Relationship between body weight and hip width in dairy buffaloes (Bubalus bubalis).

The objective of the present study was to evaluate the relationship between body weight (BW) and hip width (HW) in dairy buffaloes (Bubalus bubalis). HW was measured in 215 Murrah buffaloes with a BW of 341 ± 161.6 kg, aged between three months and five years, and raised in southeastern Mexico. Linear and non-linear regressions were used to construct the prediction models. The goodness of fit of the models was evaluated using the Akaike information criterion (AIC), Bayesian information criterion (BIC), coefficient of determination (R2), mean squared error (MSE), and root MSE (RMSE). Additionally, the developed models were evaluated through internal and external cross-validation (k-folds) using independent data. The ability of the fitted models to predict the observed values was assessed based on the root mean square error of prediction (RMSEP), R2, and mean absolute error (MAE). The relationship between BW and HW showed a high correlation coefficient (r = 0.96, P < 0.001). The chosen fitted model to predict BW was: -176.33 (± 40.83***) + 8.74 (± 1.79***) × HW + 0.04 (± 0.01*) × HW2, because it presented the lowest MSE, RMSE, and AIC values, which were 1228.64, 35.05 and 1532.41, respectively. Therefore, with reasonable accuracy, the quadratic model using hip width may be suitable for predicting body weight in buffaloes.

Public Health Implications of Drought in a Climate Change Context: A Critical Review.

Extreme weather events are expected to increase due to climate change, which could pose an additional burden of morbidity and mortality. In recent decades, drought severity has increased in several regions around the world, affecting health by increasing the risk of water-, food-, and vector-borne diseases, malnutrition, cardiovascular and respiratory illness, mental health disorders, and mortality. Drought frequency and severity are expected to worsen across large regions as a result of a decrease in precipitation and an increase in temperature and atmospheric evaporative demand, posing a pressing challenge for public health. Variation in impacts among countries and communities is due to multiple factors, such as aging, socioeconomic status, access to health care, and gender, affecting population resilience. Integrative proactive action plans focused on risk management are required, and resources should be transferred to developing countries to reduce their vulnerability and risk.

Predicting live weight using body volume formula in lactating water buffalo.

Live weight (LW) is an important piece of information within production systems, as it is related to several other economic characteristics. However, in the main buffalo-producing regions in the world, it is not common to periodically weigh the animals. We develop and evaluate linear, quadratic, and allometric mathematical models to predict LW using the body volume (BV) formula in lactating water buffalo (Bubalus bubalis) reared in southeastern Mexico. The LW (391.5 ± 138.9 kg) and BV (333.62 ± 58.51 dm3) were measured in 165 lactating Murrah buffalo aged between 3 and 10 years. The goodness-of-fit of the models was evaluated using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), coefficient of determination (R2), mean-squared error (MSE) and root MSE (RMSE). In addition, the developed models were evaluated through cross-validation (k-folds). The ability of the fitted models to predict the observed values was evaluated based on the RMSEP, R2, and mean absolute error (MAE). LW and BV were significantly positively and strongly correlated (r = 0.81; P < 0.001). The quadratic model had the lowest values of MSE (2788.12) and RMSE (52.80). On the other hand, the allometric model showed the lowest values of BIC (1319.24) and AIC (1313.07). The Quadratic and allometric models had lower values of MSEP and MAE. We recommend the quadratic and allometric models to predict the LW of lactating Murrah buffalo using BV as a predictor.

Predicting body weight through biometric measurements in growing hair sheep using data mining and machine learning algorithms.

Determination of live weight, which is one of the most important features that determine meat production, is a very important issue for herd management and sustainable livestock. In this context, the necessity of finding alternative methods has emerged, especially in rural conditions, due to the difficulties to be experienced in finding the weighing tool. Especially for conditions with no weighing tool, it has been tried to establish relations between the information obtained from body measurements and live weight. Since these studies will differ from species to species and breed to breed, the need for new studies is extremely high. For this aim, it is to evaluate the body measurement information obtained with the present study using several statistical approaches. To implement this aim, several data mining and machine learning algorithms such as multivariate adaptive regression splines (MARS), classification and regression tree (CART), and support vector machine regression (SVR) algorithms were used for training (70%) and test (30%) sets. To predict final body weight, 280 hair sheep breeds (162 female and 118 male) ranging from 2 months to 3 years were used with different data mining and machine learning approaches. Various goodness-of-fit criteria were used to evaluate the performances of the aforementioned algorithms. Although the MARS and SVR algorithms gave the same and highest results in terms of R2 and r values for both the train and the test sets, the SVR algorithm is one of the methods to be recommended as a result of this study, especially when other goodness-of-fit criteria are evaluated. In conclusion, the usage of SVR algorithms may be a useful tool of machine learning approaches for detecting the hair sheep breed standards and may contribute to increasing the sheep meat quality in Mexico.

