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The meso-scale habitat simulation model MesoHABSIM21 was used to assess the impact of low-head HPPs on fish populations. MesoHABSIM is a physical habitat modelling system developed for e-flow assessment and river channel restoration planning. It describes the utility of instream habitat conditions for aquatic fauna, allowing to simulate change in habitat quality and quantity in response to alterations of flow and river hydromorphology. Meso-scale habitats are defined as geomorphic units (GUs, such as pools, riflles, rapids, glides22) that can be used by species and life stages for a significant part of their diurnal routine23. A meso-habitat can be considered suitable or optimal when the configuration of hydraulic patterns, together with the attributes that provide shelter, create favourable conditions for survival and development of animals. MesoHABSIM approach is based on the aggregation of three models24:
1.
A hydromorphological model that describes the spatial mosaic of fish-relevant hydro-morphological features.

2.
A biological model describing the relationship between the presence and abundance of fish and the physical environment of the river.

3.
A habitat model quantifying the amounts, frequency and duration of the available habitat depending on the flow regime and local river morphology.

For the modelling, the time series of daily water discharge data in natural and altered (downstream HPPs) conditions were created for wet, normal and dry years in order to describe the habitat suitability in all possible hydrological conditions. Conditional habitat suitability criteria (CHSC) were developed to define the relationship between fish distribution and physical environment. Physical spatial measurements of river hydraulic and fish shelter attributes (current velocity, depth, discharge, sediments, woody debris, boulders, etc.) were conducted on a scale of mesohabitat during field surveys. SimStream plugin of QGIS25 was used to organize collected data for mesohabitat modelling.
Hydrological data and hydromorphological surveys
The daily time series of discharge data of three water gauging stations (WGSs; Bartuva-Skuodas, Venta-Leckava and Mūša-Ustukiai) were taken from the hydrological yearbook of the Lithuanian Hydrometeorological Service for the periods of 1970–2000 (period before construction of HPPs) and of 2001–2015 (period after construction). The WGSs are located downstream the selected HPPs, and their data were used for the assessment of the altered discharge conditions and the impact of HPPs on fish communities. Two additional WGSs of Minija River-Kartena (for the Bartuva and Venta rivers) and Nemunėlis River-Tabokinė (for the Mūša River) were chosen for the restoration of natural conditions of river discharge at case study sites according to the analogy method26. The selection of a river analogue was based on the same hydrological region, similar catchment area, similarity in physico-geographical and hydrometeorological characteristics, and absence of anthropogenic structures which interrupt the continuity of the river, e.g. dams. The regression equation between case study river and river-analogue was prepared using daily water discharge data of 1970–2000 (period before construction of HPPs). The natural regime of investigated rivers after construction of HPPs (2001–2015) was restored using regression equations. In this way, we obtain the annual hydrographs of the investigated rivers in natural and altered conditions. In order to evaluate the habitat suitability in all possible hydrological conditions, hydrographs were prepared for wet, normal and dry hydrological years (probability of 5, 50 and 95%, respectively), according to average discharge data in the period of 2001–2015.
Four different discharge values (from minimal to average) were defined for hydromorphological measurements in each site of the selected river. These discharges represented the minimum, average and maximum low flow discharges of 30 consecutive days (Q30_min, Q30_ave, Q30_max) in the warm period (May–September), and multi-annual mean water discharge (Qannual_mean) in 1970–2000 (before HPPs construction). According to the Lithuanian law, environmental flow (Qenv) is defined at each HPP as 80% or 95% probability of the mean minimum discharge of 30 consecutive days of the warm period11. A Laser Rangefinder (distance, inclination, azimuthal measurements) connected via Bluetooth with the field tablet was used for the mapping of hydromorphological units (HMUs, also called mesohabitats). The maps of HMUs polygons were digitized in the .shp format using MapStream plugin of QGIS25,27. The length of an analysed river reach was defined as 20 times the mean river width28. The depth and flow velocity measurements in each defined HMU were done using a propeller-type flow meter mounted on a wading rod. Depending on the polygon area, from 5 to 30 measurements were carried out in each HMU, while the measurement density (point/m2) was kept as constant as possible in each case study considering its size (on average one point per 6 m2 in the Bartuva, 20 m2 in the Mūša and 25 m2 in the Venta rivers).
The presence/absence of fish shelters and vegetation were assessed visually (see21 for details). All measurements were carried out as close as it is possible to four defined discharges (minimum low flow (Q30_min), average low flow (Q30_ave), maximum low flow (Q30_max) and annual mean (Qannual_mean)) of each selected case study (Table 1).
Fish data and conditional habitat suitability criteria
Four Cyprinidae fish species, which are common in cyprinid-dominated lowland rivers of Lithuania20, but differ in rheophily and reproduction habitat were selected for the assessment of HPPs impact: lithophilic rheophilic schneider Alburnoides bipunctatus and dace Leuciscus leuciscus, phyto-lithophilic eurytopic roach Rutilus rutilus, and diadromous lithophilic eurytopic vimba Vimba vimba (fish guilds according to29). Based on the classification of fish species in European rivers according to their overall resistance to habitat degradation30, the selected species also represent different guilds of tolerance capacity: schneider is intolerant species, dace and vimba are intermediate, and roach is tolerant31. These four species are all benthopelagic, and in this respect they are similar, but due to their different preferences for rheophilic conditions, spawning habitat and overall habitat quality, it was expected that their response to changes in flow conditions should also be different. Currently access for diadromous vimba to most rivers is limited by dams; therefore, habitat availability for vimba was modelled only in the Venta River, which is still accessible for this species and contains its spawning grounds.
