Use of the FAO AquaCrop model in developing sowing guidelines for rainfed maize in Zimbabwe

This paper presents a procedure in which the water-driven water productivity model AquaCrop was fine-tuned and validated for maize for the local conditions in Zimbabwe and then applied to develop sowing management options for decision support. Data from experiments of 2 seasons in Harare and from 5 other sites around Zimbabwe were used for the local calibration and validation of AquaCrop. Model parameters such as the reference harvest index (HIo); the canopy growth coefficient (CGC); early canopy decline and normalised biomass water productivity (WPb*) were adjusted during model calibration. Model performance was satisfactory after calibration with a Nash-Sutcliffe model efficiency parameter (EF = 0.81), RMSE = 15% and R2 = 0.86 upon validation. To develop sowing guidelines, historical climate series from 13 meteorological stations around Zimbabwe were used to simulate maize yield for 6 consecutive sowing dates determined according to criteria applicable in Zimbabwe. Three varieties and typical shallow and deep soil types were considered in the simulation scenarios. The simulated yield was analysed by an optimisation procedure to select the optimum sowing time that maximised long-term mean yield. Results showed that highest yields depended on the climate of the site (rainfall availability), variety (length of growing cycle) and soil depth (soil water storage capacity). The late variety gave higher mean yields for all sowing dates in the maize belt. Staggered sowing is recommended as a way of combating the effects of rainfall variability and as an answer to labour constraints.Keywords: biomass water productivity, AquaCrop, maize sowing dates, crop modellin

Relative transpiration as a decision tool in crop management: a case for rainfed maize in Zimbabwe

Water stress has been considered to be the primary constraint to yield in water-limited arid and semi-arid environments. This paper describes the characterisation of the rainfall season using relative transpiration (Trel) of a maize crop at 13 climate stations in Zimbabwe. A soil water balance model was used to simulate relative crop transpiration for a maize crop over the duration of each rainfall season to assess its quality (severity of intraseasonal dry spells). The Trel and length of growing period (LGP) were subjected to frequency analyses and the results were interpolated (kriging) to form a GIS library of expected events in normal, wet and dry years. The normal LGP (50% PE) varied across the stations, with a range of 75 days, exposing opportunities for objective management of variety selection to match crop growth cycles to expected LGP. The time series of Trel showed the time variation of quality of the season with periods of high Trel identifying the high quality parts of the rainfall season suitable for crop production. Soil depth influenced quality of the season, with deeper soils improving quality. A simple tool that can be used to indicate whether or not to grow maize varieties of particular length of growth cycle in a specified region for typical wet, normal or dry rainfall seasons was developed.Le stress hydrique a \ue9t\ue9 consid\ue9r\ue9 comme contrainte majeure au rendement des cultures dans en r\ue9gions arides et semi arides. Cet articles decrit la caract\ue9risation de la saison pluviom\ue9trique par la transpiration relative (Trel) de la culture de ma\ubfs dans 13 stations climatiques au Zimbabwe. Un mod\ue8le de balance sol-eau \ue9tait utilis\ue9 pour simuler la transpiration relative pour la culture du ma\ubfs sur une dur\ue9e de chaque saison de pluie pour \ue9valuer sa qualit\ue9 (s\ue9verit\ue9 entre les saisons s\ue8ches). La Trel et la longueur de la p\ue9riode de croissance \ue9taient soumises aux analyses de fr\ue9quence et les r\ue9sultats \ue9taient interpoll\ue9s (kriging) pour former une base des donn\ue9es GIS des \ue9v\ue9nements attendus des ann\ue9es normales aussi bien que humides que s\ue8ches. Le LGP normal (50% PR) variait \ue0 travers les stations, avec environ 75 jours, r\ue9v\ue9lant des opportunit\ue9s pour une gestion objective de la s\ue9lection vari\ue9tale, afin de correspondre les cycles de croissance au LGP attendu. Le temps de s\ue9rie de la Trel a montr\ue9 la variation dans le temps de la qualit\ue9 de la saison des p\ue9riodes de Trel \ue9lev\ue9e identifiant les parties de haute qualit\ue9 de la saison pluvieuse appropri\ue9es \ue0 la production des cultures. La profondeur du sol a influenc\ue9e la qualit\ue9 de la saison, avec des qualit\ue9s am\ue9liorant les sols les plus profonds. Un simple outil qui peut \ueatre utilis\ue9 pour produire ou pas des vari\ue9t\ue9s de ma\ubfs d\u2019un cycle particulier de longueur de croissance dans une r\ue9gion sp\ue9cifi\ue9e pour des saisons \ue0 pr\ue9cipitation typiquement humide, normale ou s\ue8che \ue9tait d\ue9velopp\ue

