Maize is one of the major agricultural commodities in the world, and a source of food in Africa, representing more than 40 million ha currently harvested on the continent. Despite sub-Saharan Africa's dependence on grain, the maize actual yield (Ya) of the crop is low when compared to its potential yield. In Brazil, the yield-gap between Ya and water-limited yield (Yw) is approximately 50% of Yw. The objective of this study was to carry out a case study, using upland maize as a reference to identify a set of agricultural areas with similar soil and climate in Brazil and sub-Saharan Africa (SSA). The climatic similarity between Brazil and SSA countries was verified, seeking homogeneous climatic zones that occur in both regions. The Ya was determined including the data of at least the last three years of cultivation and were taken from the database of the national institutes of agricultural statistics. The climatic data showed that the SSA had well-distributed rainfall throughout the crop season, being higher than in Brazil, as well as the average air temperature. The average Yw was 11.3 and 7.4 Mg/ha for Brazil and SSA, respectively. Maize Ya in SSA was 1.4 Mg/ha, while in Brazil Ya was 5.2 Mg/ha. Ya represented approximately 9% of Yw in the SSA. The low Ya shows the large yield-gap found in SSA. With this, it is evident that the technologies used and the crop management are largely responsible for the yield differences between Brazil and SSA.

Yield decline has been the hallmark of Ethiopian sugarcane plantations. However, the extent and causes of the decline have not yet been empirically studied, making it difficult to manage the problem. This study aimed at analyzing the long-term yield data (1954–2022) with respect to variety and soil type. Thus, 8,923 records of yield data were summarized and sorted into decades, varieties, and soil types and then analyzed by applying Mann-Kendall and Tukey’s tests. The fields were classified and mapped using ArcGIS 10.3. The results revealed that 69% of the plantation fields were classified as “yield declining,” and the overall rate of decline has been 8.4 quintals ha−1 year−1 (R2 = 0.76). The rate of decline was higher for older than newer varieties and for vertisols than cambiols. Therefore, the older varieties should be micropropagated or replaced with improved ones, and the vertisols should be amended through practices such as green manuring, improved fallows, etc.

Cassava flour is the main source of carbohydrates for family farmers in the Amazon region of Brazil. Cassava is mainly grown under shifting cultivation, in recurrent cultivation periods initiated through slash-and-burn. Its sustainability is, however, questioned due to the associated deforestation and often rapidly decreasing crop productivity. There is an urgent need to make these cassava systems more sustainable and more profitable, but we currently lack a deep understanding of the key factors governing their productivity. We conducted an on-farm study on 37 cassava fields of smallholder farmers at three locations that spanned a range of crop-fallow frequencies, some of which were initiated through slash-and-burn while others through fire-free land clearance. First, we analysed how cassava plant density at harvest was related with pedoclimatic and management factors in slash-and-burn systems. Second, we assessed the relationship between plant density and cassava root yield at harvest and conducted a yield gap analysis to better understand which factors govern cassava productivity beyond plant density in slash-and-burn systems. Finally, we compared cassava productivity between slash-and-burn and the fire-free land clearing techniques that some farmers started to adopt in the study region. Cassava yields averaged 7.2 ± 5.4 Mg ha–1 (50% of the average yield of 14.2 Mg ha–1 in the Pará State), and ranged from 0 (in case of root rot diseases) to 24 Mg ha–1. Cassava yield was associated with plant density at harvest (ranging from 0 to 10 000 plants ha–1), suggesting that managing plant density is a key determinant of the attainable yield levels. In addition, differences in cassava root yields could be largely explained by differences in labour inputs for weeding and fallow clearing, the effect of the latter depending on soil texture. Therefore, our results suggest that labour is a key production factor for cassava in the shifting cultivation systems of the Eastern Amazon in which the use of external inputs, such as chemical fertilizers and herbicides, is limited. Further, root yields were influenced by the method of field preparation, whereby yields were about 50% lower (and more variable) when fields were prepared by slash-and-burn than by mechanical ploughing or herbicide application. Despite the significantly higher yields, these alternatives to burning the vegetation are, however, still hardly adopted in Paragominas. Hence, there is a need for supporting more sustainable production systems through local and national public policies. These new systems should not only focus on soil fertility management but also on weed control and, more generally, on labour productivity.

Maize is the primary staple crop cultivated during the monsoon season in eastern India. However, yield gaps are large because of multiple factors, including low adoption rates of good agronomic management practices. This study aimed to narrow the maize yield gap using diverse agronomic and varietal interventions through field experiments over 2 years (2013–2014) in the rainfed plateau region of Odisha. As a result, maize yield increased by 0.9, 0.74, and 0.17 Mg ha−1 under optimum plant population, fertilizer management, and herbicide-based weed management, respectively, over farmers’ current practices (Check). Moreover, when all three interventions were combined (‘best’ management practice), grain yields increased by 1.7 Mg ha−1 in conservation tillage and 2.2 Mg ha−1 in conventional tillage. We also observed that the combination of long-duration hybrids and best management practices (BMPs) increased grain yield by 4.0 Mg ha−1 and profitability by $888 ha−1 over farmers’ current practices. In addition, Nutrient Expert decision support tool-based fertilizer management along with BMPs increased grain yield by 1.7 Mg ha−1 and profitability by $314 ha−1 over farmers’ fertilizer practices (Check). These results suggest that the combination of maize hybrids and BMPs can improve the productivity and profitability of rainfed maize in the plateau region of Odisha. However, these entry points for intensification need to be placed in the context of varying investment requirements, input and output market conditions, and matched with farmer preferences and risk.