To define conditional habitat suitability criteria (CHSC)21, the river monitoring database for 2008–2015 was used. Data on the physical, chemical and hydrological characteristics of river sites was collected by the Lithuanian Environmental Protection Agency (EPA). Fish monitoring and assessment of hydromorphological characteristics of the site at the time of sampling was carried out by the Nature Research Centre under agreement with EPA. Standardized single-pass electric fishing took place in mid-July–September on river sections with a minimum length of at least 10 times the wetted width (but not less than 50 m) using backpack pulse current electrofisher (type IG200-2; HANS GRASSL GmbH) with a maximum output of 800 V and a maximum power of 10.0 kW per pulse.
For CHSC construction, only river sites in natural conditions (from good to high ecological status according to the European Water Framework Directive) with a catchment area of 100–5000 km2 and sampled by wading were selected from the database. In total, 245 river sites were selected. 160 sites in 75 rivers (2/3 of the selected sites) were randomly selected and used to build CHSC. The remaining 85 locations in 53 rivers (1/3 of all locations) were used for calibration. Once the locations were selected, their depth and current velocity were classified into intervals of 0.15 m and 0.15 m s-1 following the MesoHABSIM protocol (up to 0.15, 0.15–0.3, 0.3–0.45, etc.). The preference of schneider, dace and roach for depth and current velocity was determined by their frequency of occurrence in each of the intervals. In order to minimize the impact of random catches, species were considered present only when the number of individuals exceeded 25th percentile of the number of individuals in all places where they were found. Species were considered abundant when the number of individuals was greater than the median abundance in all places where they were found. A species was considered present in a particular interval of depth or current velocity only when its frequency of occurrence was  > 40%. Accordingly, a species was considered abundant only in those groups of depth and velocity where the number of individuals was greater than the median in more than 50% of the sites. The preference for the type of substrate and shelters was determined according to the analysis of these environmental variables in the river sites where the species should be present based on the criteria of depth and current velocity. According to the geomorphological and ecological definition of mesohabitat21,22, 10 m2 was considered the minimum surface that an HMU must have to be considered a suitable (species present) or optimal (species abundant) habitat for fish. When tested on an independent dataset (85 sites), CHSC were considered satisfactory for the presence of species when the species were present in  > 60% of the sites meeting the criteria (total accuracy  > 0.6). CHSC were considered satisfactory for the abundance of species when the species were present in  > 60% of the sites meeting the abundance criteria and the abundance of individuals was higher than the median in at least 50% of these sites.
CHSC for vimba were selected by an expert judgement, analysing common features of the river sites where this species was observed. Migration of vimba to the majority of former spawning grounds is currently restricted by dams. Therefore, this species is constantly found in a limited number of rivers, in which vimba is present not only during spawning in spring, but is also common in specific habitats in summer and autumn.
For the validation of CHSC for schneider, dace and roach, a single-pass electric fishing was performed in 42 HMUs of 4 natural rivers (Minija, Dubysa, Šventoji and Merkys), in river stretches with a length of 150–400 m, a maximum depth up to 1.5 m, and a catchment size of 315–3040 km2, during the low flow season, with high transparency of water. Fish were sampled by wading by a team of 3 persons using a backpack pulse current unit of a similar type as for fish monitoring (IG200-2D; HANS GRASSL GmbH). CHSC verification for vimba was carried out only in 14 out of 42 HMUs, since this species is constantly found in only one of the natural rivers selected for verification. A single-pass electric fishing was also conducted in all HMUs which were identified in the studied river stretches below HPPs at the low flow. Fish sampling was accomplished by wading and using pulse current backpack electric fishing gear. A single-pass electric fishing strategy was used, as the CHSC criteria were also developed based on single-pass sampling data. Studies show that in most cases species composition and rank abundance of common species do not change significantly after the first pass32,33,34.
To assess the predictive performance of CHSC, correctly classified instances, sensitivity, specificity, and true skill statistic were calculated based on confusion matrix analysis35.
Assessment of HPPs impact
The habitat area available for the species was modelled at different discharges of rivers. The impact of HPPs on habitat availability was assessed based on the comparison of the modelled available habitat area (i) at reference conditions during a dry year, (ii) under HPPs functioning in dry, normal and wet years, and (iii) at environmental Qenv. The flow value that exceeded 97% of the time at reference conditions (Q97)36 during a dry year and the corresponding area of species habitat (expressed in m2, hereafter, the minimum threshold area) were used as common denominators. Deviation of temporal availability of suitable habitats for modelled fish species due to HPPs functioning at different flows was assessed based on relative increase in the cumulative continuous duration of days when the area of the habitat falls below the minimum threshold values (hereafter, the stress days alteration; SDA). SDA analysis is based on the assumption that minimum habitat availability is a limiting factor for fish species, and events occurring rarely in nature create stress to aquatic fauna and shape the community. Therefore, for the selected minimum habitat threshold (expressed in m2), the number of habitat stress days that occur under those conditions was calculated and used as a benchmark for comparative analysis using the SDA metric, (see e.g.28,36,37 for details). Finally, we normalize SDA values between 0 and 1 by using the index of temporal habitat availability (ITH) as it is described by Rinaldi et al.28.
The relative abundance of fish species that are common in the cyprinid-dominated rivers of Lithuania (the frequency of occurrence in the natural river sites is  > 50%) was also compared in river reaches with natural (42 sites, 85 fishing occasions) and regulated (below HPPs; 20 sites, 39 fishing occasions) flows, which met at least good water quality criteria and fell within the same range of catchment size and slope as the rivers selected for modelling did. The sites were selected from the same river monitoring database for 2008–2015, which was used for selection of sites for CHSC development. The significance of identified differences was assessed using the Mann–Whitney U test. More