Derivation of irrigation requirements for radiological impact assessments

When assessing the radiological impacts of radioactive waste disposal, irrigation using groundwater contaminated with releases from the disposal system is a principal means of crop and soil contamination. In spite of their importance for radiological impact assessments, irrigation data are scarce and often associated with considerable uncertainty for several reasons including limited obligation to measure groundwater abstraction and differences in measuring methodologies. Further uncertainty arises from environmental (e.g. climate and landscape) change likely to occur during the assessment long time frame. In this paper, we derive irrigation data using the crop growth AquaCrop model relevant to a range of climates, soils and crops for use in radiological impact assessments. The AquaCrop estimates were compared with actual irrigation data reported in the literature and with estimates obtained from simple empirical methods proposed for use in radiological impact assessments. Further, the AquaCrop irrigation data were analysed using mixed effects modelling to investigate the effects of climate, soil and crop type on the irrigation requirement. Irrigation estimates from all models were within a reasonable range of the measured values. The AquaCrop estimates, however, were at the higher end of the range and higher than those from the empirical methods. Nevertheless, they may be more appropriate for conservative radiological assessments. The use of mixed effects modelling allowed for the characterisation of crop-specific variability in the irrigation data, and in contrast to the empirical methods, the AquaCrop and the mixed effects models accounted for the soil effect on the irrigation requirement. The approach presented in this paper is relevant for obtaining irrigation data for a specific site under different climatic conditions as well as for generic dose assessments. To the best of our knowledge, this is one of the most comprehensive analyses of irrigation data in the context of radiological impact assessment currently available

Improving the use of crop models for risk assessment and climate change adaptation

Crop models are used for an increasingly broad range of applications, with a commensurate proliferation of methods. Careful framing of research questions and development of targeted and appropriate methods are therefore increasingly important. In conjunction with the other authors in this special issue, we have developed a set of criteria for use of crop models in assessments of impacts, adaptation and risk. Our analysis drew on the other papers in this special issue, and on our experience in the UK Climate Change Risk Assessment 2017 and the MACSUR, AgMIP and ISIMIP projects. The criteria were used to assess how improvements could be made to the framing of climate change risks, and to outline the good practice and new developments that are needed to improve risk assessment. Key areas of good practice include: i. the development, running and documentation of crop models, with attention given to issues of spatial scale and complexity; ii. the methods used to form crop-climate ensembles, which can be based on model skill and/or spread; iii. the methods used to assess adaptation, which need broadening to account for technological development and to reflect the full range options available. The analysis highlights the limitations of focussing only on projections of future impacts and adaptation options using pre-determined time slices. Whilst this long-standing approach may remain an essential component of risk assessments, we identify three further key components: 1. Working with stakeholders to identify the timing of risks. What are the key vulnerabilities of food systems and what does crop-climate modelling tell us about when those systems are at risk? 2. Use of multiple methods that critically assess the use of climate model output and avoid any presumption that analyses should begin and end with gridded output. 3. Increasing transparency and inter-comparability in risk assessments. Whilst studies frequently produce ranges that quantify uncertainty, the assumptions underlying these ranges are not always clear. We suggest that the contingency of results upon assumptions is made explicit via a common uncertainty reporting format; and/or that studies are assessed against a set of criteria, such as those presented in this paper

