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1.
Glob Chang Biol ; 30(5): e17298, 2024 May.
Article in English | MEDLINE | ID: mdl-38712640

ABSTRACT

Diversified crop rotations have been suggested to reduce grain yield losses from the adverse climatic conditions increasingly common under climate change. Nevertheless, the potential for climate change adaptation of different crop rotational diversity (CRD) remains undetermined. We quantified how climatic conditions affect small grain and maize yields under different CRDs in 32 long-term (10-63 years) field experiments across Europe and North America. Species-diverse and functionally rich rotations more than compensated yield losses from anomalous warm conditions, long and warm dry spells, as well as from anomalous wet (for small grains) or dry (for maize) conditions. Adding a single functional group or crop species to monocultures counteracted yield losses from substantial changes in climatic conditions. The benefits of a further increase in CRD are comparable with those of improved climatic conditions. For instance, the maize yield benefits of adding three crop species to monocultures under detrimental climatic conditions exceeded the average yield of monocultures by up to 553 kg/ha under non-detrimental climatic conditions. Increased crop functional richness improved yields under high temperature, irrespective of precipitation. Conversely, yield benefits peaked at between two and four crop species in the rotation, depending on climatic conditions and crop, and declined at higher species diversity. Thus, crop species diversity could be adjusted to maximize yield benefits. Diversifying rotations with functionally distinct crops is an adaptation of cropping systems to global warming and changes in precipitation.


Subject(s)
Climate Change , Crops, Agricultural , Zea mays , Crops, Agricultural/growth & development , Zea mays/growth & development , North America , Europe , Edible Grain/growth & development , Agriculture/methods , Biodiversity , Crop Production/methods
2.
Sci Total Environ ; 493: 32-43, 2014 Sep 15.
Article in English | MEDLINE | ID: mdl-24937490

ABSTRACT

Poland, with its large drainage area, with 50% contribution of agricultural land and 45% contribution of population to overall agricultural land area and population number in the Baltic catchment, is the largest exporter of riverine nitrogen (N) and phosphorus (P) to the sea. The economic transition has resulted in substantial, statistically significant decline in N, P export from Polish territory to the Baltic Sea. Following the obligations arising from the Helsinki Commission (HELCOM) declarations, in the coming years, Poland is expected to reduce riverine N loads by ca. 25% and P loads by ca. 60% as referred to the average flow normalized loads recorded in 1997-2003. The aim of this paper is to estimate annual source apportioned N and P emissions into these river basins in 2015 and 2020 with application of modeling studies (MONERIS). Twelve scenarios, encompassing changes in anthropogenic (diffuse, point source) and natural pressure (precipitation, water outflow due to climate change), have been applied. Modeling outcome for the period 2003-2008 served as our reference material. In applied scenarios, N emission into the Oder basin in 2015 and 2020 shows an increase from 4.2% up to 9.1% as compared with the reference period. N emission into the Vistula basin is more variable and shows an increase by max. 17.8% or a decrease by max. 4.7%, depending on the scenario. The difference between N emission into the Oder and Vistula basins is related to the catchment peculiarities and handling of point sources emission. P emission into both basins shows identical scenario patters and a maximum decrease reaches 17.8% in the Oder and 16.7% in the Vistula basin. Despite a declining tendency in P loads in both rivers in all the scenarios, HELCOM targeted P load reduction is not feasible.


Subject(s)
Economic Development , Nitrogen/analysis , Phosphorus/analysis , Rivers/chemistry , Water Pollutants, Chemical/analysis , Environmental Monitoring , Poland , Water Pollution, Chemical/statistics & numerical data
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