Ethical considerations
This study was carried out in accordance with the guidelines for ethical treatment of animals of the International Society of Applied Ethology. It was approved by the Institutional Animal Care and Use Committee of the Institute of Animal Science and the Czech Central Committee for Protection of Animals, Ministry of Agriculture (Permit Number 27356/2016-MZE-17214). Calves on the low-milk schedule received a milk allowance of approx. 12% of their body weight, equalling the traditional calf feeding practices26.
Animals and housing
The study was conducted at the Netluky research station of the Institute of Animal Science in Prague, Czech Republic. Data were collected from August 2016 until April 2017.
Seventy-two Holstein Friesian dairy calves (31 heifers and 41 bulls) were included in the study. Calves were separated from their dams at approximately 12 h after birth and housed individually either in outdoor hutches or in individual pens in a naturally ventilated open barn equipped with curtains. In both cases, the area available for each calf was 1.4 m × 1.4 m straw-bedded lying area and 1.2 m × 1.2 m solid walking area. While in individual housing, calves were fed 3 l of milk twice per day through teat-buckets at 06.00 and 18.00 and received concentrates and water ad libitum. Calves entered the experiment at an average age of 13.3 ± 3.1 days (mean ± S.D.) and were then housed in groups of three. Calves were allocated to groups balanced by sex, age and weight. Groups entered the experiment consecutively with 1–2 groups per week. Groups were housed in a naturally ventilated open barn with curtains. Group pens were 10.1 m2 consisting of a straw-bedded lying area (4.2 m × 1.4 m; approx. 2.0 m2 per calf) and a concrete walking and feeding area (3.5 m × 1.2 m). The group pens were covered with visual barriers in order to avoid direct visual contact of other calves. The visual barriers in front of the respective pens were removed in order to allow video recording; however, groups that were recorded simultaneously were allocated in the barn in a way that precluded visual contact without the front visual barriers of the pens. The calves received water, hay and concentrates ad libitum, offered in buckets and were provided with fresh straw bedding three times per week. All routine farm work was done before 10.00. Calves were hot-iron disbudded at 24.4 ± 3.1 days of age (mean ± S.D.). Disbudding wounds are painful for more than three weeks27, however no difference in play behaviour after disbudding was found after 27 hours23, thus we do not expect an effect of disbudding on play in our study. On the recording days, the air temperature in the barn ranged between -4.5 °C and 29 °C with the average (± S.D.) being 7.9 (± 8.6) °C.
Experimental design and procedures
Experimental design
Groups were allocated to treatments balanced by sex composition, age, weight and point of time entering the experiment. Milk allowance, group composition and number of groups assigned to each of the treatments are displayed in Fig. 1. Calves in all treatments received three milk meals per day at approximately 06.00, 12.00 and 18.00. All calves were offered 6 l of milk per day at the beginning of week 3. For UHigh and MHigh calves, the offered milk was gradually increased to 9 l of milk per day in week four and 12 l of milk per day in week six (Fig. 1). Therefore, the total milk amount offered from the start of the experiment until the end of week eight (42 days) was 240 l for ULow and MLow calves and 420 l for UHigh and MHigh calves. Milk was offered in teat buckets. Calves were tethered for the duration of the milk meal using neck collars and were released when all calves of the group had finished their meals (i.e. calves had either emptied the buckets or stopped drinking milk; approx. 5 min). If calves did not finish the offered milk meal, the volume of the remaining milk amount was measured. The volume of unconsumed milk was then summed from the point of entering the experiment until the respective day of behaviour recording. For ULow and MLow the total volume of unconsumed milk amounted to 0.3 ± 0.9 l (mean ± S.D.; median/interquartile range: 0/0 – 0). For UHigh and MHigh the total volume of unconsumed milk amounted to 16.7 ± 18.0 l (median/interquartile range: 10/2.5–25). The average daily amount of milk refusal was 0.1 ± 0.3 l and 0.3 ± 0.6 l, when calves were four weeks and eight weeks old, respectively.
Figure 1

Experimental design of treatments, group composition and milk allowance. Sample size is the number of calves included in statistical analysis.