Predicting carcass tissue composition in Blackbelly sheep using ultrasound measurements and machine learning methods.

This study aimed to predict Blackbelly sheep carcass tissue composition using ultrasound measurements and machine learning models. The models evaluated were decision trees, random forests, support vector machines, and multi-layer perceptrons and were used to predict the total carcass bone (TCB), total carcass fat (TCF), and total carcass muscle (TCM). The best model for predicting the three parameters, TCB, TCF, and TCM was random forests, with mean squared error (MSE) of 0.31, 0.33, and 0.53; mean absolute error (MAE) of 0.26, 0.29, and 0.53; and the coefficient of determination (R2) of 0.67, 0.69, and 0.76, respectively. The results showed that machine learning methods from in vivo ultrasound measurements can be used as determinants of carcass tissue composition, resulting in reliable results.

Temperature-related excess mortality in German cities at 2 °C and higher degrees of global warming.

BACKGROUND: Investigating future changes in temperature-related mortality as a function of global mean temperature (GMT) rise allows for the evaluation of policy-relevant climate change targets. So far, only few studies have taken this approach, and, in particular, no such assessments exist for Germany, the most populated country of Europe. METHODS: We assess temperature-related mortality in 12 major German cities based on daily time-series of all-cause mortality and daily mean temperatures in the period 1993-2015, using distributed-lag non-linear models in a two-stage design. Resulting risk functions are applied to estimate excess mortality in terms of GMT rise relative to pre-industrial levels, assuming no change in demographics or population vulnerability. RESULTS: In the observational period, cold contributes stronger to temperature-related mortality than heat, with overall attributable fractions of 5.49% (95%CI: 3.82-7.19) and 0.81% (95%CI: 0.72-0.89), respectively. Future projections indicate that this pattern could be reversed under progressing global warming, with heat-related mortality starting to exceed cold-related mortality at 3 °C or higher GMT rise. Across cities, projected net increases in total temperature-related mortality were 0.45% (95%CI: -0.02-1.06) at 3 °C, 1.53% (95%CI: 0.96-2.06) at 4 °C, and 2.88% (95%CI: 1.60-4.10) at 5 °C, compared to today's warming level of 1 °C. By contrast, no significant difference was found between projected total temperature-related mortality at 2 °C versus 1 °C of GMT rise. CONCLUSIONS: Our results can inform current adaptation policies aimed at buffering the health risks from increased heat exposure under climate change. They also allow for the evaluation of global mitigation efforts in terms of local health benefits in some of Germany's most populated cities.

Predicting milk yield in Pelibuey ewes from the udder volume measurement with a simple method.

In this research communication we describe the creation of an equation for the prediction of milk yield (MY) from udder volume (UV). A total of 280 measurements were collected between 5 and 15 d postpartum (pp) from 36 multiparous Pelibuey ewes. Study variables were measured between 2 and 9 weeks pp and MY was measured by manual milking, UV prior to and following milking was measured using the technique of making moulds from aluminium foil. The MY ranged from 0.09 to 0.83 kg/d, meanwhile UV prior and following milking ranged from 155 to 1940 and 90 to 1520 cm3, respectively. Measurements of UV had a moderate to high (P < 0.01; 0.58 ≤ r ≤ 0.78) correlation with MY. The UV prior to milking was the best prediction model for MY, which explained 62% of the variation in MY. This equation presented moderate precision (r2 = 0.61) and high accuracy (bias correction factor = 0.94), confirming a good reproducibility index (concordance correlation coefficient = 0.73). Modelling efficiency (MEF = 0.59) showed moderate concordance between observed and predicted values. In conclusion, MY in lactating Pelibuey ewes could be predicted in a moderate way using the predictor variable UV measured with the technique of moulds made with aluminium foil.

Requirements of milk intake and intake of milk components for pre-weaning growth of Pelibuey lambs.

