of all agricultural enterprises link digitization to more efficient use of resources. More than half of the companies surveyed already use digital applications, especially smart agricultural machinery and farm management software. The potential of the global market for digital products and business models is huge. The trend towards high-tech systems is growing rapidly. The main prerequisite for the transition to high-tech systems is the expansion of broadband, as well as Internet access. Digital applications and platforms can significantly change the way knowledge is processed, transmitted, accessed, and used. For farmers, digital programs provide decision-making opportunities that were previously impossible, which could potentially lead to drastic changes in personnel management. As the number of smart machines and sensor networks in enterprises grows, as data continues to grow, industrial relations become increasingly data-driven, raising critical questions about how digital agriculture will need new professionals, support decision-making, and interact with potential regimes, knowledge processing and potentially violate them. This has significant implications for the entire staff knowledge system, including the roles and capabilities of those who provide consulting services to farmers, as well as those responsible for data analysis, and the organizations and institutions that link and support their work. The realities of digitalization are such that in order to maintain their competitiveness in the labor market, a person must constantly learn, develop in various ways and acquire new skills. In the digital economy, competitive advantage will be given to those who are able to change quickly and adapt to dynamic living conditions and are able to think creatively and on a large scale. But in any case, the organization of the system of remuneration of personnel should be based on the predicted style of management, which involves the creation of flexible structures.

Genetic parameters of passive transfer of immunity for US organic Holstein calves

Passive transfer of immunity is important for calf health and survival. The objectives of this study were to estimate genetic parameters for calf passive transfer of immunity through producer-recorded serum total protein (STP) and to determine associations with other routinely evaluated traits in organic Holstein calves (n = 16,725) that were born between July 2013 to June 2018; a restricted subset (n = 7,518) of calves with known Holstein maternal grandsires was analyzed separately. Producers measured STP on farm, and STP was extracted from farm management software. Failure of passive transfer of immunity (FPT) was declared for calves with STP ≤5.2 g/dL. Calves that had the opportunity to reach 1 yr of age were recorded as either staying in the herd or leaving the herd (STAY365). Univariate and threshold models were fitted for STP and FPT, respectively, and included the fixed effects of herd-year-month of birth, calf age in days at STP measurement, dam age in years, and random effects of animal and birthdate within herd. Model effects for STAY365 included the fixed effects of herd-year-month of birth and random effects of animal and birthdate within herd. Multivariate analyses of STP with FPT or STAY365 were conducted to determine the genetic correlation between traits and STP was also regressed on gestation length. Heritability estimates of STP were 0.06 and 0.08 for full and restricted data, respectively. Heritability estimates for FPT were 0.04 and 0.06 for full and restricted data, respectively. The genetic correlation between STP and FPT was near unity. Heritability estimates for STAY365 ranged from 0.08 to 0.11 with genetic correlation estimates between STP and STAY365 ranging from 0.19 and 0.25. Approximate genetic correlations were estimated for sires (n = 302 and n = 256 for full and restricted data, respectively) with at least 10 daughters for STP and predicted transmitting abilities for health, calving traits, and production. Positive approximate genetic correlations were estimated for STP with cow livability, productive life, net merit dollars, and milk yield; favorable approximate genetic correlations were observed for daughter and sire calving ease, and sire stillbirth. Longer gestation length was associated with reduced STP genetically and phenotypically. These results suggest that passive transfer as measured through STP is heritable and favorably correlated with current measures of health, calving, and production.

Aggregate Farming in the Cloud: The AFarCloud ECSEL project

Farming is facing many economic challenges in terms of productivity and cost-effectiveness. Labor shortage partly due to depopulation of rural areas, especially in Europe, is another challenge. Domain specific problems such as accurate monitoring of soil and crop properties and animal health are key factors for minimizing economical risks, and not risking human health. The ECSEL AFarCloud (Aggregate Farming in the Cloud) project will provide a distributed platform for autonomous farming that will allow the integration and cooperation of agriculture Cyber Physical Systems in real-time in order to increase efficiency, productivity, animal health, food quality and reduce farm labor costs. Moreover, such a platform can be integrated with farm management software to support monitoring and decision-making solutions based on big data and real-time data mining techniques.