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Data from two groups were excluded from statistical analysis: in one ULow group, a calf died from health issues unrelated to the experiment and one UHigh group was treated for severe diarrhoea for a prolonged time and therefore was not offered 12 l of milk in order to avoid further digestive problems.
Health and weight assessment
Calves’ health state was assessed once per week by two assessors. The following indicators of compromised health were recorded: diarrhoea, coughing/sneezing and increased respiratory rate (adapted from Gratzer, et al.28; Supplementary Table 1). The overall health score was set to 0 when calves showed no or one symptom of diarrhoea or coughing/sneezing and 1 when calves showed either combined diarrhoea and coughing/sneezing or increased respiratory rate. The ratio of calves with a health score of 1 is shown in Table 1.
Table 1 Ratio of calves with a health score of 1 by treatment and age group.
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Calves were weighed once per week between Monday and Thursday. To allow for comparison, daily weight gain for every respective week was calculated and weights were subsequently corrected for Monday as reference weighing day. Body weights from the start until the end of the experiment (three to eight weeks of age) are presented in Supplementary Figure S1. These data show that higher milk provision in MHigh and UHigh calves resulted in faster growth.
Quantification of play behaviour
Data recording
Locomotor play behaviour of calves was quantified through leg-attached accelerometers, using a previously validated method29. In this study, accelerometers were used to record running, turning and bucking/buck-kicking, as defined in Größbacher, et al.30. The data used to validate accelerometer recordings for these behaviours30 were a subset of the data used in this study. Accelerometers (HOBO Pendant G Acceleration Data Logger, Onset Computer Corporation, Pocasset, MA, USA; product specifications described in detail in Luu, et al.29) were attached to calves’ hind legs with elastic cohesive bandages. The accelerometers were oriented with the x-axis perpendicular to the ground. Acceleration was measured on the vertical axis at 1 Hz, i.e. with one measurement per second, from 05.00 until 23.04 on two consecutive days (Tuesday and Wednesday) when calves were four and eight weeks of age and recordings were stored on the device. Accelerometers were fitted to calves from the evening before until the morning after recording days, after being programmed with an optical infrared base station with USB interface and the HOBOware Pro Software (Version 3.7.8; Onset Computer Corporation, Pocasset, MA, USA).
Behaviour classification
Data processing was performed in SAS 9.4. Always 10 acceleration measurements, representing a period of 10 s each, were evaluated. These 10 s periods were categorized into lying, standing or play behaviour using quadratic discriminant analysis. This categorization was based on six predictor variables, which were calculated for each period with the respective 10 values: mean of two highest acceleration measurements, mean of two lowest acceleration measurements, variance, maximum of absolute value of change in acceleration measurement, mean change in acceleration measurements, and total sum of absolute values of change in acceleration measurements30.
In order to develop the discriminant function, a reference data set was created with randomly selected short sections of accelerometer data obtained from recordings of calves in this study. This reference data set consisted of 52 recordings with a mean (± S.D.) duration of 37.8 ± 16.8 min. Lying, standing and play behaviour were visually identified from video of these recordings applying one-zero-sampling of the respective 10 s periods, for which predictor variables were calculated. This was used as the gold standard.
The discriminant function was then applied to the entire data set in two steps to identify periods that contained locomotor play, i.e. included events of running, turning and/or bucking30, based on the six predictor variables: The first discriminant function classified the acceleration data into lying and standing, based on equal prior probabilities (50:50 chance of both behaviours occurring). The second discriminant function classified all standing-periods according to their presence or absence of locomotor play, based on prior probabilities of the reference data set (3:97 chance of play occurring across all treatments). The transitions from lying to standing and vice versa were almost always falsely classified as playing, as identified from video, and reclassified into standing.
The validation of processing the raw acceleration data was accomplished through checking the agreement between the acceleration-based method and visually identified play of the reference data set30. It proved that although the absolute play-levels were overestimated with the acceleration method, the method was able to truthfully quantify the inter-individual differences in locomotor play in dairy calves30.
Data analysis
Data processing
The last four minutes of each recording were omitted to obtain observation durations of exactly 18 h. The number of 10 s periods of locomotor play was converted into minutes of locomotor play per recording day (18 h). Recordings were excluded for the duration of disturbance when any calf in the barn escaped their pen or a person entered the pen. If more than 1 h was missing or compromised, the entire recording day was excluded for the calves affected. If less than 1 h of the recording was missing, locomotor play was calculated on a per hour basis and extrapolated to the ‘standard’ duration of 18 h. Out of 264 recordings (4 recordings per calf overall with 2 recordings at the age of four and eight weeks, respectively), 17 recordings were excluded or missing and in 13 recordings a mean (± S.D.) of 23.9 ± 13.0 min were missing and locomotor play duration and bout frequency were extrapolated. This resulted in 245 recordings, i.e. data points, included in the model. Play bouts were assessed by counting standalone periods of play, i.e. single 10 s periods not proceeded and not followed by periods classified as play were counted as one play bout, or by counting consecutive periods of play, i.e. two or more play periods occurred in a row as one play bout. Mean bout durations were assessed by recording the duration of each bout, e.g. a play bout consisting of one play period was recorded as 10 s and a play bout consisting of 3 play periods was recorded as 30 s.
Individual play was defined as one calf performing play in a 10-s-period when no other calf in the group was performing play. Dyadic play was defined as one calf performing play in the same 10-s-period as any one other calf of the group. Individual and dyadic play were calculated as minutes per recording day (18 h). Data were only included when observations of all three calves of the group were available. If data of one of the calves in the group were partially missing, observations of the other calves for the same period of time were excluded. Then the duration of individual and dyadic play was extrapolated on a per hour basis as described above. 237 recordings, i.e. data points, were included in the analysis, whereof 15 recordings were extrapolated.
The observed and randomly expected proportion of dyadic synchronized play were calculated on the basis of dyads, i.e. the combination of always two calves of a group, according to Šilerová, et al.31. Both were calculated for each pair combination:

$$ Sync_{obs} = frac{{left( {2*C_{sync} } right)}}{{left( {C_{A} + C_{B} } right)}} $$

$$ Sync_{exp} = frac{{(2* C_{A} *C_{B} *1/P_{dyad} )}}{{left( {C_{A} + C_{B} } right)}} $$