Effects of initial aquifer conditions on economic benefits from groundwater conservation

Worldwide, there is growing recognition of the need to reduce agricultural groundwater use in response to rapid rates of aquifer depletion. To date, however, few studies have evaluated how benefits of conservation vary along an aquifer's depletion pathway. To address this question, we develop an integrated modeling framework that couples an agro-economic model of farmers' field-level irrigation decision-making with a borehole-scale groundwater flow model. Unique to this framework is the explicit consideration of the dynamic reductions in well yields that occur as an aquifer is depleted, and how these changes in intraseasonal groundwater supply affect farmers' ability to manage production risks caused by climate variability and, in particular, drought. For an illustrative case study in the High Plains region of the United States, we apply our model to analyze the value of groundwater conservation activities for different initial aquifer conditions. Our results demonstrate that there is a range of initial conditions for which reducing pumping will have long-term economic benefits for farmers by slowing reductions in well yields and prolonging the usable lifetime of an aquifer for high-value irrigated agriculture. In contrast, restrictions on pumping that are applied too early or too late will provide limited welfare benefits. We suggest, therefore, that there are ‘windows of opportunity’ to implement groundwater conservation, which will depend on complex feedbacks between local hydrology, climate, crop growth, and economics

Assessing the impact of climate change on wheat and sugarcane with the AquaCrop model along the Indus River Basin, Pakistan

Pakistan is among the most vulnerable regions to climate change impacts, in particular the agricultural areas found in the worlds' largest contiguous irrigation system, the Indus River Basin (IRB). This study assesses the impacts of two climate change scenarios (Representative Concentration Pathways-RCPs 4.5 and 8.5) on soil evaporation and transpiration rates, crop water productivity (CWP) and wheat and sugarcane yields over the 21st century, under two irrigation schedules (less/more frequent irrigation and higher/lower volume) for six locations along the Sindh and Punjab provinces. Maximum and minimum temperatures are projected to increase across the study area over the course of the 21st century. Additionally, precipitation is projected to increase (decrease) along the southernmost (northernmost) areas during the summer rainy season from June to September. To evaluate the crop-water productivity of wheat and sugarcane, we used the AquaCrop model in the six selected locations. For assessing the goodness of model validation and calibration, different statistical indicators are considered for comparing simulated and observed inter-annual yield variability (e.g. NRMSE of 12.4% and 12.1% for wheat and sugarcane, average values of the calibration and validation process). Our results show that wheat yields are likely to remain constant over time across the study areas, whereas sugarcane yields are expected to experience a decline in the Sindh province and an exponential increase in the Punjab province up to 2080, then yields will start to decline. In addition, our results reveal that both crops perform better, in terms of CWP, under low frequent irrigation and higher volumes of water. Overall, the findings of this work also support policymakers and project developers to implement adaptation strategies to cope with changing environmental conditions in a country where pressure on water resources is expected to continue to grow

Modelling impacts of climate change on arable crop diseases: progress, challenges and applications

Combining climate change, crop growth and crop disease models to predict impacts of climate change on crop diseases can guide planning of climate change adaptation strategies to ensure future food security. This review summarises recent developments in modelling climate change impacts on crop diseases, emphasises some major challenges and highlights recent trends. The use of multi-model ensembles in climate change modelling and crop modelling is contributing towards measures of uncertainty in climate change impact projections but other aspects of uncertainty remain largely unexplored. Impact assessments are still concentrated on few crops and few diseases but are beginning to investigate arable crop disease dynamics at the landscape level

Regional disparities in the beneficial effects of rising CO2 concentrations on crop water productivity

