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• Over 60 y, the scope of phenological research published in AFM has evolved. • Agricultural applications were once dominant; climate change is now a recurring theme. • Review focuses on Phenocam, or phenology camera, papers published in AFM. • Documents history and evolution of the PhenoCam Network, and lessons learned. • Opportunities for machine learning/computer vision to advance Phenocam science. Over the last twenty years, phenology—the study of seasonal life cycle events—has emerged as a key subfield of global change biology. Phenology provides an integrated measure of the organismal response to climate change and is a key driver of the functional responses of ecosystems to climate change. Since I established the PhenoCam Network in 2008, over 200 papers have been published using Phenocam technology, and these papers have added to our understanding of phenology as both an indicator of climate variability and change and a key aspect of ecosystem function. This review examines: (1) the changing phenological research landscape, as represented by phenology-themed papers in Agricultural and Forest Meteorology (AFM), over the last 60 y; (2) the contributions of phenocams and the PhenoCam Network, as reported in the pages of AFM, to the study of phenology; and (3) the lessons I have learned from developing this grassroots effort, and how other researchers might benefit from the PhenoCam Network's successes and failures. Key conclusions to emerge from this review include: (1) the enormous, value-added power of research networks; (2) the importance of both interpersonal relationships and serendipity, in the metamorphosis of ideas into results; and (3) the potential for open, freely-available data to be transformative, in ways that cut across disciplinary, socioeconomic, and demographic barriers. Finally, the development of the PhenoCam Network has been a collaborative, multidisciplinary experiment in team science, and the commitment of my team members and the enthusiasm of my collaborators have been critical to the success of these efforts. [ABSTRACT FROM AUTHOR]

Abstract: In recent years various papers have been published on the practical use of infrared thermometry and thermal imaging to estimate leaf stomatal resistance [(amongst others: Jones, H.G., 1999a,b; Leinonen et al., 2006). Use of infrared thermometry for estimation of stomatal conductance as a possible aid to irrigation scheduling. Agricultural and Forest Meteorology 95, 139–149; Jones, H.G., 1999b. Use of thermography for quantitative studies of spatial and temporal variation of stomatal conductance over leaf surfaces. Plant Cell and Environment 22, 1043–1055; Leinonen, I., Grant, O.M., Tagliavia, C.P.P., Chaves, M.M., Jones, H.G., 2006. Estimating stomatal conductance with thermal imagery. Plant, Cell & Environment 29, 1508–1518]. These papers are based on the physical relationships linking the stomatal resistance of a leaf to its own temperature and to the temperatures of reference leaves (dry and wet). These relationships differ depending on the type of leaf (amphistomatous or with stomata on only one side) and on the way the reference leaves are wetted (on either both sides or on one side only). Because of ambiguous assumptions on the wetting process and small inaccuracies in the calculation of some resistances, these basic relationships are not clear. The present paper aims to clarify and synthesize the appropriate equations for each case. [Copyright &y& Elsevier]