where Csync is the number of synchronous play periods of the pair, CA is the total number of play periods of calf A, CB is the total number of play periods of calf B and Pdyad is the total number of recorded periods for each dyad. The randomly expected proportion of play is the proportion of synchronized play occurring by chance if the calves played independently of each other31.
Triadic play was defined as all three calves of a group performing play in the same 10-s-period and calculated as minutes per recording day. Only periods in which data for all calves of the group were available were included. Extrapolation of play duration (due to partially missing data) was done in 10 out of 79 recordings.
Statistical analysis
All data was analysed in SAS Version 9.4. Five separate linear mixed effects models were run with total duration of play, frequency of play bouts, mean bout duration, duration of dyadic play or duration of individual play as dependent variables. Treatment (ULow, MLow, MHigh, UHigh), age (week four, week eight) and overall health score (0,1) were included as fixed class effects, while volume of unconsumed milk and maximum daily temperature were included as fixed quantitative effects, i.e. as covariates. Age (week), nested in calf and group were included as random effects. Furthermore, the date of recording was included as a crossed random effect. The same model was used for all dependent variables. Initially, the full model contained the interaction effect of treatment and age, however this was never significant and therefore removed from the model. An auto-regressive covariance structure was selected based on the Akaike Information Criterion (AIC). All models were visually inspected for normal distribution of residuals devoid of skewness. More

The growing interest in understanding the complex bovine udder microbiome has resulted in several published studies in recent years. These studies aimed to uncover how this microbiome influences the udder health and possibly has an important role during mastitis. In this study, we contribute to this knowledge by exploring the milk microbiota with a cross-sectional study of the milk microbiota as elucidated from over 400 quarter milk samples obtained from 60 lactating cows.
Sampling of udder milk for microbiome analysis is difficult and challenging15,16. In order to accomplish a more representative overview of the milk microbiota that colonize the upper interior part of the udder, the sampling method used in this study was different to those previously used to study milk microbiota2,6. The sampling was performed after the cow was regularly milked with the intent to remove microbial contaminations from the teat apex and to avoid the sampling of milk present in the cistern which might contain bacteria able to enter the udder between milking. The complete removal of contaminations from the teat apex or the environment cannot be ensured by the results obtained in this study, and additional experiments are needed to compare microbiota from milk samples obtained pre- and post-milking. However, although some taxa associated with the environment, such as Bacillaceae and Pseudomonadaceae, were detected in the dataset their abundance was much lower ( More

Rapeseed crops depend on pollinators such as bees.Credit: fotokostic/iStock/Getty

Farming trends deplete pollinators
Most cultivated crops depend on insect pollinators, such as bees, but global crop trends are leaving pollinators worse off.
Using data from the United Nations’ Food and Agriculture Organization, an international team, led by Marcelo Aizen at the National University of Comahue in Rio Negro, Argentina, assessed changes in the amount of land used for agriculture and the types of crops cultivated between 1961 and 2016. During that time, the area of land used to grow crops increased by around 40%, and pollinator-dependent cropland more than doubled. Soya bean, rapeseed and oil palm — crops associated with deforestation and diversity loss — account for much of the expansion and for the increase in pollinator dependence.

But although the land used has increased, crop diversity has remain largely the same since 2000. Producers have opted for large-scale cultivation of one crop. That’s a problem because monocultures don’t provide pollinators with a stable, year-round supply of food. This ultimately leads to a fall in insect numbers, lower yields and increased deforestation as demand for land surges.
Greater reliance on crops that are dependent on single-species pollinators, coupled with declining pollinator populations, could cause problems for food security. Poorer regions will be the hardest hit by crop failures, but higher-income countries that rely on imported food will also be affected.
Rotating a diverse range of crops on a single piece of land could help to stem the decline in pollinator populations. Planting native flowers and hedgerows on agricultural land and restoring neighbouring natural environments could also preserve pollinator habitats.
Glob. Change Biol. 25, 3516–3527 (2019)
US household food waste calculated
Working out how much food goes uneaten in an individual household is notoriously difficult. Comprehensive data on how much food ends up in the bin does not exist. But Yang Yu and Edward Jaenicke at Pennsylvania State University in University Park used a new method to overcome the lack of data.
Instead of trying to measure food waste directly, Yu and Jaenicke calculated a household’s ability to efficiently convert food brought into the household into the energy required to maintain the body weight of its residents. First, they obtained data on food purchases from around 4,000 households that took part in the 2012 US Department of Agriculture’s National Household Food Acquisition and Purchase Survey. The authors then calculated the metabolic energy requirements of the people living in each household from attributes such as height, weight, age and gender. The amount of food waste was estimated according to the difference between the household’s food inputs and its members’ energy requirements, not accounting for overeating.
The study showed that the average household wasted close to one-third of the food that it bought, which means that the United States wastes an estimated US$240 billion worth of food per year. The most efficient household in the study wasted about 9% of its food. Healthier diets created more waste than unhealthier diets, owing to the greater proportion of fruit and vegetables. Higher-income households wasted about 50% more food than lower-income households, and small households wasted more per person than large households.
Am. J. Agric. Econ. 102, 525–547 (2020)
Hidden hunger a global problem
There is more than enough food to feed the global population. But local patterns of production still leave 10% of the world’s people with insufficient calories, and more than half with inadequate quantities and variety of micronutrients — known as hidden hunger.
These are findings of a detailed analysis of food production by Ozge Geyik and colleagues at Deakin University in Burwood, Australia. The team gathered data on the nutrient content of 174 individual foods produced across 177 countries between 1995 and 2015. The researchers analysed whether individual countries and regions could meet the energy needs of their populations, as well as supply them with protein, iron, zinc, vitamin A, vitamin B12 and folate.
The study is one of the first to take such a detailed look at global patterns of nutrient production using disaggregated food data over time. Previous work has typically grouped foods into broad categories, such as cereals, dairy and vegetable oils, which can lead to under- or overestimates of specific nutrients.
Global food production increased steadily over the two decades, and outpaced increases in food requirements. However, on a regional level, the analysis found that more than half of the countries in Africa and Asia were not producing enough calories for their populations.
In 2015, more than 20% of the global population lived in countries with inadequate iron, vitamin A, vitamin B12 and folate production. Food production often fell short in multiple nutrients. More than 70% of countries with nutrient shortfalls produced inadequate amounts of iron, vitamin A and folate. And more than one-fifth of those not producing enough nutrients, fell short by more than half of what was necessary for their population.
The authors suggest that countries with nutrient deficiencies could prioritize the production of foods that contain the nutrients that their population needs. For example, in places where protein production is adequate, shifting production to protein sources that are higher in vitamin A and iron could alleviate these nutrient shortfalls. Adding micronutrients directly to soils and the leaves of crop plants is another possible solution.
Glob. Food Sec. 24, 100355 (2020)
Nutrient recycling possibilities mapped
The age-old practice of fertilizing crops with livestock manure has been reimagined in a study led by Sheri Spiegal from the US Department of Agriculture in Las Cruces, New Mexico. In the study, the team introduces the concept of a manureshed — land around livestock farms that could benefit from the nutrient-rich manure that those farms produce.
Spiegal and her colleagues mapped a patchwork of more than 3,000 counties across the United States. They classified counties as manure sources if they could supply nutrients in manure from livestock, or sinks if the crops grown could use the nutrients from manure.
The work reveals a surfeit of opportunity to recycle nutrients. The researchers identified counties that could recycle nitrogen and phosphorous nutrients at the local county level, as well as four regional manuresheds — in the northwest, southwest, central and southeast United States — where clusters of source counties could join together to develop sustainable redistribution programmes over longer distances. The work suggests a pathway towards removing manure from areas where it can pollute the local environment and delivering it to nutrient-poor agricultural lands, easing the reliance on commercial fertilizers that pollute the environment and deplete finite natural resources. But the authors note that further research — on how best to recover and transport manure, for instance — will be needed to turn the vision into a reality.
Agric. Syst. 182, 102813 (2020)
Intervention trade-offs assessed
Transforming the way land is managed and food is produced could shore up food supplies and address the challenges of climate change and biodiversity loss. But an assessment of proposed interventions reveals that few are up to the task of protecting both livelihoods and the environment.
Pamela McElwee from Rutgers University in New Brunswick, New Jersey, and her colleagues assessed the benefits and trade-offs of 40 proposed changes to land management, food-production chains and the management of environmental risks. The potential interventions are outlined in the 2019 report from the Intergovernmental Panel on Climate Change, and include improving management of livestock, reforestation, reducing consumer and retail food waste and management of urban sprawl.