Rising atmospheric CO2 concentrations ([CO2]) are expected to enhance photosynthesis and reduce crop water use1. However, there is high uncertainty about the global implications of these effects for future crop production and agricultural water requirements under climate change. Here we combine results from networks of field experiments1, 2 and global crop models3 to present a spatially explicit global perspective on crop water productivity (CWP, the ratio of crop yield to evapotranspiration) for wheat, maize, rice and soybean under elevated [CO2] and associated climate change projected for a high-end greenhouse gas emissions scenario. We find CO2 effects increase global CWP by 10[0;47]%–27[7;37]% (median[interquartile range] across the model ensemble) by the 2080s depending on crop types, with particularly large increases in arid regions (by up to 48[25;56]% for rainfed wheat). If realized in the fields, the effects of elevated [CO2] could considerably mitigate global yield losses whilst reducing agricultural consumptive water use (4–17%). We identify regional disparities driven by differences in growing conditions across agro-ecosystems that could have implications for increasing food production without compromising water security. Finally, our results demonstrate the need to expand field experiments and encourage greater consistency in modelling the effects of rising [CO2] across crop and hydrological modelling communities

Bayesian Calibration of AquaCrop Model for Winter Wheat by Assimilating UAV Multi-Spectral Images

Crop growth model plays a paramount role in smart farming management, which not only provides quantitative information on crop development but also evaluates various management strategies. A reliable model is desirable but challenging due to the presence of unknown and uncertain parameters; therefore, crop model calibration is significant to achieve its potentials. This work is focused on the calibration of AquaCrop model by leveraging advanced Bayesian inference algorithms and UAV multi-spectral images at field scales. In particular, aerial images with high spatial- temporal resolutions are first applied to obtain Canopy Cover (CC) value by using machine learning based classification. The CC is then assimilated into AquaCrop model and uncertain parameters could be inferred by Markov Chain Monte Carlo (MCMC). Both simulation and experimental validation are performed. The experimental aerial images of winter wheat at Yangling district from Oct/2017 to June/2018 are applied to validate the proposed method against the conventional optimisation based approach by Simulated Annealing (SA). 100 Monte Carlo simulations show that the root mean squared error (RMSE) of Bayesian approach yields a smaller parameter estimation error than optimisation approach. While the experimental results show that: (i) a good wheat/background classification result is obtained for the accurate calculation of CC; (ii) the predicted CC values by Bayesian approach are consistent with measurements by 4-fold cross validation, where the RMSE is 0.0271 smaller than optimisation approach (0.0514); (iii) in addition to parameter estimation, their distribution information is also obtained in the developed Bayesian approach, reflecting the prediction confidence. It is believed that the Bayesian model calibration, although is developed for AquaCrop model, can find a wide range of applications to various simulation models in agriculture and forestry

The 2024 Europe report of the Lancet Countdown on health and climate change: unprecedented warming demands unprecedented action

Record-breaking temperatures were recorded across the globe in 2023. Without climate action, adverse climate-related health impacts are expected to worsen worldwide, affecting billions of people. Temperatures in Europe are warming at twice the rate of the global average, threatening the health of populations across the continent and leading to unnecessary loss of life. The Lancet Countdown in Europe was established in 2021, to assess the health profile of climate change aiming to stimulate European social and political will to implement rapid health-responsive climate mitigation and adaptation actions. In 2022, the collaboration published its indicator report, tracking progress on health and climate change via 33 indicators and across five domains. This new report tracks 42 indicators highlighting the negative impacts of climate change on human health, the delayed climate action of European countries, and the missed opportunities to protect or improve health with health-responsive climate action. The methods behind indicators presented in the 2022 report have been improved, and nine new indicators have been added, covering leishmaniasis, ticks, food security, health-care emissions, production and consumption-based emissions, clean energy investment, and scientific, political, and media engagement with climate and health. Considering that negative climate-related health impacts and the responsibility for climate change are not equal at the regional and global levels, this report also endeavours to reflect on aspects of inequality and justice by highlighting at-risk groups within Europe and Europe's responsibility for the climate crisis