One of the main limiting factors for high yields of flooded rice (Oryza sativa L.) is the presence of weeds, especially herbicide-resistant weeds. The aim of this study was to evaluate the association of weed management practices adopted by flooded rice farmers in the state of Rio Grande do Sul (RS), Brazil, with grain yield. For this purpose, 324 interview surveys were administered to farmers who supplied information about the history of weed management and yields. The answers to the survey indicated that weedy rice (Oryza sativa L.) and Echinochloa spp. were the most important weeds that occurred in flooded rice areas in RS. Advanced growth stage of weeds and inadequate environmental conditions such as air temperature and relative humidity were listed as the main reasons for low weed control efficacy. Farmers achieved greater rice yields when they adopted rice–soybean [Glycine max (L.) Merr.] (9,140 kg ha−1 average yield) and herbicide site of action rotations (8,801 kg ha−1 average yield) along with tank mixes (8,580 kg ha−1 average yield) as specific management practices for resistant weed control. The use of glyphosate with residual herbicides in a tank mix in the rice spiking stage is the main factor related to greater yields. The postemergence applications and their relationship to delaying of flooding in rice is a factor that reduces rice yield when no spiking glyphosate application was made. Identification of the most important weeds in terms of occurrence and knowledge of the main agronomic practices adopted by farmers are essential so that recommendations for integrated management practices can be adopted in an increasingly accurate and sustainable manner in flooded rice areas in southern Brazil.

Yield gaps in milk production are here defined as the differentials between the actual yield obtained by the dairy farmer and the potential farm yield (production achieved by the top 10% of farmers: Gap 2) as well as the differential between this potential farm yield and the yield registered in the research stations (Gap 1). Assessment of yield gaps provides valuable information on potential production enhancement and drivers behind yield gaps. Milk production can be increased by narrowing the predominant large yield gaps in resource-poor smallholder farming system. Hence, this study assessed the milk yield gap and factors affecting the yield gap in Ri-Bhoi district of Meghalaya, a state located in the north-eastern Himalayan region of India. This research paper provides a scope for exploring the possibilities for improving dairy production in the state as well as contributing to literature through incorporating crucial determinants responsible for milk yield gap. A sample of 81 respondents was drawn purposely from two blocks of the district. The results indicated that the average number of cattle per household was 9.38 in standard animal units. The total yield gap was estimated at 6.20 l (91.06%) per day, composed of 0.80 l (11.76%) per day of yield gap I and 5.40 l (79.30%) per day of yield gap II. This demonstrates that the top performing farms were achieving a production level not dissimilar to that obtained on the research stations, but many were doing far less well. The size of cattle shed, dairy farming experience, concentrate price and human labour were the important determinants of the yield gap. Hence, encouraging the right stocking density of cattle, training on the preparations of home-made concentrates, access to cheap and quality concentrates, incorporating training and experience sharing on proper dairy management practices and use of technology could benefit the dairy farmers of the region.

Quantifying reasonable crop yield gaps and determining potential regions for yield improvement can facilitate regional plant structure adjustment and promote crop production. The current study attempted to evaluate the yield gap in a region at multi-scales through model simulation and farmer investigation. Taking the winter wheat yield gap in the Huang-Huai-Hai farming region (HFR) for the case study, 241 farmers’ fields in four typical high-yield demonstration areas were surveyed to determine the yield limitation index and attainable yield. In addition, the theoretical and realizable yield gap of winter wheat in 386 counties of the HFR was assessed. Results showed that the average field yield of the demonstration plots was 8282 kg/ha, accounting for 0.72 of the potential yield, which represented the highest production in the region. The HFR consists of seven sub-regions designated 2.1–2.7: the largest attainable yield gap existed in the 2.6 sub-region, in the southwest of the HFR, while the smallest was in the 2.2 sub-region, in the northwest of the HFR. With a high irrigated area rate, the yield gap in the 2.2 sub-region could hardly be reduced by increasing irrigation, while a lack of irrigation remained an important limiting factor for narrowing the yield gap in 2.3 sub-region, in the middle of the HFR. Therefore, a multi-scale yield gap evaluation framework integrated with typical field survey and crop model analysis could provide valuable information for narrowing the yield gap.

