Yield gap analysis in dairy production systems using the mechanistic model LiGAPS-Dairy.

The difference between the theoretical maximum (potential) production and the actual production realized by farmers is referred to as the yield gap. The objectives of this study are to develop a mechanistic model for dairy cows that allows yield gap analysis in dairy production systems and to evaluate model performance. We extended and adapted an existing model for beef cattle to dairy cattle, and the new model was named Livestock simulator for Generic analysis of Animal Production Systems-Dairy cattle (LiGAPS-Dairy). Milk production and growth of an individual cow over its entire lifespan were described as a function of the animal's genotype, the ambient climate, feed quality, and available feed quantity. The model was parameterized for Holstein-Friesian cows. After calibration, we evaluated model performance by comparing simulated results and measured results from experimental farms in the Netherlands, which were not used for model calibration. Cows were permanently housed in stables, where the diet consisted of predetermined amounts of concentrates and ad libitum high-quality roughage. The mean absolute error (MAE) for simulated milk production per lactation was 12% of the measured milk production, whereas the MAE for simulated daily milk yields was 19%. The MAE for simulated feed intake per lactation was 10% of the measured feed intake, whereas the MAE for simulated daily feed intake was 19%. The average yield gap for dairy cows was 11% of the potential milk production (YP). Yield gap analysis indicated that for experimental farms in the Netherlands, the difference between YP and feed quality limited milk production (YL) of 1,009 kg fat- and protein-corrected milk was mainly explained by feed intake capacity (33%), protein deficiency (25%), cow weight at the start of experiments (23%), and heat stress (19%). The LiGAPS-Dairy model also indicated the periods during lactation in which these factors affected milk production. In our opinion, the overall model performance is acceptable for permanently housed cows under Dutch conditions. The model needs to be evaluated further for other production systems, countries and breeds. Thereafter, LiGAPS-Dairy can be used for yield gap analysis and exploration of options to increase resource use efficiency in dairy production.

Impacts of intensifying or expanding cereal cropping in sub-Saharan Africa on greenhouse gas emissions and food security.

Cropping is responsible for substantial emissions of greenhouse gasses (GHGs) worldwide through the use of fertilizers and through expansion of agricultural land and associated carbon losses. Especially in sub-Saharan Africa (SSA), GHG emissions from these processes might increase steeply in coming decades, due to tripling demand for food until 2050 to match the steep population growth. This study assesses the impact of achieving cereal self-sufficiency by the year 2050 for 10 SSA countries on GHG emissions related to different scenarios of increasing cereal production, ranging from intensifying production to agricultural area expansion. We also assessed different nutrient management variants in the intensification. Our analysis revealed that irrespective of intensification or extensification, GHG emissions of the 10 countries jointly are at least 50% higher in 2050 than in 2015. Intensification will come, depending on the nutrient use efficiency achieved, with large increases in nutrient inputs and associated GHG emissions. However, matching food demand through conversion of forest and grasslands to cereal area likely results in much higher GHG emissions. Moreover, many countries lack enough suitable land for cereal expansion to match food demand. In addition, we analysed the uncertainty in our GHG estimates and found that it is caused primarily by uncertainty in the IPCC Tier 1 coefficient for direct N2 O emissions, and by the agronomic nitrogen use efficiency (N-AE). In conclusion, intensification scenarios are clearly superior to expansion scenarios in terms of climate change mitigation, but only if current N-AE is increased to levels commonly achieved in, for example, the United States, and which have been demonstrated to be feasible in some locations in SSA. As such, intensifying cereal production with good agronomy and nutrient management is essential to moderate inevitable increases in GHG emissions. Sustainably increasing crop production in SSA is therefore a daunting challenge in the coming decades.

Can sub-Saharan Africa feed itself?

Although global food demand is expected to increase 60% by 2050 compared with 2005/2007, the rise will be much greater in sub-Saharan Africa (SSA). Indeed, SSA is the region at greatest food security risk because by 2050 its population will increase 2.5-fold and demand for cereals approximately triple, whereas current levels of cereal consumption already depend on substantial imports. At issue is whether SSA can meet this vast increase in cereal demand without greater reliance on cereal imports or major expansion of agricultural area and associated biodiversity loss and greenhouse gas emissions. Recent studies indicate that the global increase in food demand by 2050 can be met through closing the gap between current farm yield and yield potential on existing cropland. Here, however, we estimate it will not be feasible to meet future SSA cereal demand on existing production area by yield gap closure alone. Our agronomically robust yield gap analysis for 10 countries in SSA using location-specific data and a spatial upscaling approach reveals that, in addition to yield gap closure, other more complex and uncertain components of intensification are also needed, i.e., increasing cropping intensity (the number of crops grown per 12 mo on the same field) and sustainable expansion of irrigated production area. If intensification is not successful and massive cropland land expansion is to be avoided, SSA will depend much more on imports of cereals than it does today.