Syngenta acquires software firm Cropio

Swiss agriculture firm Syngenta has purchased the assets of Cropio, a technology firm with a focus on Eastern Europe, for an undisclosed sum. Cropio’s farm management software provides imaging, record keeping, and equipment tracking to help farmers make critical decisions. Syngenta says the deal is a turning point for its digital strategy in Europe. The company has made deals to make similar tools available to its customers in the US, Brazil, and China.

A longitudinal study of digital cushion thickness and its function as a predictor for compromised locomotion and hoof lesions in Holstein cows.

Lameness is a major animal welfare and economic issue for the dairy industry and is a challenge to overcome due to multifaceted causes. Digital cushion thickness (DCT) is a strong predictor of lameness and is phenotypically associated with incidence of claw horn disruption lesions (CHDL; sole ulcers and white line disease). We hypothesized that DCT varies between digits and across lactation within the cow. This variation could be characterized to predict the occurrence of CHDL or compromised locomotion. BCS, visual locomotion score (VLS), DCT, and presence or absence of lesions were collected at 4 time points: <40 d prepartum (DPP), 1 to 30 d in milk (DIM), 90 to 120 DIM, and ≥255 DIM for 183 commercial Holstein cows enrolled in the study. Cows underwent digital sonographic examination for the measurement of DCT evaluated at the typical sole ulcer site beneath the flexor tuberosity for the right front medial and lateral digits and right hind medial and lateral digits. Factors such as parity number and stage in lactation were obtained from farm management software (DairyComp 305; Valley Agricultural Software, Tulare, CA). Cows were grouped by parity: primiparous (parity = 1) or multiparous (parity ≥ 2). The prevalence of CHDL among time points ranged from 0% to 4.2% for primiparous cows vs. 2.5% to 25% for multiparous cows, whereas the prevalence of lameness based on VLS of 3 to 5 ranged from 1.7% to 8.3% for primiparous cows vs. 12.7% to 33% for multiparous cows. DCT varied within primiparous and multiparous cows based on stage of lactation and digit (P < 0.05) and was thicker for both parity groups prior to dry off (≥255 DIM) and thinnest prior to calving (<40 DPP) and after peak lactation (90 to 120 DIM). The DCT of the front medial digit was thickest for primiparous heifers, whereas the hind lateral digit was thickest for multiparous cows. The DCT of the hind medial digit was thinnest for both parity groups. Parity group and DCT of the hind lateral digit <40 DPP were important predictors of CHDL (P < 0.05), whereas parity group and DCT of the hind lateral digit and front lateral digit at 1 to 30 DIM were key predictors of VLS lameness (P < 0.05). These results may help identify animals with higher odds of developing these diseases by highlighting key time points and specific digits of importance for monitoring. In addition, it improves our biological understanding of the relationship between DCT and lameness.

Predicting blood β-hydroxybutyrate using milk Fourier transform infrared spectrum, milk composition, and producer-reported variables with multiple linear regression, partial least squares regression, and artificial neural network