The authors assessed each of the actions against the United Nations’ 17 Sustainable Development Goals (SDGs), as well as 18 measures from the Nature’s Contributions to People (NCP) framework, which was drawn up by scientists associated with the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services in 2017. This framework is intended to recognize nature’s social, cultural, spiritual and religious significance, as well as its role in providing food, clean water and healthy air.
The analysis revealed that several interventions carried unintended negative consequences. The production of bioenergy, either with or without carbon capture, planting forests and commercial crop insurance all had potentially negative consequences for both SDGs and NCPs. For example, bioenergy had large negative impacts on maintaining land biodiversity, freshwater quality and food production, despite providing affordable clean energy. About one-third of the interventions proposed had no substantial trade-offs. These included improving water management, increasing soil organic carbon content, reducing pollution, reducing post-harvest losses and fire management.
The analysis could help decision-makers to assess environmental or developmental policies to avoid unintended trade-offs, the authors say.
Glob. Change Biol. 26, 4691–4721 (2020) More

A farmer inspects her maize crop, grown using a ‘push–pull’ approach.Credit: The ‘Push–Pull’ Farming System: Climate-smart, sustainable agriculture for Africa/ICIPE/Green Ink Ltd UK

On paper, the global agriculture sector has done an admirable job of keeping pace with a growing population. According to the United Nations’ Food and Agriculture Organization, agricultural output per person has increased by 50% since 1960 — impressive, considering the number of mouths to feed has more than doubled.
But the reality is messier. Many people, including those in high-income nations, lack reliable access to nutritious food. And food security is an ongoing struggle for people in poorer regions. Even transient disruptions can have far-reaching consequences. One article1 described the global food supply as being “on a razor’s edge” — weather events or natural disasters in one part of the world can cause the price of grain everywhere to spike by more than 50%. “Globally, we have to increase food production by 60%, and in some areas we have to increase by 100%,” says P. V. Vara Prasad, a crop ecophysiologist at Kansas State University, Manhattan.

