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

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

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

The AgMIP Coordinated Climate-Crop Modeling Project (C3MP): Methods and Protocols

Climate change is expected to alter a multitude of factors important to agricultural systems, including pests, diseases, weeds, extreme climate events, water resources, soil degradation, and socio-economic pressures. Changes to carbon dioxide concentration ([CO2]), temperature, andwater (CTW) will be the primary drivers of change in crop growth and agricultural systems. Therefore, establishing the CTW-change sensitivity of crop yields is an urgent research need and warrants diverse methods of investigation. Crop models provide a biophysical, process-based tool to investigate crop responses across varying environmental conditions and farm management techniques, and have been applied in climate impact assessment by using a variety of methods (White et al., 2011, and references therein). However, there is a significant amount of divergence between various crop models’ responses to CTW changes (R¨otter et al., 2011). While the application of a site-based crop model is relatively simple, the coordination of such agricultural impact assessments on larger scales requires consistent and timely contributions from a large number of crop modelers, each time a new global climate model (GCM) scenario or downscaling technique is created. A coordinated, global effort to rapidly examine CTW sensitivity across multiple crops, crop models, and sites is needed to aid model development and enhance the assessment of climate impacts (Deser et al., 2012)..

Copper in the treatment of molybdenosis in the rat: determination of the dose of the antidote.

info:eu-repo/semantics/publishe

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 été considéré comme contrainte majeure au rendement des cultures dans en régions arides et semi arides. Cet articles decrit la caractérisation de la saison pluviométrique par la transpiration relative (Trel) de la culture de ma¿s dans 13 stations climatiques au Zimbabwe. Un modèle de balance sol-eau était utilisé pour simuler la transpiration relative pour la culture du ma¿s sur une durée de chaque saison de pluie pour évaluer sa qualité (séverité entre les saisons sèches). La Trel et la longueur de la période de croissance étaient soumises aux analyses de fréquence et les résultats étaient interpollés (kriging) pour former une base des données GIS des événements attendus des années normales aussi bien que humides que sèches. Le LGP normal (50% PR) variait à travers les stations, avec environ 75 jours, révélant des opportunités pour une gestion objective de la sélection variétale, afin de correspondre les cycles de croissance au LGP attendu. Le temps de série de la Trel a montré la variation dans le temps de la qualité de la saison des périodes de Trel élevée identifiant les parties de haute qualité de la saison pluvieuse appropriées à la production des cultures. La profondeur du sol a influencée la qualité de la saison, avec des qualités améliorant les sols les plus profonds. Un simple outil qui peut être utilisé pour produire ou pas des variétés de ma¿s d’un cycle particulier de longueur de croissance dans une région spécifiée pour des saisons à précipitation typiquement humide, normale ou sèche était développ

Canopy cover evolution, diurnal patterns and leaf area index relationships in a Mchare and Cavendish banana cultivar under different soil moisture regimes

Open Access Journal; Published online: 9 Jun 2020The biggest abiotic threat to banana (Musa spp.) production is water deficit, but physiological indicators in plantations are lacking. Canopy Cover (CC) seems to be a relevant parameter, but so far not used in banana fields. Field experiments were conducted to determine the effect of optimal irrigation (FI) versus rainfed (RF) on CC and Leaf Area Indices (LAI) in two experiments with different cultivars (Mchare ‘Huti Green’ [HG, AA] and Cavendish ‘Grand Naine’[GN, AAA]) (n = 3 for HG, n = 4 for GN) until harvest of cycle 1 (C1), studying C1 and C2 plants. Soil moisture was followed using Time Domain reflectometry. CC and LAI were reduced 8–9 weeks after the start of soil moisture divergence between RF and FI treatments in both experiments (p < 0.05), leading to a reduction in CC growth rate (r) and maximum CC (CCmax) in RF plots (p < 0.05). On a daily timescale, CC varied diurnally (i.e. was reduced) under high evaporative demands, whereby soil moisture depletion increased the CC reduction. Cultivar specific CC-LAI curves were created following the Lambert-Beer equation, whereby HG had a lower extinction coefficient than GN (0.52 vs. 0.67). CC growth over time seems a promising indicator for water deficit in the field. Diurnally, CC is more affected by evaporative demand than soil moisture depletion, although soil moisture depletion increases CC diurnal drops under high ET0