Analysis of the potato yield gap and the corresponding nutrient gap can help in devising strategies and measurements to increase productivity for closing the gaps through improved practices. On-farm experiments conducted in the main potato production areas of northwest China were used to determine attainable yield. Official statistical data were used to determine the actual on-farm yield. Yield gap was the difference between attainable yield and actual on-farm yield. Nutrient gap was calculated by dividing the size of yield gap by partial factor productivity. Results indicated that nitrogen (N), phosphorus (P) and potassium (K) fertilization increased potato yield by an average of 1169–7625, 2937–5336 and 2331–7338 kg/ha, respectively. The maximum attainable yields (the 90th percentile yields) were 50 145, 37 855, 30 261 and 56 616 kg/ha and the average actual on-farm yield were 14 179, 16 732, 10 271 and 19 990 kg/ha in the Inner Mongolia Autonomous Region (IMAR), Gansu, Ningxia and Qinghai provinces, respectively. In the above four regions, yield would need to increase by 165, 70, 112 and 121% from actual yield to reach 75% of attainable yield. Compared with recent 3-year average NPK rates by farmers, the total NPK rates need to increase by 90.1–134.3% for IMAR, 42.9–69.2% for Gansu, 68.1–111.2% for Ningxia and 48.1–83.8% for Qinghai to improve productivity to near the 75% attainable yield. In conclusion, the high yield responses to fertilizer application provide opportunities to close the large yield gaps through balanced nutrition.

The expected increase in the global demand for livestock products calls for insight in the scope to increase actual production levels across the world. This insight can be obtained by using theoretical concepts of production ecology. These concepts distinguish three production levels for livestock: potential (i.e. theoretical maximum) production, which is defined by genotype and climate only; feed-limited production, which is limited by feed quantity and quality; and actual production. The difference between the potential or limited production and the actual production is the yield gap. The objective of this paper, the first in a series of three, is to present a mechanistic, dynamic model simulating potential and feed-limited production for beef cattle, which can be used to assess yield gaps. A novelty of this model, named LiGAPS-Beef (Livestock simulator for Generic analysis of Animal Production Systems – Beef cattle), is the identification of the defining factors (genotype and climate) and limiting factors (feed quality and available feed quantity) for cattle growth by integrating sub-models on thermoregulation, feed intake and digestion, and energy and protein utilisation. Growth of beef cattle is simulated at the animal and herd level. The model is designed to be applicable to different beef production systems across the world. Main model inputs are breed-specific parameters, daily weather data, information about housing, and data on feed quality and quantity. Main model outputs are live weight gain, feed intake and feed efficiency (FE) at the animal and herd level. Here, the model is presented, and its use is illustrated for Charolais and Brahman × Shorthorn cattle in France and Australia. Potential and feed-limited production were assessed successfully, and we show that FE of herds is highest for breeds most adapted to the local climate conditions. LiGAPS-Beef also identified the factors that define and limit growth and production of cattle. Hence, we argue the model has scope to be used as a tool for the assessment and analysis of yield gaps in beef production systems.

The model LiGAPS-Beef (Livestock simulator for Generic analysis of Animal Production Systems – Beef cattle) has been developed to assess potential and feed-limited growth and production of beef cattle in different areas of the world and to identify the processes responsible for the yield gap. Sensitivity analysis and evaluation of model results with experimental data are important steps after model development. The first aim of this paper, therefore, is to identify which parameters affect the output of LiGAPS-Beef most by conducting sensitivity analyses. The second aim is to evaluate the accuracy of the thermoregulation sub-model and the feed intake and digestion sub-model with experimental data. Sensitivity analysis was conducted using a one-at-a-time approach. The upper critical temperature (UCT) simulated with the thermoregulation sub-model was most affected by the body core temperature and parameters affecting latent heat release from the skin. The lower critical temperature (LCT) and UCT were considerably affected by weather variables, especially ambient temperature and wind speed. Sensitivity analysis for the feed intake and digestion sub-model showed that the digested protein per kg feed intake was affected to a larger extent than the metabolisable energy (ME) content. Sensitivity analysis for LiGAPS-Beef was conducted for ¾ Brahman×¼ Shorthorn cattle in Australia and Hereford cattle in Uruguay. Body core temperature, conversion of digestible energy to ME, net energy requirements for maintenance, and several parameters associated with heat release affected feed efficiency at the herd level most. Sensitivity analyses have contributed, therefore, to insight which parameters are to be investigated in more detail when applying LiGAPS-Beef. Model evaluation was conducted by comparing model simulations with independent data from experiments. Measured heat production in experiments corresponded fairly well to the heat production simulated with the thermoregulation sub-model. Measured ME contents from two data sets corresponded well to the ME contents simulated with the feed intake and digestion sub-model. The relative mean absolute errors were 9.3% and 6.4% of the measured ME contents for the two data sets. In conclusion, model evaluation indicates the thermoregulation sub-model can deal with a wide range of weather conditions, and the feed intake and digestion sub-model with a variety of feeds, which corresponds to the aim of LiGAPS-Beef to simulate cattle in different beef production systems across the world.