Over the past 50 years, producers increased agricultural output in much of the world through the ‘green revolution’. But this revolution has been environmentally harmful, relying heavily on chemical pesticides and fertilizers that have inflicted lasting damage on the soil and water supply. Natural biodiversity has been sacrificed to create vast monoculture fields. And in many low-income nations, survival depends on coaxing greater productivity from existing plots as more and more people scramble for limited resources, says Bernard Vanlauwe, a soil scientist based in Nairobi at the International Institute of Tropical Agriculture.
Many agricultural researchers are now looking to a set of practices known as sustainable intensification. The specifics vary depending on the setting, but a growing number of examples from around the world highlight the possibility of a second green revolution — one that might better live up to its name.
Many roads to sustainability
The concept of sustainable intensification was popularized2 in 1997 by Jules Pretty, an environmental scientist at the University of Essex in Colchester, UK. His goal was to challenge the idea that increasing yield is inherently incompatible with environmental health, with an agricultural philosophy that encompasses parameters such as biodiversity and water quality as well as the social and economic welfare of farmers. Researchers have defined the scope of sustainable intensification in different ways, but the big picture, says Pretty, entails recognizing that agriculture is inexorably connected with the environment and designing cultivation strategies accordingly. “Components of sustainable systems tend to be multifunctional,” he says. “You want a diverse system that provides support to pollinators, fixes nitrogen and provides a break against insects.” Advocates of sustainable intensification recognize that global agriculture can’t be reinvented in one fell swoop and that progress will come from incremental steps that improve efficiency, as well as more-dramatic measures that redesign the farming landscape.
Lucas Garibaldi, an agroecologist at the National University of Río Negro in Bariloche, Argentina, has focused on pollinators as a crucial component of what he calls ecological intensification. “Crop yield depends not only on the count of pollinators, but also on the biodiversity of pollinators,” says Garibaldi. “Millions of honeybees alone will not replace the function of diverse species of wild bees and butterflies and birds.” He notes that different bees pollinate different crops, but also allow more efficient pollination for some plant species. To create a haven for these airborne assistants, Garibaldi advocates minimizing pesticide use and including non-agricultural zones in farmland. These could be wild-plant borders that surround fields or just hedgerow-like strips of flowers that are appealing to the bees that traverse them.
Growing a mix of crops can have many benefits, including attracting pollinators. Conventional monoculture leaves soil exposed for much of the year, Garibaldi says. This creates opportunities for weeds to grow — necessitating herbicides — or leaves soil susceptible to erosion. With multiple crops or rotation throughout the year, more durable root systems that densely and extensively permeate the ground can be established, reinforcing the soil and preventing the nutrient depletion associated with long-term monoculture.

Crops rely on pollinators such as bees. Credit: Chris Gomersall/2020VISION/naturepl.com

Diversity can also eliminate the need for pesticides. Pretty says around 180,000 farmers in Kenya, Uganda and Tanzania now use push–pull cropping practices when growing maize. They plant grasses around the edges of maize plots that produce chemicals that ‘pull’ a common pest, the maize stalk borer (Busseola fusca), away from crops, while the maize itself attracts parasitic wasps that prey on the stalk borer. The farmers also intersperse legumes of the genus Desmodium with the maize that enrich the soil with nitrogen, and produce compounds that ‘push’ away pests and kill off a genus of invasive weed known as Striga.
Sustainable soil management is a thorny issue, particularly in resource-limited settings. Vanlauwe notes that nutrient depletion is one of the greatest threats to yield for African farmers, making a hard-line approach to sustainability unrealistic. “People who say you can trigger agricultural development in Africa without fertilizer do not have on the ground experience,” he says. But there are environmentally friendly ways to feed the soil. Jo Smith, a soil scientist at the University of Aberdeen, UK, has been equipping farmers in Africa and Asia with anaerobic digesters — simple systems that use microbes to convert animal manure into biogas for fuel and leave a nutrient-rich bioslurry. “It’s like giving them a little fertilizer factory — it gives you available ammonium that the crop can take up quickly,” she says. The biogas is also less harmful than conventional fuels, reducing household air pollution and improving quality of life, Smith adds.
Much of the world’s farming takes place on smallholder plots. One study3 estimated that one-third of the global food supply is produced on farms of less than two hectares. This fragmentation can make it challenging to introduce sustainable intensification practices. “Smallholder production systems are absolutely risk-averse,” says Vanlauwe. “Falling from earning US$100 to $50 a month can be the difference between being not-hungry and being hungry.”
Close collaboration with individual farmers is needed, but this is difficult to achieve at scale. Fortunately, smallholders are increasingly participating in collectives that can accelerate information sharing and reduce the risk associated with adopting new cultivation strategies. In August4, Pretty and his colleagues reported that, worldwide, around 8 million such groups have formed over the past two decades. “That’s about 240 million people working in collective-action efforts around areas like irrigation, forest management, pest management and water,” says Pretty. By partnering with these groups, researchers can design programmes that are more likely to be compatible with social, cultural and environmental conditions, and establish local networks of collaborators to facilitate the dissemination of information.
Some governments are also taking a more active role. Ethiopia, for example, has focused on aspects of ecological repair by establishing ‘exclosure’ areas for depleted soils. “Areas are fenced off, and after about ten years the land starts to recover,” Smith says.
In China, Fusuo Zhang, a plant-nutrition specialist at the China Agricultural University in Beijing, and his colleagues are working with government officials to mobilize an effort to help smallholder farmers across the nation transition to more evidence-based, sustainable cultivation. This includes selecting seed varieties that are suited to a given plot, using modelling techniques to guide planting based on levels of sunlight, water and nutrients, and optimizing the timing and density of seed planting. “We sent faculty members and groups of students to live among the farmers in the villages, and work with them to try to change their management,” says Zhengxia Dou, an agricultural scientist at the University of Pennsylvania in Philadelphia, who collaborated with Zhang’s team. By 2015, the effort had grown to include nearly 21 million farmers across China, who, on average, achieved a more than 10% boost in yield while using around 15% less fertilizer and reducing their greenhouse-gas output5.
Many farmers in India are embracing a national programme known as zero-budget natural farming (ZBNF). This cultivation strategy involves using soil microbes and mulch rather than synthetic fertilizers to enrich lands. Farmers in several Indian states are pursuing the approach, including around half a million farmers in Andhra Pradesh. But some scientists are concerned that the approach is untested and unproven. Last year, Panjab Singh, president of the National Academy of Agricultural Sciences in Delhi, told the newspaper The Hindu, “We are worried about the impact on farmers’ income, as well as food security.”

