Soil biological activity after a sixty-year fertilization practice in a wheat-maize crop rotation.

This study aimed to survey the long-term effects of fertilization practices on the functional diversity of the soil microbiota. A 60-year fertilization experiment with mineral fertilizers, farmyard manure and combined treatments was sampled in two consecutive years in maize (Zea mays L.) and wheat (Triticum aestivum L.

Modelling predicts that soybean is poised to dominate crop production across Africa.

The superior agronomic and human nutritional properties of grain legumes (pulses) make them an ideal foundation for future sustainable agriculture. Legume-based farming is particularly important in Africa, where small-scale agricultural systems dominate the food production landscape. Legumes provide an inexpensive source of protein and nutrients to African households as well as natural fertilization for the soil.

Diverging importance of drought stress for maize and winter wheat in Europe.

Understanding the drivers of yield levels under climate change is required to support adaptation planning and respond to changing production risks. This study uses an ensemble of crop models applied on a spatial grid to quantify the contributions of various climatic drivers to past yield variability in grain maize and winter wheat of European cropping systems (1984-2009) and drivers of climate change impacts to 2050. Results reveal that for the current genotypes and mix of irrigated and rainfed production, climate change would lead to yield losses for grain maize and gains for winter wheat.

Spatially explicit estimation of heat stress-related impacts of climate change on the milk production of dairy cows in the United Kingdom.

Dairy farming is one the most important sectors of United Kingdom (UK) agriculture. It faces major challenges due to climate change, which will have direct impacts on dairy cows as a result of heat stress. In the absence of adaptations, this could potentially lead to considerable milk loss.

Integrating Plant Science and Crop Modeling: Assessment of the Impact of Climate Change on Soybean and Maize Production.

Increasing global CO2 emissions have profound consequences for plant biology, not least because of direct influences on carbon gain. However, much remains uncertain regarding how our major crops will respond to a future high CO2 world. Crop model inter-comparison studies have identified large uncertainties and biases associated with climate change.

Neglecting legumes has compromised human health and sustainable food production.

The United Nations declared 2016 as the International Year of Pulses (grain legumes) under the banner 'nutritious seeds for a sustainable future'. A second green revolution is required to ensure food and nutritional security in the face of global climate change. Grain legumes provide an unparalleled solution to this problem because of their inherent capacity for symbiotic atmospheric nitrogen fixation, which provides economically sustainable advantages for farming.

Applications of the MVWG multivariable stochastic weather generator.

Weather generators (WG) became significant modules of crop models and decision support systems in the past decade. Using a large meteorological database from North America; two basic problems, related to the applicability of WGs in case of short or lacking data series, were investigated in the framework of the Multivariable weather generator (MVWG). First, the minimum data series length, required for adequate parameterization of the WG, was determined.

Simulation of soil temperature dynamics with models using different concepts.

This paper presents two soil temperature models with empirical and mechanistic concepts. At the test site (calcaric arenosol), meteorological parameters as well as soil moisture content and temperature at 5 different depths were measured in an experiment with 8 parcels realizing the combinations of the fertilized, nonfertilized, irrigated, nonirrigated treatments in two replicates. Leaf area dynamics was also monitored.

Improving the s-shape solar radiation estimation method for supporting crop models.

In line with the critical comments formulated in relation to the S-shape global solar radiation estimation method, the original formula was improved via a 5-step procedure. The improved method was compared to four-reference methods on a large North-American database. According to the investigated error indicators, the final 7-parameter S-shape method has the same or even better estimation efficiency than the original formula.