Prediction of postpartum hyperketonemia (HYK) using Fourier transform infrared (FTIR) spectrometry analysis could be a practical diagnostic option for farms because these data are now available from routine milk analysis during Dairy Herd Improvement testing. The objectives of this study were to (1) develop and evaluate blood β-hydroxybutyrate (BHB) prediction models using multivariate linear regression (MLR), partial least squares regression (PLS), and artificial neural network (ANN) methods and (2) evaluate whether milk FTIR spectrum (mFTIR)-based models are improved with the inclusion of test-day variables (mTest; milk composition and producer-reported data). Paired blood and milk samples were collected from multiparous cows 5 to 18 d postpartum at 3 Wisconsin farms (3,629 observations from 1,013 cows). Blood BHB concentration was determined by a Precision Xtra meter (Abbot Diabetes Care, Alameda, CA), and milk samples were analyzed by a privately owned laboratory (AgSource, Menomonie, WI) for components and FTIR spectrum absorbance. Producer-recorded variables were extracted from farm management software. A blood BHB ≥1.2 mmol/L was considered HYK. The data set was divided into a training set (n = 3,020) and an external testing set (n = 609). Model fitting was implemented with JMP 12 (SAS Institute, Cary, NC). A 5-fold cross-validation was performed on the training data set for the MLR, PLS, and ANN prediction methods, with square root of blood BHB as the dependent variable. Each method was fitted using 3 combinations of variables: mFTIR, mTest, or mTest + mFTIR variables. Models were evaluated based on coefficient of determination, root mean squared error, and area under the receiver operating characteristic curve. Four models (PLS-mTest + mFTIR, ANN-mFTIR, ANN-mTest, and ANN-mTest + mFTIR) were chosen for further evaluation in the testing set after fitting to the full training set. In the cross-validation analysis, model fit was greatest for ANN, followed by PLS and MLR. Diagnostic strength after cross-validation was poorest for MLR and was similar for ANN and PLS. Models that used mTest + mFTIR variables performed marginally better than models that used only mFTIR or mTest variables. These results suggest that blood BHB prediction models that use mFTIR + mTest variables may be useful additions to existing HYK diagnostic and management programs.

Association of immediate postpartum plasma calcium concentration with early-lactation clinical diseases, culling, reproduction, and milk production in Holstein cows

The objective of this study was to evaluate the association of postpartum plasma Ca concentration with early-lactation disease outcomes, culling within 60 d in milk, pregnancy to first service, and milk production. A total of 1,453 cows from 5 commercial dairy farms in New York State were enrolled in a prospective cohort study from February to November 2015. Blood samples were collected within 12 h of parturition, and plasma was submitted to a diagnostic laboratory for total Ca measurement. Early-lactation disease, reproductive performance, and milk production from Dairy Herd Improvement Association (DHIA) test-day data were compiled from each farm's management software. Multivariable Poisson regression models were built to evaluate the association of plasma Ca with the risks of retained placenta (RP), metritis, displaced abomasum (DA), clinical mastitis, culling within 60 d in milk, and pregnancy to first service. Repeated-measures ANOVA were used to evaluate the association of Ca at parturition with milk production across the first 9 DHIA tests. Herd was considered a random effect in all models. Primiparous cows were modeled separately from multiparous cows if differential responses were observed. Calcium was not associated with the risk of RP, metritis, clinical mastitis, or pregnancy to first service in primiparous or multiparous cows. For multiparous cows only, higher Ca concentration tended to be associated with increased culling within the first 60 d in milk. Multiparous cows with Ca ≤1.85 mmol/L had an increased risk of being diagnosed with a DA compared with cows with Ca >1.85 mmol/L. For the milk production models, Ca was not associated with the amount of milk produced within the first 9 DHIA tests in primiparous cows; however, multiparous cows with Ca ≤1.95 mmol/L produced, on average, 1.1 kg more milk per day across the 9 DHIA tests than their multiparous counterparts with Ca >1.95 mmol/L. Our results indicate that plasma Ca concentration measured within 12 h of parturition is a poor predictor of early-lactation health outcomes. Reduced Ca concentration in the immediate postpartum period was associated with higher milk production in multiparous cows. From these results, we caution that studies attempting to categorize subclinical hypocalcemia based on a single sample in the immediate postpartum period could misclassify the disorder.

Lameness Prevalence and Risk Factors in Large Dairy Farms in Upstate New York. Model Development for the Prediction of Claw Horn Disruption Lesions.