Abstract: Solar radiation (R s), a very important variable in agricultural meteorology, is measured at a very limited number of meteorological stations worldwide. However, a number of methods are reported in the literature for estimating R s from routinely measured air temperature extremes, the accuracy of which needs to be tested. R s was thus estimated by different methods for 29 stations distributed throughout India. The methods compared were Hargreaves [Hargreaves, G.H., 1994. Simplified coefficients for estimating monthly solar radiation in North America and Europe. Dept. Paper, Dept. Biol. and Irrig. Eng., Utah State Univ., Logan, Utah], Annandale et al. [Annandale, J.G., Jovanovic, N.Z., Benadé, N., Allen, R.G., 2002. Software for missing data error analysis of Penman–Monteith reference evapotranspiration. Irrig. Sci. 21, 57–67], Allen [Allen, R.G., 1995. Evaluation of procedures for estimating mean monthly solar radiation from air temperature. Rep., Food and Agriculture Organization of the United Nations (FAO), Rome, Italy], Samani [Samani, Z., 2000. Estimating solar radiation and evapotranspiration using minimum climatological data. J. Irrig. Drain. Eng., ASCE, 126(4), 265–267], Allen [Allen, R.G., 1997. Self-calibrating method for estimating solar radiation from air temperature. J. Hydrol. Eng., ASCE, 2 (2), 56–67], and Bristow and Campbell [Bristow, K.L., Campbell, G.S., 1984. On the relationship between incoming solar radiation and daily maximum and minimum temperature. Agric. Forest Meteorol., 31, 159–166]. The estimated R s values were then compared to measured R s (or R s estimated from measured sunshine hours with locally calibrated Ångström coefficients), to check the suitability of these methods under Indian conditions. Three statistical indicators were used for comparing the performances of different methods, namely, modelling efficiency (ME), coefficient of residual mass (CRM), and root mean squared error (RMSE) expressed as a percentage of the arithmetic mean of observed values. To understand the effect of R s estimation on reference evapotranspiration, ET0, the internationally accepted FAO-Irrigation and Drainage Paper 56 Penman–Monteith based method (Allen et al. [Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration—guidelines for computing crop water requirements. Irrig. and Drain. Paper 56, Food and Agriculture Organization of the United Nations (FAO), Rome, Italy]), employed using both measured R s and estimated R s, were also compared. The methods performed differently for different stations. In general, the original Hargreaves methods, both with [Annandale, J.G., Jovanovic, N.Z., Benadé, N., Allen, R.G., 2002. Software for missing data error analysis of Penman–Monteith reference evapotranspiration. Irrig. Sci. 21, 57–67] and without [Hargreaves, G.H., 1994. Simplified coefficients for estimating monthly solar radiation in North America and Europe. Dept. Paper, Dept. Biol. and Irrig. Eng., Utah State Univ., Logan, Utah] altitude correction, performed better. The choice of method to obtain R s estimates was found to have a relatively small effect on the resulting ET0 estimates. In conclusion, under Indian conditions where no R s measurements are available, the R s estimation methods from air temperature extremes were ranked as (in order of descending suitability): Hargreaves [Hargreaves, G.H., 1994. Simplified coefficients for estimating monthly solar radiation in North America and Europe. Dept. Paper, Dept. Biol. and Irrig. Eng., Utah State Univ., Logan, Utah]>Annandale et al. [Annandale, J.G., Jovanovic, N.Z., Benadé, N., Allen, R.G., 2002. Software for missing data error analysis of Penman–Monteith reference evapotranspiration. Irrig. Sci. 21, 57–67]>Samani [Samani, Z., 2000. Estimating solar radiation and evapotranspiration using minimum climatological data. J. Irrig. Drain. Eng., ASCE, 126(4), 265–267]>Allen [Allen, R.G., 1995. Evaluation of procedures for estimating mean monthly solar radiation from air temperature. Rep., Food and Agriculture Organization of the United Nations (FAO), Rome, Italy]>Allen [Allen, R.G., 1997. Self-calibrating method for estimating solar radiation from air temperature. J. Hydrol. Eng., ASCE, 2 (2), 56–67]>Bristow and Campbell [Bristow, K.L., Campbell, G.S. 1984. On the relationship between incoming solar radiation and daily maximum and minimum temperature. Agric. Forest Meteorol., 31, 159–166], for estimates of monthly means. [Copyright &y& Elsevier]

The development of methodologies for predicting crop yield, in real-time and in response to different agro-climatic conditions, could help to improve the farm management decision process by providing an analysis of expected yields in relation to the costs of investment in particular practices. Based on the use of crop models, this paper compares the ability of two methodologies to predict wheat yield ( Triticum aestivum L.), one based on stochastically generated climatic data and the other on mean climate data. It was shown that the numerical experimental yield distribution could be considered as a log-normal distribution. This function is representative of the overall model behaviour. The lack of statistical differences between the numerical realisations and the logistic curve showed in turn that the Generalised Central Limit Theorem (GCLT) was applicable to our case study. In addition, the predictions obtained using both climatic inputs were found to be similar at the inter and intra-annual time-steps, with the root mean square and normalised deviation values below an acceptable level of 10% in 90% of the climatic situations. The predictive observed lead-times were also similar for both approaches. Given (i) the mathematical formulation of crop models, (ii) the applicability of the CLT and GLTC to the climatic inputs and model outputs, respectively, and (iii) the equivalence of the predictive abilities, it could be concluded that the two methodologies were equally valid in terms of yield prediction. These observations indicated that the Convergence in Law Theorem was applicable in this case study. For purely predictive purposes, the findings favoured an algorithm based on a mean climate approach, which needed far less time (by 300-fold) to run and converge on same predictive lead time than the stochastic approach. [ABSTRACT FROM AUTHOR]