The objective of the present study was to determine the needs of milk intake (MI) and their components (i.e., lactose, fat, protein, and energy) to increase the average daily gain (ADG) of Pelibuey lambs during the pre-weaning period. Data of 62 lambs were used (35 males and 27 females; 32 of single lambing and 30 twins). All lambs were housed in individual pens with their mothers during the first 45 days post-lambing, without access to mother's food. Every 10 d, MI was estimated by the double lamb weighing method, and then, the intake of each milk component was calculated, considering both milk chemical composition and its estimated intake. The ADG was associated with MI and its components through a linear regression analysis. All milk variables were highly (P < 0.05; 0.69 ≤ r ≤ 0.85) correlated with ADG. The ADG explained (P < 0.0001) between 48 and 72% of the variation observed for MI and its components, being lower for fat intake and higher for MI. Given the positive slope parameter (ß1), an increase of one kilogram of body weight required intakes of 3.1 kg of milk, 117 g of fat, 203 g of protein, 187 g of lactose, or 16.8 MJ of energy. In conclusion, Pelibuey lambs to gain one kilogram of live weight during the pre-weaning period need to consume 3.1 kg of milk having an adequate amount of protein, fat, lactose, and energy.

Relationships among body condition score, FAMACHA© score and haematological parameters in Pelibuey ewes.

The objective of the present study was to determine the associations between the FAMACHA© score, body condition score (BCS) and several haematological parameters of Pelibuey ewes. In total, 52 clinically healthy Pelibuey ewes were evaluated in this study. A blood sample was collected from each animal to determine haematological variables. The BCS was evaluated on a scale of 1 to 5 and subsequently grouped into four categories: (1) < 1.5, (2) 2.0-2.5, (3) 3.0-3.5 and (4) > 4.0. The coloration of conjunctival mucosa was evaluated using the FAMACHA© system. The analysed haematological parameters were red blood cell (RBC) count, haemoglobin (HGB), haematocrit (HCT), mean corpuscular volume (MCV), mean corpuscular haemoglobin (MCH), mean corpuscular haemoglobin concentration (MCHC), red blood cell distribution width standard deviation (RDW-SD) and red blood cell distribution width coefficient of variation (RDW-CV). A descriptive statistical analysis was performed, and the Pearson correlation coefficients between variables were estimated. Haematological parameters were analysed with one-way analyses of variance, and the Tukey test was applied when significant differences were detected. The FAMACHA© score was only associated with MCHC (r = - 0.30, P < 0.05). The haematological parameters of RBC count, HCT, RDW-SD and RDW-CV statistically differed among BCS groups. The BCS was associated with RBC count, HCT, RDW-SD and RDW-CV, with r values ranging from 0.35 to 0.48. The haematological values were similar to those reported in ewes from Iran and Iraq. Hence, the optimization of the BCS calculation of Pelibuey ewes can generate association tables with haematological parameters to aid in the sanitary and feeding management of sheep.

Estimation of body weight in hair ewes using an indirect measurement method.

The aim of the present study was to develop and evaluate an equation to predict body weight (BW) using hip width (HW) in Pelibuey ewe lambs and ewes. Five hundred seventy-seven 2-month-old to 3-year-old, non-pregnant, non-lactating, clinically healthy ewe lambs and adult ewes with a mean BW of 34.7 ± 12.4 kg and HW of 15.6 ± 3.4 cm were considered. Three equations were evaluated: BW (kg): - 19.17 + 3.46 × HW (Eq. 1), BW (kg): - 17.79 + 3.25 × HW + 0.007 × HW2 (Eq. 2) and BW (kg): 0.39 × HW1.63 (Eq. 3). Independent data from 80 animals with similar characteristics (BW of 23.4 ± 10.9 kg and HW of 12 ± 3.1 cm) were also considered to evaluate the developed equations. The evaluation was based on the relationship between the observed and predicted values of BW analysed using a linear regression, the mean squared error of prediction (MSEP), the root MSEP (RMSEP) and the concordance correlation coefficients (CCCs). Additionally, cross-validation analyses were performed using the k-folds validation (k = 10) procedure. The correlation coefficient (r) between BW and HW was 0.94 (P < 0.001). The parameters for precision and accuracy showed that the proposed equations had high precision (R2 > 0.95%), accuracy (Cb > 0.98) and reproducibility (CCC > 0.96) in predicting the BW of ewe lambs and adult ewes. Equation (1) accurately predicted observed BW, with a bias (observed - predicted) of 4.3 kg and RMSEP of 9.68% with respect to the observed BW (random error of 84.23%); it also generated the best prediction according to the residual mean squared prediction error, coefficient of determination and mean absolute error. In conclusion, the highly correlated relationship between BW and HW in Pelibuey ewe lambs and adult ewes under humid tropic conditions enabled the development of mathematical models herein to estimate BW with an adequate goodness of fit. The linear model showed the best performance according to the goodness-of-fit evaluation and internal and external validation; hence, this model is proposed for use in both the experimental and commercial farms.