The main objectives of this prospective cohort study were a) to describe lameness prevalence at drying off in large high producing New York State herds based on visual locomotion score (VLS) and identify potential cow and herd level risk factors, and b) to develop a model that will predict the probability of a cow developing claw horn disruption lesions (CHDL) in the subsequent lactation using cow level variables collected at drying off and/or available from farm management software. Data were collected from 23 large commercial dairy farms located in upstate New York. A total of 7,687 dry cows, that were less than 265 days in gestation, were enrolled in the study. Farms were visited between May 2012 and March 2013, and cows were assessed for body condition score (BCS) and VLS. Data on the CHDL events recorded by the farm employees were extracted from the Dairy-Comp 305 database, as well as information regarding the studied cows’ health events, milk production, and reproductive records throughout the previous and subsequent lactation period. Univariable analyses and mixed multivariable logistic regression models were used to analyse the data at the cow level. The overall average prevalence of lameness (VLS > 2) at drying off was 14%. Lactation group, previous CHDL, mature equivalent 305-d milk yield (ME305), season, BCS at drying off and sire PTA for strength were all significantly associated with lameness at the drying off (cow-level). Lameness at drying off was associated with CHDL incidence in the subsequent lactation, as well as lactation group, previous CHDL and ME305. These risk factors for CHDL in the subsequent lactation were included in our predictive model and adjusted predicted probabilities for CHDL were calculated for all studied cows. ROC analysis identified an optimum cut-off point for these probabilities and using this cut-off point we could predict CHDL incidence in the subsequent lactation with an overall specificity of 75% and sensitivity of 59%. Using this approach, we would have detected 33% of the studied population as being at risk, eventually identifying 59% of future CHDL cases. Our predictive model could help dairy producers focusing their efforts on CHDL reduction by implementing aggressive preventive measures for high risk cows.

Driving farm performance using information systems

FarmIQ Systems Limited was established as a Primary Growth Partnership (PGP) between the Ministry for Primary Industries (MPI), the meat processor Silver Fern Farms and Landcorp Farming Limited. FarmIQ aims to create a truly integrated value and information chain for red meat from product back to farm. The major output of this partnership is the FarmIQ System ‒ farm management software developed in New Zealand for New Zealand farmers. Hill country farmers have historically been at a disadvantage compared to their lowland counterparts in terms of the ease of quantifying farm performance. The FarmIQ System is a cloudbased tool that addresses these challenges of scale and isolation. It allows all types of red meat farmers to plan, communicate, record, analyse and report information about land, feed and animals easily and accurately. Keywords: FarmIQ System, integration, better decisions

Study On Correlation Of Data From Yield Monitoring System And Hand Samples

Abstract One of the basic tools used in precision agriculture technology is yield monitoring and mapping. Yield maps are used to monitor the efficiency of applied inputs, to assess the variability of field and have potential to be used in farm records and farm management software. The paper deals with comparison of yield maps with information on yield obtained from hand sampling. Analyses are based on five-year data from one field with spring barley, oil seed rape, winter wheat and maize crop rotation. Results show that the correlation coefficient across the time period reached a value of 0.89 (p &lt;0.05), which means that the yield maps show the yield trend of the field. However, looking at the data in more detail and comparing the pairs of data for selected (discrete) monitoring points, the error of estimation was calculated. This error showed that the yield monitor underestimated or overestimated the yield in average by 25 %. Therefore, the data cannot be used to evaluate the absolute yield at selected place of the field.

Livestock farm management software

Livestock farm management software

Near-infrared imagery from unmanned aerial systems and satellites can be used to specify fertilizer application rates in tree crops

Aerial images obtained using an unmanned aerial system (UAS) were used to create a classified image showing tree canopy health in a macadamia orchard. The resulting map was used to modify management of a macadamia plantation. Vegetation indices, principally the Canopy Chlorophyll Content Index (CCCI), derived from both UAS and WorldView2 satellite imagery, were compared and correlated with spectral radiometry and leaf nitrogen levels determined by field sampling. Classified CCCI images from both sensor types were integrated into farm management software (PAM Ultracrop) and processed into a suitable format for driving a GPS-controlled fertilizer spreader for more effective control of nitrogen application rates. Applying fertilizer at a variable rate according to tree health will result in cost savings to the industry and potentially increase production.