Water productivity of rainfed maize and wheat: A local to global perspective.

Water productivity (WP) is a robust benchmark for crop production in relation to available water supply across spatial scales. Quantifying water-limited potential (WPw) and actual on-farm (WPa) WP to estimate WP gaps is an essential first step to identify the most sensitive factors influencing production capacity with limited water supply. This study combines local weather, soil, and agronomic data, and crop modeling in a spatial framework to determine WPw and WPa at local and regional levels for rainfed cropping systems in 17 (maize) and 18 (wheat) major grain-producing countries representing a wide range of cropping systems, from intensive, high-yield maize in north America and wheat in west Europe to low-input, low-yield maize systems in sub-Saharan Africa and south Asia. WP was calculated as the quotient of either water-limited yield potential or actual yield, and simulated crop evapotranspiration. Estimated WPw upper limits compared well with maximum WP reported for field-grown crops. However, there was large WPw variation across regions with different climate and soil (CV = 29% for maize and 27% for wheat), which cautions against the use of generic WPw benchmarks and highlights the need for region-specific WPw. Differences in simulated evaporative demand, crop evapotranspiration after flowering, soil evaporation, and intensity of water stress around flowering collectively explained two thirds of the variation in WPw. Average WP gaps were 13 (maize) and 10 (wheat) kg ha-1 mm-1, equivalent to about half of their respective WPw. We found that non-water related factors (i.e., management deficiencies, biotic and abiotic stresses, and their interactions) constrained yield more than water supply in ca. half of the regions. These findings highlight the opportunity to produce more food with same amount of water, provided limiting factors other than water supply can be identified and alleviated with improved management practices. Our study provides a consistent protocol for estimating WP at local to regional scale, which can be used to understand WP gaps and their mitigation.

Residual soil phosphorus as the missing piece in the global phosphorus crisis puzzle.

Phosphorus (P) is a finite and dwindling resource. Debate focuses on current production and use of phosphate rock rather than on the amounts of P required in the future to feed the world. We applied a two-pool soil P model to reproduce historical continental crop P uptake as a function of P inputs from fertilizer and manure and to estimate P requirements for crop production in 2050. The key feature is the consideration of the role of residual soil P in crop production. Model simulations closely fit historical P uptake for all continents. Cumulative inputs of P fertilizer and manure for the period 1965-2007 in Europe (1,115 kg . ha(-1) of cropland) grossly exceeded the cumulative P uptake by crops (360 kg ha(-1)). Since the 1980s in much of Europe, P application rates have been reduced, and uptake continues to increase due to the supply of plant-available P from residual soil P pool. We estimate that between 2008 and 2050 a global cumulative P application of 700-790 kg . ha(-1) of cropland (in total 1,070-1,200 teragrams P) is required to achieve crop production according to the various Millennium Ecosystem Assessment scenarios [Alcamo J, Van Vuuren D, Cramer W (2006) Ecosystems and Human Well-Being: Scenarios, Vol 2, pp 279-354]. We estimate that average global P fertilizer use must change from the current 17.8 to 16.8-20.8 teragrams per year in 2050, which is up to 50% less than other estimates in the literature that ignore the role of residual soil P.

Intensifying rice production to reduce imports and land conversion in Africa.

Africa produces around 60% of the rice the continent consumes, relying heavily on rice imports to fulfill the rest of the domestic demand. Over the past 10 years, the rice-agricultural area increased nearly 40%, while average yield remained stagnant. Here we used a process-based crop simulation modelling approach combined with local weather, soil, and management datasets to evaluate the potential to increase rice production on existing cropland area in Africa and assess cropland expansion and rice imports by year 2050 for different scenarios of yield intensification. We find that Africa can avoid further increases in rice imports, and even reduce them, through a combination of cropland expansion following the historical trend together with closure of the current exploitable yield gap by half or more. Without substantial increase in rice yields, meeting future rice demand will require larger rice imports and/or land conversion than now.

