The water footprint of a crop (WF) is a common metric for assessing agricultural water consumption and productivity. To provide an update and methodological enhancement of existing WF datasets, we apply a global process-based crop model to quantify consumptive WFs of 175 individual crops at a 5 arcminute resolution over the 1990–2019 period. This model simulates the daily crop growth and vertical water balance considering local environmental conditions, crop characteristics, and farm management. We partition WFs into green (water from precipitation) and blue (from irrigation or capillary rise), and differentiate between rainfed and irrigated production systems. The outputs include gridded datasets and national averages for unit water footprints (expressed in m³ t⁻¹ yr⁻¹), water footprints of production (m³ yr⁻¹), and crop water use (mm yr⁻¹). We compare our estimates to other global studies covering different historical periods and methodological approaches. Provided outputs can offer insights into spatial and temporal patterns of agricultural water consumption and serve as inputs for further virtual water trade studies, life cycle and water footprint assessments.

Crop water productivity is a key element of water and food security in the world and can be quantified by the water footprint (WF). Previous studies have looked at the spatially explicit distribution of crop WFs, but little is known about their temporal dynamics. Here, we present AquaCrop-Earth@lternatives (ACEA), a new process-based global gridded crop model that can simulate three consumptive WF components: green (WF_g), blue from irrigation (WF_bi), and blue from capillary rise (WF_bc). The model is applied to analyse global maize production in 1986–2016 at 5×5 arcmin spatial resolution. Our results show that over the 2012–2016 period, the global average unit WF of maize is 728.0 m³ t⁻¹ yr⁻¹ (91.2 % WF_g, 7.6 % WF_bi, and 1.2 % WF_bc), with values varying greatly around the world. Regions with high-input agriculture (e.g. Western Europe and Northern America) show small unit WFs and low interannual variability, while low-input regions show opposite outcomes (e.g. Middle and Eastern Africa). From 1986 to 2016, the global average unit WF reduced by a third, mainly due to the historical increase in maize yields. However, due to the rapid expansion of rainfed and irrigated areas, the global WF of maize production increased by half, peaking at 768.3×10⁹ m³ yr⁻¹ in 2016. As many regions still have a high potential in closing yield gaps, unit WFs are likely to reduce further. Simultaneously, humanity's rising demand for food and biofuels may further expand maize areas and hence increase WFs of production. Thus, it is important to address the sustainability and purpose of maize production, especially in those regions where it might endanger ecosystems and human livelihoods.

Crop production has the largest water footprint (WF) of all economic sectors and ranks as a leading cause of water scarcity. Despite this, our understanding of historical changes in global WFs of crops remains limited. In this study, we analyse the recently published dataset on green and blue WFs of 175 individual crops in 1990–2019. We explore the main changes in unit WFs (expressed in m³t⁻¹ yr⁻¹) and WFs of production (m³ yr⁻¹) and connect the observed changes to various physical and socio-economic drivers. We find that nearly 80% of crops reduced global average unit WFs (required less water per tonne) as crop yields improved and cultivation centred around more productive areas. However, the total WF of crop production increased by 30% as these productivity gains were insufficient to compensate for cropland expansion of mostly water-intensive crops. Close to 90% of the increase occurred between 2000–2019 likely driven by accelerated economic growth, globalisation, changing diets, and production of first-generation biofuels. Among crops, we observe the largest increases for oil palm fruit, soya beans, and maize as they became the main providers of crop-based nutrients, animal feed, and biofuels for the modern economy. Among regions, most of the increase occurred across the tropics, mainly in Indonesia, Brazil, and Nigeria. However, India, China, and the USA had the largest WFs of production over the study period. Humanity consumed 6.8 trillion m³ of water (87.1% green) to produce crops in 2019. This number is likely to increase in the future which may exacerbate already existing environmental and socio-economic issues. Thus, it is important to transition to more water-sustainable agrifood systems. Much potential still exists in increasing crop water productivity, shifting production to less water-scarce geographies, and rethinking our dietary and industrial consumption patterns.

We propose a methodology to estimate the yield response factor (i.e., the slope of the water-yield function) under local conditions for a given crop, weather, sowing date, and management at each growth stage using AquaCrop-OS. The methodology was applied to three crops (maize, sugar beet, and wheat) and four soil types (clay loam, loam, silty clay loam, and silty loam), considering three levels of bulk density: low, medium, and high. Yields are estimated for different weather and management scenarios using a problem-specific algorithm for optimal irrigation scheduling with limited water supply (GET-OPTIS). Our results show a good agreement between benchmarking (mathematical approach) and benchmark (estimated by AquaCrop-OS) using the Normalised Root Mean Square Error (NRMSE), allowing us to estimate reliable yield response factors ( $Ky$ ) under local conditions and to dispose of the typical simple mathematical approach, which estimates the yield reduction as a result of water scarcity at each growth stage.

There is a pressing need for transformative change, with a vision of long-term human well-being within planetary boundaries. The lack of progress—despite increasing awareness and action—illustrates how challenging it is to foster change in our complex global society. Education and learning are needed to enable change. Transdisciplinary learning, which meaningfully integrates diverse knowledge and perspectives, contributes to developing an integrative understanding—a necessity for tackling complex challenges. We explore how transdisciplinary learning for early-career researchers can foster transformative change and lead to increased biodiversity conservation. This paper focuses on a case study of the authors’ shared experiences during the 2021 Alternet Summer School, which focused on transformative change for biodiversity conservation and human well-being. In this introspective research, we gained insights through an online survey for participants and organizers of the summer school (n = 27). Using qualitative content analysis, we identify seven crucial elements of transdisciplinary learning which can lead to transformative change on (a) a personal level, as the learning process shifts values and helps researchers identify their roles; (b) a research level, by rethinking science and providing tools for transdisciplinary approaches, and (c) a societal level, by moving from the individual to the collective and constructing a shared vision for a sustainable future. Participants highlighted how changes on all these levels could benefit biodiversity conservation. These insights point to the benefit of transdisciplinary learning opportunities that empower young researchers to take up their part in fostering transformative change.