Abstract: This study introduces four methods for determining turbulent water vapour and carbon dioxide flux densities, the evapotranspiration and CO2 flux respectively. These methods combine scintillometer measurements with point-sampling measurements of scalar quantities and consequently have a faster statistical convergence than the eddy-covariance method. The scintillometer measures the friction velocity and stability averaged over space, allowing the time averaging to be a minute or less in homogenous conditions. This paper aims to thoroughly test the methods by analysing their sensitivity to the variables that go into the method and validate the methods with 30-min eddy-covariance data. Introduced are: the Bowen-variance method, the flux-variance method, the structure-parameter method, and the energy-balance method. Sensitivity analysis shows that each method is sensitive to the turbulence measurements of the scalar quantities that are specific to the method, as well as to the friction velocity. This demonstrates that the accuracy of the flux results from a correct representation of the turbulence variables used by the methods. Furthermore, a 30-min flux validation shows that the methods compare well to the independent eddy-covariance fluxes. We found that the structure-parameter method performs best – a low scatter (the correlation coefficient, r =0.99) and a 5% underestimation were observed. Also the other methods perform well, although the energy-balance did not close, because storage terms and CO2 flux were neglected. Furthermore, during the night the variance methods were influenced by non-stationarity in the measurement signal. Finally, we suggest using the correlation coefficients between temperature and scalar quantities to acquire the sign of the fluxes. Data for this study were gathered in May–June 2009 over a wheat field near Merken, Germany, in the framework of the TransRegio32 program. [Copyright &y& Elsevier]

Abstract: Crop models often simulate drought impacts with full and no irrigation scenarios, while planners are more interested in whether the current available irrigation water can cope with the future more serious droughts. This paper addresses a key constraint common to modeling studies: the limited representation of actual irrigation water supply. We present a data-driven approach to identify a benchmark for agronomic drought risk levels as defined by water availability thresholds at the baseline climate (1980–2008) using reported crop yields, climate and irrigation statistics. Then, holding the current irrigation supplies, we adopted Bayesian formula to estimate magnitude of the future water availability and the associated probability of crops yields being decreased to rainfall-deficiency under climate conditions in 2030s (2020–2040) based on the RegCM3 climate model output driven by IPCC SRES A1B scenario. Results reveal that future drought stress would overwhelm the irrigation capacity of current supplies in northern and western China, while drought remains at baseline climate levels in the central, eastern and southern regions. The largest increases in the probability of projected drought risk were in northeast and southwest, ranging from 14% to 28% above baseline climate. Regional drought impacts for grain self sufficiency are discussed. [Copyright &y& Elsevier]

• Conclusions in Zeng et al (2020) AFM Vol 290, 15 August 2020 are debatable • Afforestation does not cause increase in surface water areas in northern China • Rise in precipitation and building water storages increases surface water areas The paper "Large-scale afforestation significantly increases permanent surface water in China's vegetation restoration regions, Agricultural and Forest Meteorology, Volume 290, 15 August 2020, 108001" by Zeng et al. (2020) finds that northern China is greening up and that "vegetation cover is an important factor in controlling permanent water changes". They suggest that afforestation partially caused the increase in surface water areas due to the "significant positive correlations between forest covers and surface water area". The authors suggest that, except precipitation, "climatic factors were not the main factor influencing permanent water". They attribute the increase in area of surface waterbody to the increase in dams, precipitation rise, and afforestation. This commentary aims at clarifying concepts of afforestation-water yield-river flow relations and offers an alternative explanation of the observed expansion of surface water areas in northern China. Using a simple water balance-based approach, we conduct a back-of-envelope calculation and show that afforestation and 'greening up' are not likely to cause an increase in water yield and surface water storage. We argue that the detected rise of permanent surface water changes in the study regions is a result of hydraulic infrastructure construction, urbanization, and increase in precipitation, perhaps not vegetation recovery from afforestation. We believe that large-scale afforestation is not likely to increase surface water resources in northern China as implied in Zeng et al (2020). Future process-based studies are needed to understand the sources of the local precipitation and the effects of revegetation on precipitation, soil improvement, and water yield. [ABSTRACT FROM AUTHOR]