Daily bias-corrected weather data and daily simulated growth data of maize, millet, sorghum, and wheat in the changing climate of sub-Saharan Africa.

Crop models are the primary means by which agricultural scientists assess climate change impacts on crop production. Site-based and high-quality weather and climate data is essential for agronomically and physiologically sound crop simulations under historical and future climate scenarios. Here, we describe a bias-corrected dataset of daily agro-meteorological data for 109 reference weather stations distributed across key production areas of maize, millet, sorghum, and wheat in ten sub-Saharan African countries. The dataset leverages extensive ground observations from the Global Yield Gap Atlas (GYGA), an existing climate change projections dataset from the Inter-Sectoral Model Intercomparison Project (ISIMIP), and a calibrated crop simulation model (the WOrld FOod Studies -WOFOST). The weather data were bias-corrected using the delta method, which is widely used in climate change impact studies. The bias-corrected dataset encompasses daily values of maximum and minimum temperature, precipitation rate, and global radiation obtained from five models participating in the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6), as well as simulated daily growth variables for the four crops. The data covers three periods: historical (1995-2014), 2030 (2020-2039), and 2050 (2040-2059). The simulation of daily growth dynamics for maize, millet, sorghum, and wheat growth was performed using the daily weather data and the WOFOST crop model, under potential and water-limited potential conditions. The crop simulation outputs were evaluated using national agronomic expertise. The presented datasets, including the weather dataset and daily simulated crop growth outputs, hold substantial potential for further use in the investigation of future climate change impacts in sub-Saharan Africa. The daily weather data can be used as an input into other modelling frameworks for crops or other sectors (e.g., hydrology). The weather and crop growth data can provide key insights about agro-meteorological conditions and water-limited crop output to inform adaptation priorities and benchmark (gridded) crop simulations. Finally, the weather and simulated growth data can also be used for training machine learning techniques for extrapolation purposes.

Interventions to increase circularity and reduce environmental impacts in food systems.

Applying specific circularity interventions to the food system may have environmental benefits. Using an iterative linear food system optimisation model (FOODSOM), we assess how changes in human diets, imports and exports, and the utilisation of waste streams impact land use and greenhouse gas emissions (GHG). After including these circularity principles, land use and GHG emissions were on average 40% and 68% lower than in the current food system, primarily driven by a reduction in production volumes and a shift towards feeding the domestic population. Shifting from the current diet to a circular diet decreased land use with 43% and GHG emissions with 52%. Allowing up to half of each nutrient in the human diet to be imported, while balancing imports with equal exports in terms of nitrogen, phosphorus and potassium, also decreased land use (up to 34%) and GHG emissions (up to 26%) compared to no imported food. Our findings show that circularity interventions should not be implemented mutually exclusively; by combining a circular diet with imported food and fully utilising waste streams, the lowest land use and GHG emissions can be realised.

Recoupling livestock and feed production in the Netherlands to reduce environmental impacts.

In many places on earth, livestock and feed production are decoupled, as feed is grown in one region and fed to livestock in another. This disrupts nutrient cycles by depleting resources in feed producing regions and accumulating resources in livestock areas, which leads to environmental degradation. One solution is to recouple livestock and feed production at a more local level, which enhances nutrient circularity. Recoupling livestock and feed production creates a natural ceiling for livestock numbers based on the feed producing capacity of a region. In this study we assess the consequences of recoupling livestock and feed production (i.e., by avoiding the import and export of animal feed) on ammonia and greenhouse gas (GHG) emissions, with and without feed-food competition. To this end, we used FOODSOM, an agro-ecological food system optimisation model representing the Dutch food system in this study. The Netherlands is one example of a region with high livestock densities and resource accumulation. We found that recoupling decreased livestock numbers (beef cattle: -100 %; dairy cattle: -29 %; broiler chickens: -57 %; laying hens: -67 %; pigs: -62 %; sheep -100 %) and animal-sourced food exports (-59 %) while still meeting the current human diet in the Netherlands. Consequently, ammonia emissions and GHG emissions decreased, and the nitrogen use efficiency increased from 31 % to 38 % at the food systems level. Recoupling alone was almost sufficient to meet national emission targets. Fully meeting these targets required further small changes in livestock numbers. Avoiding feed-food competition decreased livestock productivity and GHG emissions but did not improve nitrogen use efficiency. Total meat production could not meet domestic consumption levels while avoiding feed-food competition, and resulted in additional beef cattle. We show that recoupling livestock and feed production is a promising next step to enhance circularity while decreasing agricultures environmental impact.