Modulation of PM2.5 in tropical South America by the upper-level atmospheric circulation and El Niño-Southern Oscillation phases.

In the Amazon basin, biomass burning has been identified as a major cause of poor regional air quality and the dominant source of particulate matter (PM). In this study, we analyse the impact of the upper-level jet on PM2.5 (PM with an aerodynamic diameter ≤ 2.5 µm) concentrations in tropical South America (SA) from December to February during the period 2003-2022. Furthermore, we investigate the response of air pollutants to the joint modulation by the upper-level jet and El Niño-Southern Oscillation (ENSO). We find that PM2.5 concentrations in north-eastern Brazil are reduced on days when the subtropical jet (STJ) is absent due to enhanced convection and precipitation over the region. This improvement in air quality is independent of the ENSO phase. Conversely, a prominent STJ inhibits convection and contributes to dry conditions that favour increased biomass burning and elevated pollutant concentrations. Furthermore, the co-occurrence of a prominent STJ with an El Niño phase acts synergistically to increase pollutant concentrations, as both reduce precipitation in north-eastern Brazil. In combination with La Niña, this upper-level pattern does not exert any modulation of the PM2.5 concentrations, as the wet conditions favoured by this ENSO phase prevail to reduce biomass burning.

Large-scale ozone episodes in Europe: Decreasing sizes in the last decades but diverging changes in the future.

Episodes of high near-surface ozone concentrations tend to cover large areas for several days. They are strongly dependent on meteorology, precursor emissions, and the ambient photochemical conditions. This study introduces a new pseudo-Lagrangian algorithm that identifies the spatiotemporal patterns of episodes, allowing for a good characterization of their areal extent and an assessment of their drivers. The algorithm has been used to identify ozone episodes in Europe from April to September over the last twenty years (2003-2022) in the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis as well as in the historical simulation (1950-2014) and four shared socio-economic pathways (SSPs, spanning 2015-2100) of three Earth system models (UKESM1-0-LL, EC-Earth3-AerChem and GFDL-ESM4). While the total number of episodes has increased in recent years, the frequency of large episodes has decreased following European precursor emission reductions. The analysis of the 100 largest episodes shows that they tend to occur in Northern Europe during spring and in the center and south of the continent from June onwards. Most of the top 10 episodes occurred in the first years of the century and were associated with high temperatures, enhanced solar radiation, and anticyclonic conditions. Despite the decrease in large episodes in recent years, there is uncertainty regarding future European episodes. Episodes of reduced size are found for SSPs with weak greenhouse forcing and low precursor emissions, whereas episode sizes increase in scenarios with high methane concentrations and enhanced radiative forcing, even exceeding the maximum historical size. However, the three models project episodes of different sizes for any given scenario, probably associated with their differing warming trends and the varying level of complexity in the implementation of processes. These results point to the need to implement both effective climate and air quality policies to address the ozone air pollution problem in Europe in a warming climate.

Anatomy and Physiology of Water Buffalo Mammary Glands: An Anatomofunctional Comparison with Dairy Cattle.

The present review aims to analyze the anatomical and physiological characteristics of the mammary gland and udders of water buffalo by making an anatomofunctional comparison with dairy cattle. It will also discuss the knowledge generated around the physiological regulation of milk ejection in the water buffalo. It was found that buffalo's average udder depth and width is approximately 20 cm smaller than Bos cattle. One of the main differences with dairy cattle is a longer teat canal length (around 8.25-11.56 cm), which highly influences buffalo milking. In this sense, a narrower teat canal (2.71 ± 0.10 cm) and thicker sphincter muscle are associated with needing higher vacuum levels when using machine milking in buffalo. Moreover, the predominant alveolar fraction of water buffalo storing 90-95% of the entire milk production is another element that can be related to the lower milk yields in buffalo (when compared to Bos cattle) and the requirements for prolonged prestimulation in this species. Considering the anatomical characteristics of water buffalo's udder could help improve bubaline dairy systems.