Smith concurs. “It was a political move, not a scientific move,” she says, adding that the natural farming approach has “not been properly trialled”. To assess the technique, she and her colleagues modelled the long-term impact of ZBNF on soil health. They found that the approach could meaningfully and sustainably improve nitrogen levels for low-yield lands, but that it would offer little benefit to farms already achieving high yields6. They concluded that a more targeted implementation of ZBNF is needed to protect overall national food security. Smith remains largely positive about ZBNF, which has been gaining momentum among farmers. “There’s a lot of good things about it, but it needs more science,” she says.
Outside national initiatives, smallholder sustainable intensive farming requires targeted investment and efforts to support social and economic stability. Vanlauwe contends that, in many parts of sub-Saharan Africa, environmental and political conditions mean that many farmers will continue to struggle at the margins for the foreseeable future. Still, he sees a path towards economic mobility. “Give them access to credit they pay back over time, and invest in integration and value-chains so they can get rid of or sell excess produce,” he says. “It’s about creating incentives and access systems.”
But durable change also requires building local expertise in crop and soil research, and in ecosystems. Many specialists in these areas are also involved with international education and training. For example, as director of the Feed the Future Innovation Lab for Collaborative Research on Sustainable Intensification, Prasad has helped to coordinate undergraduate- and graduate-level agriculture programmes in places such as Senegal, Cambodia and Bangladesh. Normally, these programmes take on a few dozen students at a time, but the shift to online training as a result of the coronavirus pandemic could prove to be a long-term gain for capacity building. “We are now talking to about 500 or even 1,000 students,” he says. More

Calculation of solar elevation and azimuth angles versus time
For our numerical calculations, the solar elevation angle θs(t) from the horizon and the solar azimuth angle αs(t) from south (axis y, Fig. 7A) were calculated as a function of time t with an algorithm based on a semi-analytical approximation (analytical Kepler’s orbits modified with astronomical perturbations) and the planetary theory VSOP 87 (Variations Séculaires des Orbites Planètaires) of Bretagnon and Francou30. This method is valid for the 1950–2050 period with an accuracy of 0.01°. Using this algorithm, we calculated the geocentric ecliptical, then the geocentric equatorial, and finally the geocentric horizontal coordinates of the Sun, resulting in the values of θs(t) and αs(t).
Diurnal cloudiness
Total cloud cover (TCC) time series of high temporal resolution (1 h) were evaluated for the period 01.01.2009–31.12.2018 from the ERA5 reanalysis of the European Centre for Medium-Range Weather Forecasts31. The geographic coverage is global with a native spatial resolution of 0.25° × 0.25° ≈ 27 km × 27 km. Climatological mean values of TCC were determined by averaging for each hour of each calendar day of every year in the vegetative period of sunflowers. Since TCC is a dimensionless relative parameter in the range 0–1 (0 is clear sky, 1 is overcast), the hourly climatological means are equivalent to the time-dependent probability 0 ≤ σ(t) ≤ 1 of cloudy situation. We determined the diurnal cloud probability function σ(t) in July, August and September in Boone County (Kentucky, USA, 39° N, − 84.75° E, Fig. 2A), central Italy (41.0° N, 15.0° E, Fig. 2B), central Hungary (47.0° N, 19.0° E, Fig. 2C), and south Sweden (58.0° North, 13.0° East, Fig. 2D). The cloudiness data used in our calculations correspond to the decade between 2009 and 2018. Because similar data are not readily available for the period when sunflowers were domesticated, we assume in this work that the data obtained in the last decade is historically representative. The validity of this assumption can be evaluated when paleo-climatological cloudiness data become available.
Measurement of the elevation angle of mature sunflower heads versus time
In a sunflower plantation at Budaörs (near Budapest), we measured the elevation angle θn of the normal vector of the mature head of the same 100 sunflowers as a function of time t, approximately weakly from 6 July to 11 September 2020. The studied sunflowers were individuals in a given row of the plantation.
Measurement of the absorption spectra of mature sunflower heads
The absorption spectra A(λ) of young (2 weeks after anthesis) and old (4 weeks after anthesis) inflorescence and back of mature sunflower heads were measured in the field with an Ocean Optics STS-VIS spectrometer (Ocean Insight, Largo, USA) in July 2020. Measurements were performed under total overcast conditions to ensure isotropic diffuse skylight illumination. At first, the reflection spectrum of the inflorescence/back was determined as follows: a spectrum was measured by directing the spectrometer’s head on the target at a distance of 5 cm, then another spectrum was registered by pointing the spectrometer to the overcast sky. In the laboratory these two spectra were divided by each other. Finally, assuming that all non-reflected light was absorbed, the absorption spectrum A(λ) = 1 − R(λ) was obtained by subtracting the reflection spectrum R(λ) from 1. Absorption spectra were measured for 3 sunflowers and then averaged.
Calculation of sky irradiance absorbed by a sunflower inflorescence
In the x–y-z reference frame of Fig. 7A, let the normal vector of a mature sunflower inflorescence be

$$underline {text{n}} = , left( {{text{cos}}theta_{{text{n}}} cdot {text{sin}}alpha_{{text{n}}} ,{text{ cos}}theta_{{text{n}}} cdot {text{cos}}alpha_{{text{n}}} ,{text{ sin}}theta_{{text{n}}} } right),$$
(2)

where axes x and y point to west and south, axis z points vertically upward, the elevation angle − 90° ≤ θn ≤  + 90° is measured from the horizontal (θn  > 0°: above the horizon, θn  More