Global wheat production could benefit from closing the genetic yield gap.

Global food security requires food production to be increased in the coming decades. The closure of any existing genetic yield gap (Yig) by genetic improvement could increase crop yield potential and global production. Here we estimated present global wheat Yig, covering all wheat-growing environments and major producers, by optimizing local wheat cultivars using the wheat model Sirius. The estimated mean global Yig was 51%, implying that global wheat production could benefit greatly from exploiting the untapped global Yig through the use of optimal cultivar designs, utilization of the vast variation available in wheat genetic resources, application of modern advanced breeding tools, and continuous improvements of crop and soil management.

Circularity in animal production requires a change in the EAT-Lancet diet in Europe.

It is not known whether dietary guidelines proposing a limited intake of animal protein are compatible with the adoption of circular food systems. Using a resource-allocation model, we compared the effects of circularity on the supply of animal-source nutrients in Europe with the nutritional requirements of the EAT-Lancet reference diet. We found the two to be compatible in terms of total animal-source proteins but not specific animal-source foods; in particular, the EAT-Lancet guidelines recommend larger quantities of poultry meat over beef and pork, while a circular food system produces mainly milk, dairy-beef and pork. Compared with the EAT-Lancet reference diet, greenhouse gas emissions were reduced by up to 31% and arable land use reduced by up to 42%. Careful consideration of the feasible substitutability between animal-source foods is needed to define potential roles of animal products in circular human diets.

Spatial frameworks for robust estimation of yield gaps.

Food security interventions and policies need reliable estimates of crop production and the scope to enhance production on existing cropland. Here we assess the performance of two widely used 'top-down' gridded frameworks (Global Agro-ecological Zones and Agricultural Model Intercomparison and Improvement Project) versus an alternative 'bottom-up' approach (Global Yield Gap Atlas). The Global Yield Gap Atlas estimates extra production potential locally for a number of sites representing major breadbaskets and then upscales the results to larger spatial scales. We find that estimates from top-down frameworks are alarmingly unlikely, with estimated potential production being lower than current farm production at some locations. The consequences of using these coarse estimates to predict food security are illustrated by an example for sub-Saharan Africa, where using different approaches would lead to different prognoses about future cereal self-sufficiency. Our study shows that foresight about food security and associated agriculture research priority setting based on yield potential and yield gaps derived from top-down approaches are subject to a high degree of uncertainty and would benefit from incorporating estimates from bottom-up approaches.

Principles, drivers and opportunities of a circular bioeconomy.

A circular, bio-based economy could provide the pathway to a sustainable future. Here we present five ecological principles to guide biomass use towards a circular bioeconomy: safeguarding and regenerating the health of our (agro)ecosystems; avoiding non-essential products and the waste of essential ones; prioritizing biomass streams for basic human needs; utilizing and recycling by-products of (agro)ecosystems; and using renewable energy while minimizing overall energy use. Implementing these principles calls for a transformation of our current economic system, including fundamental changes to policies, technologies, organizations, social behaviour and markets.

A generic bio-economic farm model for environmental and economic assessment of agricultural systems.

Bio-economic farm models are tools to evaluate ex-post or to assess ex-ante the impact of policy and technology change on agriculture, economics and environment. Recently, various BEFMs have been developed, often for one purpose or location, but hardly any of these models are re-used later for other purposes or locations. The Farm System Simulator (FSSIM) provides a generic framework enabling the application of BEFMs under various situations and for different purposes (generating supply response functions and detailed regional or farm type assessments). FSSIM is set up as a component-based framework with components representing farmer objectives, risk, calibration, policies, current activities, alternative activities and different types of activities (e.g., annual and perennial cropping and livestock). The generic nature of FSSIM is evaluated using five criteria by examining its applications. FSSIM has been applied for different climate zones and soil types (criterion 1) and to a range of different farm types (criterion 2) with different specializations, intensities and sizes. In most applications FSSIM has been used to assess the effects of policy changes and in two applications to assess the impact of technological innovations (criterion 3). In the various applications, different data sources, level of detail (e.g., criterion 4) and model configurations have been used. FSSIM has been linked to an economic and several biophysical models (criterion 5). The model is available for applications to other conditions and research issues, and it is open to be further tested and to be extended with new components, indicators or linkages to other models.