Prediction of Body Weight by Using PCA-Supported Gradient Boosting and Random Forest Algorithms in Water Buffaloes (Bubalus bubalis) Reared in South-Eastern Mexico.

This study aims to use advanced machine learning techniques supported by Principal Component Analysis (PCA) to estimate body weight (BW) in buffalos raised in southeastern Mexico and compare their performance. The first stage of the current study consists of body measurements and the process of determining the most informative variables using PCA, a dimension reduction method. This process reduces the data size by eliminating the complex structure of the model and provides a faster and more effective learning process. As a second stage, two separate prediction models were developed with Gradient Boosting and Random Forest algorithms, using the principal components obtained from the data set reduced by PCA. The performances of both models were compared using R2, RMSE and MAE metrics, and showed that the Gradient Boosting model achieved a better prediction performance with a higher R2 value and lower error rates than the Random Forest model. In conclusion, PCA-supported modeling applications can provide more reliable results, and the Gradient Boosting algorithm is superior to Random Forest in this context. The current study demonstrates the potential use of machine learning approaches in estimating body weight in water buffalos, and will support sustainable animal husbandry by contributing to decision making processes in the field of animal science.

Inclusion of chia seeds (Salvia hispanica L.) and pumpkin seeds (Cucurbita moschata) in dairy sheep diets.

Chia (Salvia hispanica L.) seed (CS) and Pumpkin (Cucurbita moschata) seed (PS) are used in ruminant diets as energy sources. The current experiment studied the impact of dietary inclusion of CS and PS on nutrient intake and digestibility, milk yield, and milk composition of dairy sheep. Twelve primiparous Texel × Suffolk ewes [70 ± 5 days in milk (DIM); 0.320 ± 0.029 kg milk yield] were distributed in a 4 × 3 Latin square design and fed either a butter-based control diet [CON; 13 g/kg dry matter] or two diets with 61 g/kg DM of either CS or PS. Dietary inclusion of CS and PS did not alter live weight (p >0.1) and DM intake (p >0.1). However, compared to the CON, dietary inclusion of both CS and PS increased the digestibility of neutral detergent fiber (p <0.001) and acid detergent lignin (p < 0.001). Milk production (p = 0.001), fat-corrected milk (p < 0.001), and feed efficiency (p < 0.001) were enhanced with PS, while the highest milk protein yield (p < 0.05) and lactose yield (p < 0.001) were for CS-fed ewes. Compared to the CON diet, the ingestion of either CS and/or PS decreased (p < 0.001) the C16:0 in milk. Moreover, both CS and PS tended to enhance the content of C18:3n6 (p > 0.05) and C18:3n3 (p > 0.05). Overall short-term feeding of CS and/or PS (up to 6.1% DM of diet) not only maintains the production performance and digestibility of nutrients but also positively modifies the milk FA composition.

Drought risk in Moldova under global warming and possible crop adaptation strategies.

This study analyzes the relationship between drought processes and crop yields in Moldova, together with the effects of possible future climate change on crops. The severity of drought is analyzed over time in Moldova using the Standard Precipitation Index, the Standardized Precipitation Evapotranspiration Index, and their relationship with crop yields. In addition, rainfall variability and its relationship with crop yields are examined using spectral analysis and squared wavelet coherence. Observed station data (1950-2020 and 1850-2020), ERA5 reanalysis data (1950-2020), and climate model simulations (period 1970-2100) are used. Crop yield data (maize, sunflower, grape), data from experimental plots (wheat), and the Enhanced Vegetation Index from Moderate Resolution Imaging Spectroradiometer satellites were also used. Results show that although the severity of meteorological droughts has decreased in the last 170 years, the impact of precipitation deficits on different crop yields has increased, concurrent with a sharp increase in temperature, which negatively affected crop yields. Annual crops are now more vulnerable to natural rainfall variability and, in years characterized by rainfall deficits, the possibility of reductions in crop yield increases due to sharp increases in temperature. Projections reveal a pessimistic outlook in the absence of adaptation, highlighting the urgency of developing new agricultural management strategies.

Shifting summer holidays in Spain as an adaptation measure to climate change.