Balancing indicators for sustainable intensification of crop production at field and river basin levels.

Adequate tools for evaluating sustainable intensification (SI) of crop production for agro-hydrological system are not readily available. Building on existing concepts, we propose a framework for evaluating SI at the field and river basin levels. The framework serves as a means to assess and visualise SI indicator values, including yield, water-use efficiency and nitrogen-use efficiency (NUE), alongside water and nitrogen surpluses and their effects on water quantity and quality. To demonstrate the SI assessment framework, we used empirical data for both the field level (the Static Fertilization Experiment at Bad Lauchstädt) and the river basin level (the Selke basin, 463 km2) in central Germany. Crop yield and resource use efficiency varied considerably from 1980 to 2014, but without clear trends. NUE frequently fell below the desirable range (<50%), exposing the environment to a large N surplus (>80 kg N ha-1). For the catchment as a whole, the average nitrate-N concentration (3.6 mg L-1) was slightly higher than the threshold of 2.5 mg L-1 nitrate-N in surface water. However, weather and climate-related patterns, due to their effects on transport capacity and dilution, influenced water quantity and quality indicators more than agronomic practices. To achieve SI of crop production in the Selke basin, irrigation and soil moisture management are required to reduce yield variability and reduce N surpluses at field level. In addition, optimum application of fertiliser and manure could help to reduce the nitrate-N concentration below the set water quality standards in the Selke basin. In this way, there is scope for increase in yields and resource use efficiencies, and thus potential reduction of environmental impacts at basin level. We conclude that the framework is useful for assessing sustainable production, by simultaneously considering objectives related to crop production, resource-use efficiency and environmental quality, at both field and river basin levels.

Beyond the plot: technology extrapolation domains for scaling out agronomic science.

Ensuring an adequate food supply in systems that protect environmental quality and conserve natural resources requires productive and resource-efficient cropping systems on existing farmland. Meeting this challenge will be difficult without a robust spatial framework that facilitates rapid evaluation and scaling-out of currently available and emerging technologies. Here we develop a global spatial framework to delineate 'technology extrapolation domains' based on key climate and soil factors that govern crop yields and yield stability in rainfed crop production. The proposed framework adequately represents the spatial pattern of crop yields and stability when evaluated over the data-rich US Corn Belt. It also facilitates evaluation of cropping system performance across continents, which can improve efficiency of agricultural research that seeks to intensify production on existing farmland. Populating this biophysical spatial framework with appropriate socio-economic attributes provides the potential to amplify the return on investments in agricultural research and development by improving the effectiveness of research prioritization and impact assessment.

Intercropping with wheat lowers nutrient uptake and biomass accumulation of maize, but increases photosynthetic rate of the ear leaf.

Intercropping is an ancient agricultural practice that provides a possible pathway for sustainable increases in crop yields. Here, we determine how competition with wheat affects nutrient uptake (nitrogen and phosphorus) and leaf traits, such as photosynthetic rate, in maize. In a field experiment, maize was planted as a sole crop, in three different intercrop configurations with wheat (a replacement intercrop and two add-row intercrops), and as a skip-row system with one out of each three maize rows omitted. Nitrogen and phosphorus uptake were determined at flowering and maturity. Specific leaf area, leaf nitrogen concentration, chlorophyll content and photosynthetic rate of the ear leaf were determined at flowering. Nitrogen and phosphorus concentrations were significantly lower in intercropped maize than in sole maize and skip-row maize at flowering, but these differences were smaller at maturity. At flowering, specific leaf area was significantly greater in intercrops than in skip-row maize. Leaf nitrogen concentration was significantly lower in add-row intercrops than in sole maize, skip-row maize or maize in the replacement intercrop. Leaf chlorophyll content was highest in sole and skip-row maize, intermediate in maize in the replacement intercrop and lowest in maize grown in add-row intercrops. On the contrary, photosynthetic rate was significantly higher in the replacement intercrop than in sole maize, skip-row maize and the intercrop with an additional maize row. The findings indicate that competition with intercropped wheat severely constrained nutrient uptake in maize, while photosynthetic rate of the ear leaf was not negatively affected. Possible mechanisms for higher photosynthesis rate at lower leaf nitrogen content in intercropped maize are discussed.