This paper assesses whether moving summer holidays to the warmest period of the year in Spain could be a useful climate change adaptation strategy. While the most popular period for Spanish summer holidays has traditionally been August, we illustrate that the second half of July is the hottest period of the year and when the negative effects of high temperatures are most pronounced. If the holiday period in the second fortnight of August was moved to the second fortnight of July, some of the associated impacts would be mitigated due to the reduced anthropogenic activity during non-working days. In particular, we find a significant reduction in the annual peak of labour productivity loss (~25 %) and, to a lesser extent, of electricity demand and near-surface ozone concentrations (~3-4 %). Finally, we also show that global warming could lead to enhanced differences between both fortnights (even with no change in the seasonal cycle of temperature) because of the non-linear relationships between temperature and its impacts. Therefore, the positive effect of shifting holidays would be even larger in the coming future.

Thermal Balance in Male Water Buffaloes Transported by Long and Short Journeys.

Transport is a stressor that can cause physiological and metabolic imbalances in livestock, resulting in stress-induced hyperthermia. In water buffaloes, studies regarding the thermal state of animals during mobilization are scarce. Therefore, this study aimed to compare the thermal response of 1516 water buffaloes using infrared thermography (IRT) during 15 short trips (783 animals, 60,291 records, average duration = 50.33 min ± 5.48 min) and 14 long trips (733 animals, 56,441 records, average duration = 13.31 h ± 47.32 min). The surface temperature was assessed in 11 regions (periocular, lacrimal caruncle, nasal, lower eyelid, auricular, frontal-parietal, pelvic limb, torso, abdominal, lumbar, and thoracic) during seven phases from pasture to post-transport. It was found that the surface temperature of the periocular, lacrimal caruncle, nasal, auricular, frontal-parietal, pelvic limb, torso, abdominal, lumbar, and thoracic regions was significantly higher during SJs (+3 °C) when compared to LJs (p < 0.0001). In particular, the frontal-parietal region had a significant increase of 10 °C during the post-transport phase (p < 0.0001) in both groups, recording the highest temperatures during this phase. Likewise, a strong positive significant correlation between the different regions was found (r = 0.90, p < 0.0001). It is worth mentioning that the herding, loading, pre-, and post-transport phases were the ones where the greatest thermal response was recorded, possibly due to the influence of human interaction. Finally, a strong positive correlation (r above 0.9, p > 0.001) between the periocular, lacrimal caruncle, pinna, and pelvic limb was found. According to the results, SJ could be considered a stressful event that hinders thermal generation, contrarily to LJ.

The Challenge of Global Warming in Water Buffalo Farming: Physiological and Behavioral Aspects and Strategies to Face Heat Stress.

Water buffaloes have morphological and behavioral characteristics for efficient thermoregulation. However, their health, welfare, and productive performance can be affected by GW. The objective of this review was to analyze the adverse effects of GW on the productive behavior and health of water buffaloes. The physiological, morphological, and behavioral characteristics of the species were discussed to understand the impact of climate change and extreme meteorological events on buffaloes' thermoregulation. In addition, management strategies in buffalo farms, as well as the use of infrared thermography as a method to recognize heat stress in water buffaloes, were addressed. We concluded that heat stress causes a change in energy mobilization to restore animal homeostasis. Preventing hyperthermia limits the physiological, endocrine, and behavioral changes so that they return to thermoneutrality. The use of fans, sprinklers, foggers, and natural sources of water are appropriate additions to current buffalo facilities, and infrared thermography could be used to monitor the thermal states of water buffaloes.

DOCU-CLIM: A global documentary climate dataset for climate reconstructions.

Documentary climate data describe evidence of past climate arising from predominantly written historical documents such as diaries, chronicles, newspapers, or logbooks. Over the past decades, historians and climatologists have generated numerous document-based time series of local and regional climates. However, a global dataset of documentary climate time series has never been compiled, and documentary data are rarely used in large-scale climate reconstructions. Here, we present the first global multi-variable collection of documentary climate records. The dataset DOCU-CLIM comprises 621 time series (both published and hitherto unpublished) providing information on historical variations in temperature, precipitation, and wind regime. The series are evaluated by formulating proxy forward models (i.e., predicting the documentary observations from climate fields) in an overlapping period. Results show strong correlations, particularly for the temperature-sensitive series. Correlations are somewhat lower for precipitation-sensitive series. Overall, we ascribe considerable potential to documentary records as climate data, especially in regions and seasons not well represented by early instrumental data and palaeoclimate proxies.